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2023 | Buch

European Workshop on Structural Health Monitoring

EWSHM 2022 - Volume 1

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This volume gathers the latest advances, innovations, and applications in the field of structural health monitoring (SHM) and more broadly in the fields of smart materials and intelligent systems, as presented by leading international researchers and engineers at the 10th European Workshop on Structural Health Monitoring (EWSHM), held in Palermo, Italy on July 4-7, 2022. The volume covers highly diverse topics, including signal processing, smart sensors, autonomous systems, remote sensing and support, UAV platforms for SHM, Internet of Things, Industry 4.0, and SHM for civil structures and infrastructures. The contributions, which are published after a rigorous international peer-review process, highlight numerous exciting ideas that will spur novel research directions and foster multidisciplinary collaboration among different specialists.

Inhaltsverzeichnis

Frontmatter

Seismic Structural Health Monitoring for Civil Structures

Frontmatter
Structural Health Monitoring for Architectural Heritage: Case Studies in Central Italy

Structural health monitoring (SHM) has been recognized as a useful tool for experimentally assessing the structural behavior of historical buildings over time. If monitoring is performed continuously and for a long time, it allows to evaluate variations in the building’s dynamic response to external factors. The main goal is to estimate the dynamic response of the monitored buildings to daily stresses produced by environmental and anthropogenic factors (variations in ambient temperature and humidity, wind velocity, vibrations produced by vehicular traffic or other anthropogenic noise sources including visitors, service staff, etc.) to distinguish ordinary fluctuations in the buildings’ response from other anomalous behavior. Continuous monitoring also makes it possible to assess the impact of extraordinary events such as extreme weather events, earthquakes, excavations, cultural events involving many people nearby the monitored buildings. Some examples from the authors’ many monitoring campaigns on monuments located in different urban environments are presented. In particular, the effect on one of the monitored buildings of the drastic reduction of seismic noise during the SarsCov2 pandemic lockdown is investigated.

R. M. Azzara, M. Girardi, M. Occhipinti, C. Padovani, D. Pellegrini, M. Tanganelli
Structural Monitoring of a Tall Building Under Ambient Conditions and Earthquake

In regions with high seismicity such as Istanbul, it is necessary to determine the condition of tall buildings after an earthquake. Today, damage assessment after an earthquake is generally conducted by visual inspections. However, condition assessment of the buildings in a fast, remote and reliable way is important for both economy and public safety. Current condition of buildings can be assessed by controlling changes in dynamic properties and inter-story drift ratios quickly, remotely and objectively with Structural Health Monitoring (SHM) systems. With the motivations and reasons mentioned above, a tall building in Istanbul has been monitored continuously. Thanks to SHM system regularly recording vibration data of the structure, the earthquake of 5.7 magnitude occurred on September 26, 2019 in Marmara Sea was recorded. In this study, firstly, dynamic properties of the building before, during and after the earthquake were identified by using different identification techniques. After that, identified damping ratios of tall building were compared with several empirical equations in codes, guidelines and standards. Finally, Transfer Matrix Method (TMM) for Timoshenko beam model was utilized to estimate the accelerations of non-instrumented floors from instrumented floors and predict inter-story drift ratios.

Emre Aytulun, Serdar Soyöz
Impact of Decision Scenarios on the Value of Seismic Structural Health Monitoring

The limited knowledge that decision-makers have on the actual condition of civil structures and infrastructures complicates the management of seismic emergencies in urbanized areas. In this respect, Seismic Structural Health Monitoring (S2HM) can support decision-makers by providing real-time information on the structural condition. Nevertheless, S2HM information comes with a cost, and decision-makers have to decide if installing this type of system is worthy before the information is collected. In this paper, the benefit of S2HM in post-earthquake emergency management is assessed through the Value of Information (VoI) from Bayesian decision analysis. The VoI can be intended as the expected reduction in management costs resulting from monitoring information. If the VoI is higher than the cost of the monitoring system, the manager should install it. The methodology is applied to an exemplary building in a seismic area. It is demonstrated and discussed in the paper that the value of S2HM is strongly influenced by the decision scenario considered by the decision-maker. Specifically, it is shown that the VoI is particularly high when the S2HM information prevents unnecessary building evacuation and related losses of functionality.

Pier Francesco Giordano, Said Quqa, Maria Pina Limongelli
Dynamic Identification of the Sant’Andrea Pulpit in Pistoia (Italy): Some Preliminary Notes

Cultural heritage structures, that can be considered as the assemblage of rigid bodies (statues, pulpits, etc.), are susceptible to overturning in case of seismic events, with the potential loss of invaluable masterpieces. Therefore, the response under ground motion and their intrinsic vulnerability opened a debate among the scientific community about the possibility of a base isolation of such structures. The answer to this question is not trivial and the assessment of their structural dynamic (under ambient vibration) is required as a first step in a structural health monitoring perspective. In this paper the case of the Sant’Andrea Pulpit in Pistoia (Italy), a medieval masterwork by the Italian sculpture Giovanni Pisano, is presented, and the massive dynamic testing campaign performed is described.

Giacomo Zini, Michele Betti, Gianni Bartoli
Piezo Sensors Based Operational Strain Modal Analysis for SHM

Piezo sensors are a class of smart material based dynamic stain measuring sensors which are yet to be thoroughly investigated for modal analysis based global level monitoring. Operational modal analysis is a vibration testing technique to obtain the modal parameters under low amplitude operational excitation forces. This study investigates and evaluates the efficacy of piezo sensor for structural health monitoring with operational strain modal analysis. A scaled-down bridge prototype was tested under pedestrian motions to capture and locate the artificial damage at different locations. Modal frequencies, damping ratios and modes shapes are identified in healthy and damaged states and compared to localize the damage. Piezo sensors are able to detect and localize the damage effectively and are found to be superior to the accelerometers for sensitivity towards the damage. Modal assurance criteria (MAC) is estimated to compare the mode shapes and localize the damage. MAC successfully captures and localize the damage due to change in the mode shapes. The strain modes from piezo sensors are more sensitive towards the damage, and hence, they are effectively able to capture and identify the damage under low amplitude operational force based ambient excitations.

Dattar Singh Aulakh, Suresh Bhalla
Seismic Assessment of the Carillon Tower in the Philippines Using a Finite Element Model Updated with Operational Modal Analysis

This research is on the seismic assessment of the Carillon Tower in the University of the Philippines in Quezon City, Philippines. Despite being vulnerable and having been exposed to earthquakes in the past, assessment efforts have been limited mainly due to the lack of as-built plans. Consequently, it was aimed to create an FE model updated with Operational Modal Analysis (OMA). First, the Finite Element (FE) model was created using the available limited pre-build plans utilizing ETABS, considering the inclination of practicing engineers and maintenance staff. In-situ measurements revealed that there had been significant modifications, making the available drawings inaccurate. Next, with OMA, stable modes with high modal amplitude coherence values were identified using Natural Excitation Technique and Eigensystem Realization Algorithm (NExT/ERA). Comparison of the OMA results and FE modal analysis revealed a close agreement between the mode shapes but a considerable difference between the modal frequencies. The model was then updated manually to reduce the difference in the natural frequencies. Finally, to obtain the tower’s general dynamic response, Nonlinear Time History analysis was used with a simulated site-calibrated accelerogram of the predicted M7.2 EQ in the Philippine West Valley Fault System.

Ammiel Mac A. Barros, Helli-mar T. Trilles, Jaime Y. Hernandez
Application of Innovative High Accuracy GNSS Based System to the Monitoring of Civil Structures

This paper presents a study on a GNSS based Structural Health Monitoring (SHM) system, exploiting advanced PPP technologies, with sampling frequency up to 50 Hz. The work is framed in the activities of the EU H2020 project GISCAD-OV and it aims to develop an RTK GPS and Galileo High Accuracy positioning System (G-HAS) that can be used as an SHM system for civil structures, in comparison with more traditional accelerometric systems. The methodology consisted of the monitoring of a benchmark structure by means of both the G-HAS and an accelerometric system installed by the Italian Department of Civil Protection (DPC). The comparison between the results was carried out in the time and frequency domain. The time domain comparison was conducted by means of the acquired real-time displacements, while the frequency domain analysis was developed by means of the principal vibration frequency. The main goal of the paper is to demonstrate the accuracy of the G-HAS as an SHM system in both the time and frequency domain, paving the way for the development of an affordable and efficient large-scale SHM tool.

D. Cinque, D. Spina, R. Capua, D. Antonetti, S. Gabriele
Seismic Monitoring of Masonry Structures Using Smart Bricks: Experimental Application to Masonry Walls Subjected to In-Plane Shear Loading

Masonry is a construction material consisting of bricks and mortar layers, the compressive strength of which is significantly higher than its shear and tensile counterparts. Such a mechanical behavior makes masonry constructions particularly prone to brittle collapses when subjected to seismic loading. In light of this, structural health monitoring of masonry constructions, particularly for the existing ones, is of pivotal importance to evaluate the structural integrity of residential and cultural heritage buildings. In this regard, piezoresistive strain-sensing brick-like sensors, termed “smart bricks”, have been recently developed for structure-scale monitoring of masonry constructions. Smart bricks, indeed, can be easily integrated within masonry load-bearing structures made of conventional clay bricks, providing measurable variations in their electrical outputs correlated to changes in their strain states. This paper focuses on the use of smart bricks for seismic monitoring of masonry constructions by presenting an experimental program carried out to investigate their effectiveness in measuring strains within masonry walls. To this aim, smart bricks with standard dimensions were produced and installed in full-scale masonry walls with different mechanical characteristics, which were tested under diagonal compression. The results demonstrate that smart bricks can be effective to early detect shear-induced damages when employed in such structural settings.

Andrea Meoni, Antonella D’Alessandro, Felice Saviano, Gian Piero Lignola, Fulvio Parisi, Filippo Ubertini
Sensitivity Analysis of the Environmental Effect on the Dynamics of Concrete Historical Architectures with Structural Joints

A large part of the 20th century architectural heritage is approaching the end of its useful service life and issues related to its conservation should be therefore addressed. In that period, the use of concrete as a building material spread considerably, favoring experimentation in geometries and structural schemes. If on one hand the innovativeness and uniqueness of these buildings have made them an essential part of our architectural heritage, on the other hand they make their study challenging. The complexity of these structures’ dynamic behavior results in the uncertainty on the sensitivity to possible damage scenarios and environmental factors, which represent a relevant aspect in Structural Health Monitoring (SHM). This paper reports the results of a sensitivity analysis of changing environmental conditions on a concrete historical building, composed by blocks divided by structural joints. The case study is the Pavilion V of Turin Exhibition Center, built by Riccardo Morandi in the late 50s. The research conducted on the pavilion’s Finite Element Model (FEM) allowed to show the effects of the variation of the elastic modulus of the structure’s components on the modal parameters for different temperature sensitivity scenarios and, consequently, to extract useful information for an upcoming permanent monitoring.

Linda Scussolini, Giorgia Coletta, Valerio Oliva, Gaetano Miraglia, Erica Lenticchia, Rosario Ceravolo
Structural Health Monitoring Systems Operating in a 5G-Based Network

The work aims to show some preliminary results coming from a Structural Health Monitoring (SHM) systems network. This latter is composed by five building located in the city of L’Aquila that is hosting SICURA - “caSa Intelligente delle teCnologie per la sicURezza - L’Aquila” one of the 5 “houses of emerging technologies” (Case delle Tecnologie Emergenti) promoted by the Italian Ministry of Economic Development (MiSE) to promote the exploitation of 5G, IoT and blockchain technologies to improve the public safety. Advantages coming from such new technologies will have beneficial effects in the management of the monitored structural data and information. One of the most relevant consists in the designing and implementation of possible strategy of early warning solutions in case of seismic events. Indeed, the 5G-based architecture can reduce the latency and increase the reliability of the measured and transmitted data. Another aspect regards the improvements connected to traditional monitoring and control procedures of the buildings. The continuous and synchronized knowledge of the whole structural health network allows to quickly identify the areas most affected during and after a critical event. A single building constitutes a sort of sentinel addressing in opportune and adequate way the emergency interventions. Such scenario poses the bases for the developing of Digital Twin models at single building and city level.

Fabio Franchi, Vincenzo Gattulli, Fabio Graziosi, Francesco Potenza

SHM in Wind Turbine Technology

Frontmatter
Rapid Assessment of Offshore Monopile Fatigue Using Machine Learning

Offshore wind turbine monopiles require structural health monitoring throughout their lifespan, yet direct structural measurements are limited. This paper combines numerical modeling and machine learning to present an approach to obtain rapid estimations of monopile fatigue using hourly metocean conditions. Aero-hydro-servo-elastic numerical simulations for a reference turbine provide the meta-model training dataset that encompasses wind-wave conditions applicable to the North Sea. Analysis reveals conditions whereby higher-order fully non-linear wave kinematics produce larger damage values compared to linear waves. This increase in damage is absent when implementing a simple probabilistic data lumping method. The prototype meta-model is developed based on convolutional neural networks to determine the monopile damage from measured wind-wave conditions at high temporal frequency. The proof-of-concept meta-model provides a step-change that demonstrates a promising approach to estimate monopile fatigue accumulation at high temporal resolution with scope for development to specific real-world offshore wind farms where validation data is available.

Robert C. Houseago, Agota Mockute, Elizabeth J. Cross, Nina Dethlefs
Offshore Wind Turbine Jacket Damage Detection via a Siamese Neural Network

This paper states a methodology to detect damage in the support structure of offshore wind turbines. Using vibration-response-only accelerometer measurement, a methodology based on a Siamese convolutional neural network is proposed. In contrast to standard Siamese Neural Networks (SNNs), which are feedforward neural networks, it is proposed to introduce convolutional layers to discerns between healthy and damage structural states. The strategy is validated in an experimental laboratory down-scaled structure. The results demonstrate the feasibility of the proposed methodology.

Christian Tutivén, Joseph Baquerizo, Yolanda Vidal, Bryan Puruncajas, José Sampietro
Towards a Fleetwide Data-Driven Lifetime Assessment Methodology of Offshore Wind Support Structures Based on SCADA and SHM Data

In recent years there has been an increased interest of the offshore wind industry to use structural health monitoring (SHM) data in the assessment of consumed lifetime and lifetime extension for an entire wind farm. In order for operators, certifying bodies, insurance entities and government agencies to agree on a lifetime extension, a commonly accepted lifetime assessment strategy with proven results is required. This paper aims to provide such an answer through a data-driven lifetime assessment approach using SHM and SCADA data. The research involves training neural network (NN) models using SCADA and SHM data to estimate the fore-aft damage equivalent moment (DEM) at the tower interface level on a 10-min basis for implementation in a data-driven lifetime assessment. The NN are trained and validated based on one instrumented turbine (the fleetleader) and cross-validated based on another instrumented turbine. A DEM representative for the lifetime of the asset is calculated based on the 10-min DEM’s. An analysis of the NN models’ performance (error of 10-min DEM estimation in relation to DEM derived from SHM data) and accuracy (lifetime DEM error) is undertaken. The DEM representative for the lifetime of the assets is benchmarked with the as-designed DEM to assess the lifetime.

Francisco de Nolasco Santos, Koen Robbelein, Pietro D’Antuono, Nymfa Noppe, Wout Weijtjens, Christof Devriendt
On the Minimum Required Sampling Frequency for Reliable Fatigue Lifetime Estimation in Structural Health Monitoring. How Much is Enough?

Structural Health Monitoring (SHM) represents the course of action of implementing a damage assessment strategy for engineering infrastructures. SHM systems can provide substantial aid towards the improvement of Offshore Wind turbines (OWTs) reliability, sustainability, and profitability. Usually, SHM system development is affected by three major concerns: the sensing technology, the associated signal analysis, and the interpretation algorithm. In this work, we focus on the relevance of the signal analysis on fatigue, being one of the most relevant damage sources. At some stage of the signal analysis process, analogue signals from strain transducers shall go digitized for computer analysis. In this phase, the engineer pursues the trade-off between gathering all the necessary information and storing the minimum data quantity. The sampling frequency adopted is paramount to this aim and can have substantial effects on the final lifetime estimated by damage accumulation rules. The Nyquist-Shannon sampling theorem is not well suited for minimum sampling frequency estimation in OWTs SHM. It allows recovering the frequency content of the original signal (bandwidth limited) yet does not guarantee correct reconstruction of its amplitudes (quantisation error). On top of that, the quantisation error is always on the non-conservative side of the lifetime estimation. We, therefore, provide examples showing that a ratio of the signal maximum “significant frequency” to the sampling frequency greater than or equal to ten (as opposed to two in the Nyquist-Shannon sampling theorem) is the rule of thumb to follow to avoid lifetime underestimation.

Pietro D’Antuono, Wout Weijtjens, Christof Devriendt
Analysis of Icing on Wind Turbines by Combined Wireless and Wired Acceleration Sensor Monitoring

Rotor blade icing negatively affects the operation of wind turbines and its electricity generation. The ice layer increases the blade masses, influences aerodynamics, and can fall off anytime during rotation with unpredictable trajectory and impact on ground. Due to this potential risk to human life, operation needs to be interrupted. Turbines equipped with blade heating face a downtime of some minutes to hours until removal of ice and restart of operation. Older turbines without heating system might stand still for a longer duration, resulting in significant loss of generated electricity. Modern Supervisory Control And Data Acquisition (SCADA) systems either detect icing by sophisticated measurement systems in the rotor blades, for example fiber Bragg grating devices, or predict the presence of ice by a combination of measured moisture, temperature and wind speed. Wireless acceleration sensors on the turbine tower may detect icing, monitor ice growth and even predict time of alert. The present work discusses the surveillance of an existing power plant by both wireless and wired acceleration sensors in parallel to the ice alerts of the turbine’s SCADA system and change of key parameters for indication of icing status.

B. Wondra, J. Rupfle, A. Emiroglu, C. U. Grosse
Periodic System Approximation for Operational Modal Analysis of Operating Wind Turbine

The inherent modelling of the operational wind turbines and rotating machines do not agree in general with the assumptions of the operational modal analysis (OMA) methods developed for civil engineering, where time invariant systems are considered. Current OMA methods for rotating machines introduce data-pre-processing to adapt classical identification methods. However, they show strong limitations and rely on strong assumptions, such as the isotropy of the rotor, making them hardly applicable in practice. To overcome these limitations, this paper proposes to employ the Floquet theory of periodic system to approximate rotating systems as time invariant systems. Thus, classical identification methods can be used to retrieve the parametric signature of the periodic systems. This Floquet-based approximation gives a physical meaning to the identified eigenmodes. The proposed approach is validated on both a small numerical model and an aero-servo-elastic numerical model of a rotating 10 MW wind turbine, with isotropic and anisotropic rotors, using the stochastic subspace identification to retrieve the modes and their uncertainty.

Ambroise Cadoret, Enora Denimal, Jean-Marc Leroy, Jean-Lou Pfister, Laurent Mevel
Structural Health Monitoring of Offshore Jacket Platforms via Transformers

The goal of this project is to monitor the structural health of jacket-type platforms for offshore wind turbines. The methodology is based on vibration-response-only accelerometer measurement and a transformer-based framework for multivariate time series. The original transformers paper proposed an architecture applied to a natural language processing task, meanwhile later works approached the use of transformers for forecasting, missing value imputation, and classification of time series. In general, the transformers based on attention mechanisms demonstrate being superior in terms of quality and performance on many sequential tasks in comparison to other architectures. Similar results are expected with time series data. Thus, this work proposes to use transformers for the classification of different structural types of damage in jacket-type wind turbines. The methodology follows the next steps: (i) accelerometer data is acquired, (ii) data is cleaned and wrangled into time series, (iii) a transformer-based framework classifies different damage scenarios. In a down-scaled experimental laboratory structure, the method is validated. The results demonstrate the feasibility of the proposed methodology.

Christian Tutivén, Héctor Triviño, Yolanda Vidal, José Sampietro
Machine Learning Techniques for Damage Detection in Wind Turbine Blades

In the wind energy industry, wind turbines are subject to enormous mechanical loads and extreme environmental conditions during operation, which is why damage detection methodologies play a vital role in their operation. In order to reduce costs and guarantee the integrity and longevity of such structures, the use of a reliable Structural Health Monitoring (SHM) methodology, capable of detecting structural defects, is crucial to plan and perform maintenance operations in a way to extend the lifetime of these structures. In this context, Machine Learning (ML) techniques have succeeded in a broad range of applications and can be leveraged to develop accurate and automated SHM procedures.In this work, two different methodologies were successfully implemented to recognize deviating patterns from the healthy state, to detect anomalies in the dynamic response of wind turbine blades. One methodology combines automated modal analysis and the classification of modal parameters with a Multivariate Gaussian anomaly detection technique. The other uses Autoencoders (AEs) to classify frequency-domain data of the structure, i.e., frequency response functions or cross-power spectral densities. The response of the structures was obtained through modal shaker, modal hammer, and pull-and-release testing.

André Tavares, Bernardo Lopes, Emilio Di Lorenzo, Bram Cornelis, Bart Peeters, Wim Desmet, Konstantinos Gryllias

Nonlinear Ultrasonic Guided Wave Methods for SHM

Frontmatter
Sparse Array (Nonlinear) Guided Wave Imaging for Localization of Damage in Composites

Composite materials are subjected to an increasing importance in critical components commonly used for automotive, aerospace and other modern industrial sectors. Yet, these composites are quite susceptible to damages and defects which can be introduced during the manufacturing and/or operational life. Reconstruction Algorithm for the Probabilistic Inspection of Damage (RAPID) and Delay and Sum (DAS) are well-known guided wave imaging techniques capable to reveal the presence of damage in a sparse ultrasonic sensor network.In this study, several factors in RAPID are investigated to understand their influence on the quality of the reconstructed damage map for a composite laminate with an inter-ply defect. A critical comparison between RAPID and DAS is performed in terms of their accuracy and calculation cost. Finally, results are presented on a baseline-free probabilistic imaging modality by exploiting nonlinear wave/defect interactions.

Yusheng Ma, Saeid Hedayatrasa, Koen Van Den Abeele, Mathias Kersemans
Experimental Investigation of Modulation Transfer Phenomenon Due to Shear Horizontal Ultrasonic Wave Interaction with Local Nonlinearity

The Structural Health Monitoring methods based on the nonlinear features of the propagating ultrasonic waves have received a lot of academic attention in the last decade. Numerous works confirm that local nonlinear sources such as fatigue cracks, plastic zones or delaminations, can distort the propagating ultrasonic waves. It manifests itself in a number of nonlinear phenomena e.g. generation of high-order harmonics, side-bands (cross-modulation), frequency shift or modulation transfer. In this work, the nonlinear modulation transfer for shear horizontal guided waves is scrutinized experimentally. Therefore, the amplitude-modulated pumping wave and monoharmonic probing waves are excited simultaneously in a damaged glass plate using PZT wafer transducers. First, the frequencies of generated pumping and probing waves are selected heuristically based on empirical measurements. Next, the influence of the amplitude ratio and the amplitude modulation index of the excited waves on the modulation transfer intensity is addressed. This parametric study allows to identify the excitation parameters for effective monitoring of the state of the tested specimen using methods based on the modulation transfer phenomenon for shear horizontal waves. It also lays the foundation for future research focused on the damage detection and identification. The conclusions resulting from this experimental study can be used to develop monitoring methods of widespread plate-like mechanical structures based on the discussed nonlinear effect.

Mariusz Osika, Aleksandra Ziaja-Sujdak, Rafal Radecki, Wieslaw J. Staszewski

Verification and Validation Approaches for Demonstrating the Value of SHM

Frontmatter
Damage Diagnostics on Post-buckled Stiffened Panels Utilizing the Digital-Twin Concept

A digital twin representative of a typical composite stiffened panel is utilized to monitor skin-to-stringer disbonds. A validated finite element model of the composite panel estimates the longitudinal strains of the pristine state, at the exact location where integrated fiber Bragg grating sensors are permanently installed. Experimental strains are acquired and compared to those provided by the digital twin in order to reveal the presence of disbonds. The integrated sensor grid is used in a manner that some sensors identify the load acting on the panel, leveraging on the digital twin baseline, whilst the remaining ones are dedicated for diagnostic purposes. Two damaged single-stringer panels are tested under compression-compression fatigue conditions. Static strains are received during quasi-static test intervals among the fatigue cycles. The historical strain data are analyzed in a near real-time manner to detect and localize the induced damage throughout the test span.

Dimitrios Milanoski, Georgios Galanopoulos, Dimitrios Zarouchas, Theodoros Loutas
Train-Track-Bridge Interaction Analytical Model with Non-proportional Damping: Sensitivity Analysis and Experimental Validation

Recent studies related to the dynamic response of railway bridges focused on gradually increasing the model complexity of the train-bridge interaction, however, did not always discuss any experimental validation. In the present work, the authors analyse the role of the ballast in the dynamic train-track-bridge interaction (TTBI). The analytical response of Euler-Bernoulli (EB) beams is coupled with a distributed springs layer modelling the ballast. The two equations are solved with trainloads as elementary moving load excitation, avoiding too complex models. This non-classically damped problem has been solved with a Runge-Kutta finite-difference method with temporal-spatial discretization. Furthermore, the authors experimentally validated the mathematical TTBI solution, comparing it with the displacement response of a case study. Specifically, at first, experimental modal bending stiffness parameters have been estimated to provide a representative equivalent EB beam model. Thereafter, the coupling effects of the ballast have been considered with a sensitivity analysis of the modelling parameters. Finally, the optimization to the actual experimental response of the model provided an estimate of the vertical ballast stiffness and its damping. The relevant difference in the damping of the experimental and mathematical model evidences the fundamental role of the ballast in adsorbing vibrations induced by the train passages.

Marco M. Rosso, Angelo Aloisio, Raffaele Cucuzza, Giuseppe C. Marano, Rocco Alaggio
On the Probability of Localizing Damages Based on Mode Shape Changes

Obtaining reliable diagnosis results based on modal parameter changes is a challenge due to their low sensitivity to local damages and uncertainties related to their estimation, especially under ambient excitation. In non-destructive testing, the reliability is quantified through probability of detection (POD) curves, which are often limited to damage detection and cannot be applied to structural health monitoring applications where no data from the damaged state is available. To fill this gap, a method is developed in this paper that allows one to create probability of localization curves (POL curves) based on measurements from undamaged structures. The approach is based on statistical damage localization tests and requires a finite element model. For proof of concept, the method is applied to a simple numerical structure, demonstrating that it is a powerful tool to analyze the performance of SHM systems before damage occurs. The findings demonstrate that the POL increases with an increasing number of observed modes of vibration, an increasing measurement duration, an appropriate sensor layout, and low measurement noise levels.

Alexander Mendler, Szymon Greś, Michael Döhler, Sylvia Keßler
An SHM Data-Driven Methodology for the Remaining Useful Life Prognosis of Aeronautical Subcomponents

Prognosis of the Remaining Useful Life (RUL) of a structure from Structural Health Monitoring data is the ultimate level in the SHM hierarchy. Reliable prognostics are key to a Condition Based Maintenance paradigm for aerospace systems and structures. In the present work, we propose a methodology for RUL prognosis of generic aeronautical elements i.e. single stringered composite panels subjected to compression/compression fatigue. Strain measurements are utilized in this direction via FBG sensors bonded to the stiffener feet. The strain data collected during the fatigue life are processed and used for the RUL prognosis. In order to accomplish this task, it is essential to produce Health Indicators (HIs) out of raw strain that can properly capture the degradation process. To create such HIs a new pre/post-processing technique is employed and a variety of different HIs are developed. The quality of the HIs can enhance the performance of the prognostic algorithms, hence a fusion methodology is proposed using genetic algorithms. The resulted fused HI is used for the RUL estimation of the SSCPs. Gaussian processes and Hidden Semi Markov Models are employed for RUL prognosis and their performance is compared. Despite the complexity the raw data we demonstrate the feasibility of successful RUL prognostics in a SHM-data driven approach.

Georgios Galanopoulos, Nick Eleftheroglou, Dimitrios Milanoski, Agnes Broer, Dimitrios Zarouchas, Theodoros Loutas
Cross-Correlation Based Algorithm for SHM De-bonding Analysis of Typical Aeronautical Structures via OFDR

An SHM algorithm to detect damage size and location without the need of either strain or load reference was proposed and tested. The proposed methodology is based on the physical connection between the local signal scattering and the structural discontinuity, with respect to both geometrical distribution and time evolution. In detail, first order derivative is first applied, followed by a classical cross-correlation process to estimate the degree of relationship between the analysed signals. Deviations are quantified with respect to auto-correlation values. This approach was tested on a complex beam, made of two flat unidirectional carbon-epoxy tape skins and two C-shaped profile spars bonded together by a structural paste adhesive. Experiments consisted of making that test article undergo a 3-point bending by a dedicated tool, equipped with a linear actuator. In order to detect the debonding occurrences within the bonding line of the spar cap, optical fibres were integrated in the primary structure. Optical Frequency Domain Reflectometry (OFDR) was applied, implementing a 2 m long fibre optic having a spatial resolution of 2.6 mm and interrogating at a max sampling rate of 250 Hz, to monitor the spar bonding line. The results showed that the SHM algorithm provided reliable measurements for both position and size of the disbonding, and pose the right bases for further investigations on larger aeronautic scale.

M. Ciminello, B. Galasso, A. Concilio, L. Pellone, U. Mercurio, G. Apuleo, A. Cozzolino, S. Shoham, D. Bardenstein, I. Kressel
Experimental Demonstration of Structural Health Monitoring Design Map for an Airborne Primary Structure

Aircraft structures are designed to withstand ultimate load associated with the maximum expected maneuver during the entire life of the platform. As a precaution, they are mandated to fly below a prescribed, lower load limit. In the presence of damage, the structural load carrying capabilities are reduced as the damage grows, posing a safety issue. A primary goal of a real-time Structural Health Monitoring (SHM) system is to detect a damage, under normal operating scenario, well before the damage reduces the structural integrity, in terms of hindered ultimate load carrying capabilities and, more strictly, limit load carrying capabilities. An SHM design tool is proposed for the quantitative assessment of the margin between a robust detection of a damage at a known load and the ultimate load characteristics of the structure, as a function of platform loading and damage size. When a digital twin is available and the aircraft loading spectra are known, it is possible to simulate the dependence of properly processed readings of a given SHM sensor-net as a function of aircraft loads and damage size. These data can then be arranged into an SHM design map that characterizes the SHM detection threshold in terms of aircraft loading envelope, damage size and structural load carrying capabilities. In the present work the concept of such an SHM design map is presented and applied to a digital twin, representing a damaged wing spar cap. It is also experimentally tested using a a fiber-optic-based SHM system, which monitors a spar-to-skin de-bond.

I. Kressel, Y. Ofir, U. Ben-Simon, S. Shoham, J. Bohbot, M. Tur
Adaptive Multi-category Train Scheduling Validation Based on Fatigue Reliability of a Long-Span Suspension Bridge

Fatigue is an important issue in the durability of long-span cable-supported bridges. These structures are submitted repetitively to loads such as strong winds, varying temperatures, and increasing roadway or railway traffic. The trainload accounts for a relatively significant portion of the fatigue of critical members. Usually, trains consist of different numbers of carriages, and they cause different levels of stress. Consequently, the induced fatigue damage varies accordingly. This paper proposes an adaptive multi-category train schedule validation approach based on fatigue reliability using bridge monitoring data. The method involves three steps. Firstly, the typical train categories are identified based on long-term train weigh-in-motion (WIM) data, and their percentages are obtained. Secondly, the stress responses under each type of train are collected to establish a database, and fatigue damage is calculated to generate the probability density distribution. Thirdly, the number of different types of trains can be obtained for a typical train daily schedule, and the fatigue damage is calculated and checked with the design fatigue life. The case study of a suspension bridge is used to verify the approach, and conclusions are drawn from the perspective of the bridge management.

Zhen Sun, Elsa Caetano, João Santos
Measuring Dynamic Response of the Wilford Suspension Bridge with a Vision-Based Measurement System: A Case Study

In this case study the dynamic response of the Wilford suspension bridge is measure using computer vision-based measurement system. The footbridge is subjected to forced excitations, students walking over it, jumping at certain part of it. A modified GoPro action camera with zoom lens is placed approximately 70 m away from the bridge and focused to record videos of multiple targets (hanger to beam connections). Template matching technique is used for the estimation of pixel motions of the targets. Accurate target displacements are derived. They are then used to obtain vibration frequencies and draw the mode shape of the first vertical vibration mode. It is concluded that, although vibration frequencies corresponding to higher vibration modes can be measured, finding the vibration mode shape is difficult with the measurement resolution of 1/10 pixels used in this study.

Rolands Kromanis, Said Elias
Gaussian Process Latent Force Models for Virtual Sensing in a Monopile-Based Offshore Wind Turbine

Fatigue assessment in offshore wind turbine support structures requires the monitoring of strains below the mudline, where the highest bending moments occur. However, direct measurement of these strains is generally impractical. This paper presents the validation of a virtual sensing technique based on the Gaussian process latent force model for dynamic strain monitoring. The dataset, taken from an operating near-shore turbine in the Westermeerwind Park in the Netherlands, provides a unique opportunity for validation of strain estimates at locations below the mudline using strain gauges embedded within the monopile foundation.

Joanna Zou, Alice Cicirello, Alexandros Iliopoulos, Eliz-Mari Lourens
Predictive Maintenance of Aircraft Primary Structures Based on Load Monitoring

The paper is focused to the development of a load monitoring system (LMS) capable to prevent eventual failures on primary structural elements based on the actual load history experienced by the aircraft during its life. The knowledge of the actual loads encountered by the aircraft and a numerical model able to predict failures allow to warn the aircraft operator that further inspections and eventual maintenance are required.The LMS is based on the data acquired by several sensors, some of them already present on any aircraft (to measure aircraft speed, altitude, load factor, etc.). Additional strain sensors are necessary to monitor the state of stress at certain locations that allow to estimate the actual load condition (e.g. shear, bending, torsion distribution on the aircraft wing box).The proposed LMS is tested with the ground static test of the winglet of a general aviation aircraft and will be applied in-flight to reconstruct the actual load conditions.

F. Ricci, E. Monaco, U. Mercurio, L. Pellone, I. Dimino, M. Oliva, M. Giuliani, V. Capuano

Nonlinear SHM Methods for High Sensitivity

Frontmatter
Identification of Contact Acoustic Nonlinearities of Subsurface Cracks Located at Free-Edges

Structural health monitoring (SHM) is the continuous on-board monitoring of a structure’s condition during operation by integrated systems of sensors. Standard methods of SHM are vibration-based methods like the electromechanical impedance (EMI) method. This method uses a piezoelectric transducer that is attached to a mechanical structure of interest. The transducer is supplied by a harmonic voltage that results in a typically harmonic oscillation of both transducer and structure and allows to monitor the combined dynamic response by measuring the supplied current, and thus, the impedance. Changes of the impedance reflect changes of the structure and can be used to conclude on a potential damage. Today, linear response is typically evaluated by the EMI. Critical damages like delamination in fiber reinforced polymer components could also provoke nonlinear response that might allow more conclusions on the damage. This contribution demonstrates the existence of contact acoustic nonlinearity in an aluminum beam with a free-edge-delamination-like subsurface crack at the end. The investigation is based on transfer frequency response functions between an exciting transducer and out-of-plane surface velocities of the beam measured by laser Doppler scanning vibrometry. For identification of the nonlinear response an identification method is used and verified that is readily published. Furthermore, the diffusion of the nonlinear behavior shall be investigated to conclude on its possible measurement by a transducer that is located at some distance to such a damage.

Christoph Kralovec, Martin Schagerl
Some Exceptional Features of Flexural Wave Scattering by a Cluster of Nonlinear Scatterers on a Beam

The scattering of flexural waves on a beam can be manipulated by adding scatterers to it in the form of point attachments. A number of special scattering properties have previously been presented when only the linear properties of the scatterers are considered, such as asymmetric total reflection and asymmetric total absorption. More recently, scatterer clusters which also account for nonlinear properties of the attachments have been proposed, analyzed and simulated, using an analytical scheme based on perturbation theory. These models have predicted further exceptional features of scattering response which are not possible with only linear scatterers, such as non-reciprocal transmission and energy transfer to different frequency bands. In this paper, the established perturbation methodology is used to analyze the reflection and transmission coefficients for two different special configurations of scatterer clusters. The analytical results are also validated with those from Finite Element models. The particular characteristics of the scattering behavior of each configuration are highlighted. The potential for exploiting the exceptional response characteristics of such nonlinear scatterer clusters for applications is also briefly discussed.

Angelis Karlos, Pawel Packo
Investigation of Thermoelastic Modulation Phenomenon Due to Frictional Dissipation on Crack Interfaces

Interaction of an ultrasonic wave with a structural defect causes various types of physical phenomena, which in-depth description requires a multiphysical or even multiscale approach. Heat generation in the vicinity of a crack is one of these phenomena. Different theoretical models were developed to describe its features such as the local temperature rise due to the crack’s surfaces mutual interactions, or the heat generated at the grain boundaries of polycrystalline materials. In this work, the coupled thermoelastic theory, analytically described by the heat-diffusion equation and Navier’s equation of motion, is considered to investigate the ultrasonic wave behavior in a homogenous isotropic structure with local nonlinearity. The nonlinearity is modeled as a through-thickness crack with an implemented Coulomb friction model. Numerical simulations are used to analyze the influence of the heat dissipation at the crack on the propagating ultrasonic wave. In particular, an amplitude modulated shear horizontal wave is used to cause the local heat generation at the crack. This frictional interaction becomes a source of a thermal wave that perturbs the structure near the defect. The amplitude modulation transfer from the elastic to the thermal wave, and the influence of the normal force prescribed at the crack are addressed.

Aleksandra Ziaja-Sujdak, Mariusz Osika, Rafal Radecki, Wieslaw J. Staszewski
Investigation of the Vibro-Modulation Effect in the Pressure Changing Nonlinear Surface Contact

The influence of the nonlinear effects on the propagating ultrasonic waves in solids have exhibited a growing attention in the last decade. Numerous investigators have focused on exploring various nonlinear sources, such as nonlinear material definition, different types of crack motion, de-boding, etc.In particular, the nonlinear crack motion continues to be very attractive to investigated due to lack of precise distinction about its impact on the generated higher-order harmonics and/or side harmonics. Furthermore, it was shown in the literature that this type of nonlinearity has the highest impact on the generated nonlinear wave components. Thus, in this paper, the nonlinear shear motion between the crack interfaces is taken into consideration. First, the analytical solution is prepared based on the Coulomb’s friction model, in which the low-frequency modulation of the normal force to cracks interface is assumed. Solution is obtained using the Harmonic Balance method. Next, the results are validated through the experimental investigation, confirming the choice of the model. Lastly, the analysis of the obtained side-bands amplitude components is performed to find characteristic features of the considered crack motion.

Rafal Radecki, Aleksandra Ziaja-Sujdak, Mariusz Osika, Wieslaw J. Staszewski

Towards the Next Generation of Performance Indicators Supported by SHM

Frontmatter
The Sensitivity Enhancement of Distributed Fiber Optical Sensors

Distributed sensing with fibre optic sensors (FOS) has many advantages for strain monitoring, shape sensing and damage detection. However there are various factors that affect the sensitivity of the sensors. In this work different parameters such as types of coating and twist pre-stress are investigated to enhance the measurement sensitivity of distributed FOS used in Rayleigh-Backscattering sensing (ODiSI-B manufactured by Luna Innovations), exposed to environmental and operational conditions of aircraft. To enhance the temperature measurement sensitivity, different UV curable materials including UV683, UV639 (manufactured by Permabond Engineering Adhesives Ltd) were adopted as the FOS coating. Comparing with the commonly used coatings such as acrylates and polyimide, these UV curable coatings show different temperature sensitivity, especially in low temperature range (−50 $$^{\circ }$$ ∘ C to 0 $$^{\circ }$$ ∘ C). However, this temperature sensitivity difference decreased significantly when FOS are mounted onto the surface of composite laminates due to the adhesive. To improve the strain measurement sensitivity, different coatings and the twist pre-stress are induced in FOS. Experiments confirm a small enhancement of strain measurement sensitivity with different coatings. A greater sensitivity enhancement is observed when twist pre-stress is induced before the tension experiment.

Yingwu Li, Zahra Sharif Khodaei
OFDR-Based Integral Process Monitoring and SHM System for Composites Manufactured by Resin Infusion Under Flexible Tooling

Composite materials are now widely established across many high-performance industries like aerospace. Increased use has shown they are able to outperform legacy materials when used correctly and maintained well. On the contrary, issues around susceptibility to impact damage in-service and high manufacturing costs have limited applications in other areas. Several SHM-technologies have been successfully used to address many of the in-service monitoring needs. The aim of this work is to build upon a proven SHM system based on fiber optic sensors and develop a complementing integral manufacturing process monitoring regime.Distributed fiber optic sensors based on Rayleigh Optical Backscattering Reflectometry (OBR) are utilized in this research to capture key characteristics over a large area with high spatial resolution. The OBR sensors can monitor the entirety of a product’s life and not just certain aspects of it, thus providing feedback in the manufacturing stage and enabling quality improvements.The presented work assesses OBR for its merit with regards to manufacturing monitoring. The focus lies on the most important factors in liquid resin infusion of a dry fiber bed. The OBR sensor was integrated into the woven fabric during the manufacturing process to provide vital information on the fabric compaction level and infusion parameters. Those factors have great influence on the quality of the manufactured component. In a second stage, several adverse processing conditions, typical for Resin Infusion were simulated and the system’s detection capability of such flaws is analyzed. The results obtained reliably inform the vacuum pressure level and detect manufacturing process errors with millimeter-level spatial resolution. With the added potential for real-time data streaming online parameter changes can be made based on the sensor readings. This confirms the aptitude of the chosen OFDR sensing method and provides the basis for a highly integrated Life-Cycle management system.

Valentin Buchinger, Zahra Sharif Khodaei

Wireless Sensing Systems for Structural Health Monitoring

Frontmatter
Structural Health Monitoring System for Micro-hydraulic Power Stations Through Water Level Monitoring with a Wireless Network of Optical Sensors

The current context in the energy market with a growing energy demand requires the employment of efficient renewable sources. This is the case of micro-hydraulic power stations, which provide reliable and decentralized energy since early 20th century. However, many of these infrastructures, built decades ago, are obsolete and need important updates to gain efficiency and ensure structural integrity. Furthermore, these power stations are usually placed in remote areas and subject to flooding, landslides or vegetation in different parts of the infrastructure such as the canals, penstock and forebay that endanger the integrity of the infrastructure and are only detectable by human inspection. Water level monitoring in different parts of the infrastructure plays a critical role, since allows detecting and localizing these issues by differences in water level between separate localizations on the power station. In this paper, we present a water level monitoring system based on optical time of flight sensors with 10 mm resolution. Each sensor has a 3.7-year autonomy connected to a wireless network through LoRaWAN protocol, to monitor the integrity of the micro-hydroelectric power station. Furthermore, the monitoring system is low cost, based on Arduino technology and 3D printing parts.

Danel Bargiela, Ander Zornoza, Igor Ayesta, Amaia Berganza, Gaizka Durana, Joseba Zubia
Wireless Module for SHM Applications Based on Solitary Waves

In recent years, there has been an increasing interest in the use of highly nonlinear solitary waves (HNSWs) for nondestructive evaluation and structural health monitoring applications. HNSWs are mechanical waves that can form and travel in highly nonlinear systems, such as granular particles in Hertzian contact. The easiest setup consists of a built-in transducer in drypoint contact with the structure or material to be inspected/monitored. The transducer is made of a monoperiodic array of spherical particles that enables the excitation and detection of solitary waves. The transducer is wired to a data acquisition system that controls the functionality of the transducer and stores the time series for post-processing. In this paper, the design and testing of a wireless unit that enables the remote control of a transducer without the need to connect it to sophisticated test equipment are presented. Comparative tests and analyses between the measurements obtained with the newly designed wireless unit and the conventional wired configuration are provided. The results are corroborated by an analytical model that predicts the dynamic interaction between solitary waves and materials with different modulus. The advantages and limitations of the proposed wireless platform are given along with some suggestions for future developments.

Ritesh Misra, Hoda Jalali, Samuel J. Dickerson, Piervincenzo Rizzo
A New Real-Time SHM System Embedded on Raspberry Pi

This paper outlines the development of a real-time monitoring system, which incorporates hardware, software and database, applied to structural health monitoring (SHM). The system was conceived, designed, implemented and embedded on Raspberry Pi 3 (RP). With the need for reliability and information being provided in real time (IoT), RP has tightly integrated into the SHM field. To accomplish that, we developed an acquisition system based on Pmod IA (AD5933) along with the multiplex 4066, used to switch among the piezoelectric transducers. Furthermore, a real-time web application was developed to manage the acquisition system, integrate hardware with software and store the data collected in a dedicated NoSQL database. To perform excitation and get the structural response signals, experiments were carried out based on the electromechanical impedance technique by using three PZTs glued into an aluminium structure. Sinusoidal excitation signals, ranging from 20 kHz to 30 kHz with an amplitude of 2 V, were applied to the host structure. Overall, the reference system presented higher sensitivity for the RMSD metric, whilst the proposed system showed more relevance for damage detection via CCDM. Despite being implemented in low-cost hardware, the developed system identified structural failures with good reliability, being advantageous from both financial and dimension standpoints.

Mario de Oliveira, Raul Nascimento, Douglas Brandao
Structural Health Monitoring Using Wireless Sensor Networks with Nonsimultaneous Sampling

Structural health monitoring with wireless sensor networks (WSN) is an attractive alternative to traditional wired technology. The main challenges of WSNs are time synchronization, transmission of large amounts of data, and energy consumption. In this paper, autocovariance functions (ACFs) are estimated in all sensor nodes. Strict time synchronization is not necessary, because cross-correlations are not utilized. The measurement period must be long for a sufficient accuracy, but the number of samples in the transmitted ACFs is much smaller. The ACFs from all sensor nodes are transmitted to the base station for centralized data processing. Spatiotemporal correlation can be utilized, because for a stationary random process the ACFs have the same form as the free decay of the system. The covariance matrix is estimated using the training data from the undamaged structure under different environmental conditions. An extreme value statistics control chart is designed to detect damage. A numerical experiment was performed by simulating a bridge deck under stationary random excitation and variable environmental conditions. The excitation or environmental variables were not measured. Damage was a crack in a steel girder. Nonsimultaneous sampling of the WSN was simulated by selecting the starting time of the measurement randomly in each sensor node.

Jyrki Kullaa

Integrated Approaches for SHM: Models, Data and Experiments

Frontmatter
SHM/NDE Research at the Laboratory of Active Materials and Smart Structures

This paper reviews recent structural health monitoring (SHM) and nondestructive evaluation (NDE) research at the Laboratory for Active Materials and Smart Structures (LAMSS) of the University of South Carolina, USA. A common theme throughout our research efforts is the judicious combination of theoretical exploration, numerical simulation, and experimental measurements. Both passive and active SHM methods have been pursued.The active SHM research has focused on detecting various types of composite damage using guided-wave interrogation and sensing. The composite damage considered covered both seeded delaminations and barely visible impact damage (BVID).The passive SHM research was focused on recording acoustic emission (AE) wave signals created during fatigue loading of aerospace-grade sheet-metal coupons. The AE signals were analyzed for finding specific signatures associated with fatigue crack growth. It was found that not all AE signals were associated with fatigue crack growth, some resulting from the interaction of the faying surfaces.The NDE research was focused on developing novel methods for damage detection with particular focus on composite materials. Remarkable results were obtained in using angle beam transducers (ABT) to selectively excite either quasi-S0 or quasi-SH0 guided waves in composite materials. Eddy current methods were used to detect composites flaws and damage.

Victor Giurgiutiu
Integration of Fatigue Estimation into Experimentable Digital Twins for Structural Applications

Digital Twins are becoming a trend topic, as they raise expectancies for economic benefits in wide fields of industry, e.g., through better inventory prediction, improved product capabilities, predictive maintenance, and related business objectives. One concept to push Digital Twins into practice is the so-called Experimentable Digital Twin (EDT), which allows interaction between Digital Twins in a pure virtual or in a hybrid environment.We expect EDTs to generate additional values for structures and thus, provide an incentive for a more wide-spread use of SHM as the link between digital and real twin. An application, where EDTs generate extra value, is the use for fatigue problems of structures. By predicting the life fraction costs of their physical twins, EDTs allow to compare different process options in a virtual experiment. This is particularly useful if there are multiple options for the same mission.As an example, a crane trolley on a cantilever beam is inspected. The EDT is equipped with a plugin for cyclic counting for variable amplitude loading, which illustrates that basic analytical methods enable the EDT to give a quick estimation of life fraction costs.

Sebastian Schmid, Rebecca Richstein, Kai-Uwe Schröder
Using SHM for the Representation of Structural Components over Their Service Life Within Digital Twins

Although the interest in the Digital Twin (DT) has grown across industry and academia, only a few concrete proposals for the technical implementation of such a DT have been made. To benefit from the operational assessments and improvements through the use of a DT, the integration of structural analysis should be addressed. To connect DTs to their physical counterparts during the whole service life, changes during the lifetime, e.g., due to fatigue or damage, must be detected, quantified and evaluated. The suitable tool to achieve this goal is Structural Health Monitoring (SHM). This contribution examines the state of the art for DTs in the context of structural applications. Furthermore, the role of SHM as a necessary condition for the realisation of DTs is discussed. With the help of the SHM levels according to Rytter, a method is proposed to select the required information depth for adapting the DT to a changing physical twin. Only after detecting a measurable deviation (Level 1) it is necessary to apply more detailed models to assess changes in the structural behaviour. These considerations are illustrated by the example of a cantilever beam structure.

Rebecca Richstein, Sebastian Schmid, Kai-Uwe Schröder
Crack Size Estimation with an Inverse Finite Element Model

The inverse Finite Element Method (iFEM) is a model-based technique able to compute the displacement field of a structure from strain measurements and a geometrical discretization of the same structure. In addition to shape sensing, i.e. the computation of displacements, different Structural Health Monitoring (SHM) applications based on iFEM are available in the literature. These are mainly focused on the detection and localization of the damage, without attempting damage size estimation. The latter can be performed with different approaches such as Artificial Neural Networks, however, a prior knowledge of the different damage scenarios would be required. To overcome this issue, the present research proposes a novel iFEM approach to estimate the damage size without creating any database of damage models. The damage is included in the model to decrease the discrepancies between the physical structure and its model. In particular, the damage size is systematically increased until the strain discrepancy between the experimental measurements from sensors and the numerical strain reconstruction is minimized. The technique is experimentally verified on an aluminum plate subject to fatigue crack propagation. The strain field as input to the iFEM is measured with an Optical Backscatter Reflectometry (OBR) fiber and with Digital Image Correlation (DIC).

Daniele Oboe, Dario Poloni, Claudio Sbarufatti, Marco Giglio
Shape Sensing of Stiffened Plates Using Inverse FEM Aided by Virtual Strain Measurements

The inverse problem of structural deformation reconstruction using experimentally measured strains, known as ‘shape sensing’, is a topic with numerous applications in the field of Structural Health Monitoring (SHM). Existing shape sensing methods are influenced by the number and location of in-situ strain sensors used. A dense strain-sensor array can produce accurate displacement predictions, whereas a sparse strain-sensor distribution leads to inaccurate predictions and possibly a breakdown of the method. In the latter cases, introducing virtual strain sensors can provide additional input strain data for the shape sensing method. This paper provides experimental validation of this coupled shape-sensing approach, using real and virtual strain data, for the displacement reconstruction of a stiffened aluminium plate instrumented with fibre optic sensors. The inverse Finite Element Method (iFEM) is the shape sensing technique employed, and two strategies are compared for producing virtual strain data: the Smoothing Element Analysis (SEA), and modal expansion. The experimental results presented demonstrate the effectiveness of the two strategies investigated.

Rinto Roy, Marco Esposito, Cecilia Surace, Marco Gherlone, Alexander Tessler
Concrete Bridges Continuous SHM Using MEMS Sensors: Anomaly Detection for Preventive Maintenance

Bridge infrastructures in Europe are facing ageing, progressive damaging processes, change of traffic loads as well as climate change effects; as such, a sound diagnostics process based on the analysis of accurate information acquired from monitoring systems is a key enabler to support the application of preventive maintenance plans and to guide efficient decisions on repairs or strengthening. The fast-paced development of cheaper but reliable devices has allowed to collect a huge amount of data to deepen the knowledge of the structural behavior over time of existing structures under service conditions. This paper shows the use of MEMS sensors, both clinometers and accelerometers, for continuous structural health monitoring on concrete bridges. A dense sensing monitoring approach is applied, and data are analyzed and compared in near-real time with a threshold set based on an updated reference FE model of the bridges. A case study is presented, where anomaly detection algorithms based on key performance indicator evolution in time have efficiently identified and localized damages triggering repeated proactive maintenance interventions. Attention is given to the seasonal influence on both the static and dynamic response of the bridges, and on the misleading effects on the damage detection and diagnostics processes. This approach is part of a wider framework aimed at an industrial application of SHM, in which the specific aspects covered in this paper have been identified and analyzed on multiple similar concrete bridges under continuous monitoring, of which evidence is provided.

Francesco Basone, Alfredo Cigada, Paola Darò, Giulia Lastrico, Monica Longo, Giuseppe Mancini
Mechanics Informed Approach to Online Prognosis of Composite Airframe Element: Stiffness Monitoring with SHM Data and Data-Driven RUL Prediction

During the service of composite airframes, damage initiates and accumulates due to the manufacturing imperfections, impact damage and cyclic loadings, leading to the degradation in its load-bearing capacity. The nature of the degradation process is complicated due to the multi-mode damage propagation and complexity in the structural details of airframes. In the condition-based health management of airframe structures, the degradation is expressed in the concept of remaining useful life (RUL). Online prognostic health management is an emerging field dedicated to the timely prediction of RUL using onboard sensors. This work presents a mechanics-informed approach to the prognosis of a typical airframe element, stiffened CFRP composite panel, under compression-compression fatigue. The fatigue degradation of axial stiffness is monitored by Lamb wave velocity and utilised for online RUL prediction via particle filter.

Nan Yue, Georgios Galanopoulos, Theodoros Loutas, Dimitrios Zarouchas
A Performance Metric to Evaluate Frequency-Based Damage Indicators

Frequency-based correlation methods have been established as an autonomous tool for extracting key features of structures in Structural Health Monitoring (SHM). Although the literature on the subject is extensive and includes multiple correlation strategies, validation of most of these methods has been performed on simple structures, such as beams or plates. In contrast, their validity for application in more complex structures under more realistic damage scenarios deserves further investigation.In this work, a method called Precision-Recall curve based on the confusion matrix is proposed to objectively evaluate the performance of spectral correlation methods for structural damage detection from vibration data sets. The Precision-Recall curve is then condensed into a scalar value using the Area Under the Curve (AUC) metric. The work is based on extensive experimental lab tests that use three different structures with decreasing modal information, challenging the detection of damage scenarios. In addition, the effects of noise and frequency range are studied as key factors that can reduce or improve the performance of the indicators. The work results also validate the method based on the Complex Frequency Domain Assurance Criterion (CFDAC) previously proposed by the authors in structures with scarce modal information. Finally, experimental evidence allows conclusions to be drawn on the performance of different indicators available in the literature.

Josep Font-Moré, Marco A. Pérez
An Influence of Temperature on Fiber Bragg Grating Sensor Embedded into Additive Manufactured Structure

Additive manufacturing (AM) is a name for techniques applied for constructing three-dimensional (3D) objects in a layer-by-layer process. AM methods can be applied for creating elements from polymers without and with reinforcing fibers. During the process, fiber Bragg grating (FBG) sensors can be embedded into the element structure for the purpose of structural health monitoring (SHM) system development. Such an approach combines in one, advantages of AM (limited waste, elements with complex shape) and SHM system (safety, information about real loading conditions). Such a method can be applied for the manufacturing of different elements applied in many branches of industry, e.g. marine or civil engineering.The goal of the paper is to analyze the possibility of FBG sensors embedding into an AM polymeric elements without and with carbon fiber reinforcement. The analyzes are focused on the influence of the manufacturing process on FBG sensors (both spectrum and strain). Additionally, the influence of temperature (both elevated and sub-zero) on the finished AM elements was be investigated.

Magdalena Mieloszyk, Torkan Shafighfard, Katarzyna Majewska, Artur Andrearczyk
Model-Based Remote Health Monitoring of Ballast Conditions in Railway Crossing Panels

Railway crossing panels accumulate damage due to impact loads induced in the wheel transition area. To reduce the need for labour-intensive visual inspections of crossing panels, railway administrations are evaluating solutions for remote health monitoring. One such solution is to measure the track response that follow from the wheel–crossing impact using embedded accelerometers mounted on the sleeper at the crossing transition. The challenge that remains is to determine the health of the asset from these measured signals. In this paper, a procedure is developed to identify the ballast condition under a crossing panel via the calibration of a multibody simulation model to measurement data. This model considers the complex wheel–rail interaction in the crossing transition area, while also capturing the dynamic response of the track using a Finite Element representation of the track structure. The calibration procedure has been developed using data from six in situ crossing panels where the crossing geometry and the track response are known via laser-scanned crossing geometries and measured accelerations. Parameters associated with the physical state of the ballast are identified by minimizing the least-squares discrepancy between the measured track response and the corresponding response from simulations of dynamic vehicle–track interaction. The utilised ballast parameterization has been motivated from sensitivity analysis to ensure that each parameter has a clear and observable influence on the track response. The performance of the developed calibration procedure is demonstrated and its suitability for implementation in condition monitoring solutions is discussed.

Marko Milosevic, Björn Pålsson, Arne Nissen, Håkan Johansson, Jens C. O. Nielsen
Experimental and Numerical Study of Lamb Waves Generation Efficiency by Lead Zirconate Titanate Transducers Embedded in a Composite Laminate

In the context of airplane structures monitoring, the performance of an embedded SHM system for composite material components is studied. The present article focuses on the influence of the PZT transducers integration on the Lamb waves it generates. First the article presents finite element modeling of Lamb waves generation in a carbon/epoxy plate, either with surface mounted or with embedded PZT transducer. Ultrasonic wavefield simulated in both configurations are compared. Experiments are also performed on composite laminates with embedded or surface mounted PZT. The out-of-plane displacement induced by PZT excitation on the plate surface is measured by Laser Doppler Vibrometry in both configurations. Out-of-plane displacements are compared together.

Nina Kergosien, Guillemette Ribay, Ludovic Gavérina, Florence Saffar, Pierre Beauchêne, Olivier Mesnil, Olivier Bareille
A Contactless Approach to Monitor Rail Vibrations

This article presents a numerical formulation and the experimental validation of a noninvasive method to determine the axial load in continuous welded rail. A general finite element model of unrestrained rail segments under varying boundary conditions and axial stresses was formulated to predict the natural frequencies of vibration. The model was then validated experimentally by testing a 2.4 m-long rail under compressive loading. During the experiment, the rail was subjected to the impact of an instrumented hammer and the triggered vibrations were recorded with conventional accelerometers and a high-speed camera. The videos recorded with the camera were processed using the phase-based motion magnification technique to extract the frequency as well as the mode-shapes of the test specimen. The results proved that the information extracted from the video match well with those obtained with the conventional accelerometers. The findings presented in this article demonstrate the robustness of image processing techniques to identify modal characteristics of the tested rail. This enables the determination of the stress based on the numerical simulation.

Alireza Enshaeian, Lele Luan, Matthew Belding, Hao Sun, Piervincenzo Rizzo
Hierarchical Model Verification and Validation for Structural Health Monitoring Using Dynamic Substructuring

Despite the success of data-based methods in structural health monitoring (SHM), these approaches often suffer from a lack of training data, which can be difficult to acquire for several reasons: damage-state data acquisition can be infeasible, structures may be unique and only tested in situ, sensor placement can cause issues, certain structures cannot be tested in controllable laboratory conditions and representative environmental conditions can be difficult to simulate. Training data can be simulated using physics-based models. However, this is dependent on model verification and validation (V&V), meaning assembly-level data is still required.Hierarchical V&V is a novel technique in the field of SHM. The aim of hierarchical V&V is to remove the necessity for assembly-level validation data. Instead, the process entails the V&V of subassembly-level models, which are then combined to produce an assembly-level model using dynamic substructuring (DS). This simplifies the data acquisition process in order to reduce the associated difficulties and costs.This paper focuses on the role of DS in the hierarchical V&V process for SHM. DS allows substructures to be used to create an assembly model, and for simultaneous uncertainty propagation. This allows confidence to be established in the assembled models without requiring assembly-level data.

James Wilson, Paul Gardner, Graeme Manson, Robert J. Barthorpe
Comparison Between Model Prediction and Measured Response of a Prestressed Concrete Bridge Tested to Failure

The number of bridges that are approaching or exceeding their initial design life has increased radically. Meanwhile, an ever-increasing volume of traffic each year, both in number and weight of vehicles, is creating an additional critical situation for this kind of structure. To predict the response of bridges to traffic loads and their ultimate capacity with low uncertainties, we can use numerical structural models; however, such uncertainties increase as bridges age due to deterioration mechanisms. Non-destructive tests of material specimens and full-scale on-site load tests of the structure allow to update model parameters and have a better estimate of the bridge behaviour. However, different load tests provide different information with different impacts on the updated model accuracy. With the aid of a real-life case study, the Alveo Vecchio highway bridge, which has been tested to failure with a sequence of progressively increasing load, we aim to understand what behaviour can predict a structural model and what we can learn from a load test. This study is part of a research agreement between the Italian Ministry of Sustainable Infrastructure and Mobility, Autostrade per l’Italia SpA (the main operator of Italian highways), and the University of Trento. It concerns the management and monitoring of civil infrastructure intending to develop survey protocols and monitor systems to assess the safety and performance of existing highway bridges.

F. Rossi, F. Brighenti, A. Verzobio, D. Tonelli, D. Zonta, P. Migliorino
Design Criteria for Structural Health Monitoring Systems. Application to the Construction of Arches Using the Cantilevered Cable-Stayed Technique. Tajo Bridge Experience

The design and definition of the Structural Health Monitoring Systems (SHMS) of large structures is a process of continuous updating. Its integration with domain knowledge provided by experts allows advancing in new construction procedures. The authors develop the design criteria of these SHMS and present the experience of its application in monitoring the structural response during the construction of Tajo Bridge: an arch-type viaduct with a span of 324 m, built using the cable-stayed cantilever technique. In the structural monitoring of the Tajo Bridge, a total of 114 sensors were used, which made it possible to monitor the following phenomena: (a) stress experienced by the provisional suspension tie rods; (b) existing stress in the anchors of the temporary cable-stayed towers; (c) deformation experienced by the passive reinforcement of the semi-arches and stay piers; (d) thermal gradients experienced in different sections of the semi-arches, piers and temporary stay towers; (e) acceleration experienced in different structural sections of the semi-arches and temporary suspension tie rods; (f) the rotation experienced by different sections of the semi-arches and the heads of the piers and temporary stay towers; (g) incident wind on the semi-arches, piers and temporary cable-stayed towers. The authors raise the differences techniques to control the stress in bridge stay cables: installation of load cells in active anchorages; installation of extensometers in one of the strands that make up the tie rod; and installation of accelerometers.

Alvaro Gaute-Alonso, David Garcia-Sanchez, Felipe Collazos-Arias
A Self-supervised Classification Algorithm for Sensor Fault Identification for Robust Structural Health Monitoring

A self-supervised classification algorithm is proposed for detecting and isolating sensor faults of health monitoring devices. This is achieved by automatically extracting information from failure investigations. This approach uses (i) failure reports for extracting comprehensive failure labels; (ii) recorded data of a faulty monitoring device and the information of the failure type for selecting fault-sensitive features. The features-label pairs are then used to train a classification algorithm, so that when a new set of measurements becomes available, the algorithm is capable of identifying with a high accuracy one of the possible failure types included in the training data set. The proposed approach is successfully applied to the failure investigations conducted on a low-cost wearable device, displaying similar challenges encountered in SHM.

Andreea-Maria Oncescu, Alice Cicirello
The Application of the SVD-FDD Hybrid Method to Bridge Mode Shape Estimation

This study conducts a numerical simulation and a field experiment to measure the bridge’s traffic vibrations and to estimate the mode shapes of the bridge by the FDD (Frequency Domain Decomposition) method. But traffic vibration does not always satisfy the assumption to apply the FDD method, the results were not expected to be calculated correctly. So we use the SVD (Singular Value Decomposition) as the support to improve the applicability of the FDD method for traffic induced vibration. In the numerical simulation, the modal-parameters were estimated by acting traffic loads to a finite element plate model using shell elements. As expected, the mode shapes and natural frequencies estimated by the FDD method only were not accurate. However, we were able to improve the accuracy by comparing those results with the estimation results of the SVD method. In the field experiment, a vehicle and a bridge with known specifications were used to conduct multipoint bridge vibration measurements at 10 points using MEMS (Micro-Electro-Mechanical Systems) sensors. The results of the field experiment showed a similar trend to the numerical experiments, we could most likely estimate the natural frequencies of the bridge.

Masaki Sakai, Naoki Kaneko, Ryota Shin, Kyosuke Yamamoto
Impact Damage Identification on Composite Structures

Impacts are an important hazard for composite structures in aerospace applications. Various methods are developed for detection of impacts, which can be done in geometrically more complex structures. A number of challenges prohibits a further analysis of impacts, like the energy associated and the required action to avoid immediate or delayed catastrophic failure. The challenges include both the algorithms to reconstruct the energy, and the sensors capturing the response.In the current research, an impact identification algorithm is developed, using both Piezoelectric Wafer Active Sensors and optical sensors. Initially, drop weight impacts of various energies are applied on a homogeneous thin aluminium plate and on a quasi-isotropic thin carbon fibre reinforced plastic plate. The objective is to compare the performance of the various sensor methods and explore the abilities to reconstruct not only the location but also the impact energy. In a second stage, impacts are applied to a geometrically complex, full-scale aircraft horizontal tail plain component. Parallel to the experimental work, numerical models are developed to assist the reconstruction.The first results show that the optical fibres have potential, as the signal to noise ratio is high. However, the signals are not yet of sufficient quality to proceed to force or energy reconstruction and are below the performance known from piezoelectric sensors in a pitch catch setting.

Richard Loendersloot, Natália Ribeiro Marinho, Frank Grooteman
Composite and Monolithic DFOS Sensors for Load Tests and Long-Term Structural Monitoring of Road Infrastructure

Distributed fibre optic sensing (DFOS) is a versatile measurement technology, especially useful to monitor linear structures including road infrastructure. It allows measuring strains, displacements or temperatures continuously in a geometrical sense, so all local events (e.g. cracks or sinkholes) can be detected directly. The article summarises the newest achievements related to composite and monolithic DFOS sensors created especially for civil engineering and geotechnical applications. The design of the sensors supported by theoretical background and practical applications are discussed in relation to road embankments and asphalt layers. These successful applications of composite and monolithic DFOS sensors are the first of such types in the world.

Rafał Sieńko, Łukasz Bednarski, Tomasz Howiacki, Katarzyna Zuziak

Acoustic Emission for Structural Health Monitoring of Civil Infrastructure

Frontmatter
Investigation of Crack Formation During Long-Term Acoustic Emission Measurements on a Reinforced Concrete Railroad Switch Sleeper in the Context of Structural Health Monitoring

A major task in civil engineering is to investigate the condition of concrete structures for evaluation of the integrity. Therefore, long-term acoustic emission (AE) monitoring in the context of structural health monitoring (SHM) has been carried out during a dynamic three-point bending test to investigate the fatigue behavior of a prestressed reinforced concrete railroad switch sleeper. To detect microcracking during cyclic loading broadband AE sensors with a measurement frequency of up to 200 kHz were used which were especially developed for the detection of microcracks in concrete. For automatic three-dimensional location of the AE sources, a network with 16 of these AE sensors were attached to the surface of the specimen.In a period from Jan. 16 to Feb. 11, 2019, approximately 330,000 load cycles could be applied to a railroad switch sleeper. During the fatigue test the AE events were in real-time three-dimensionally located using longitudinal-wave (P) and transverse-wave (S) onsets. A total of 40,513 AE events were in real-time automatically located using at least 6 onsets. The results of AE monitoring show that the AE activity begins immediately after starting dynamic loading. Most of the located AE events identify macroscopic crack planes in the middle of the switch sleeper, where the load was applied. To the left- and right-hand side AE events indicate father macroscopic cracks, mainly in vertical direction. After accumulation of several microscopic cracks, the ultimate failure of the railroad switch sleeper took place.

Gerd Manthei, Marcel Walther, Jens Minnert
Acoustic Emission-Based Detection in Restricted-Access Areas Using Multiple PZT Disc Sensors

The performance of the Acoustic Emission (AE) technique is significantly dependent on the sensors attached to the structural surface. Although conventional commercially AE sensors, like R15a and WSa sensors, have been extensively employed in monitoring many different structures, they are unavailable in restricted-assess areas. In contrast, thin PZT sensors are small, inexpensive and lightweight. These thin PZT sensors have a great potential for passive sensing to detect AE signals. However, their utility in AE monitoring is limited due to their low signal-to-noise ratio and information incompleteness because of their simple construction. This work discusses the issues and possible solutions with regards to the specific selection and application of thin PZT sensors for passive sensing. The compatibility of different thin PZT sensors and conventional bulky sensors is investigated using pencil break lead (PBL) tests. The comparison between the recorded signals is carried out in both the time domain and frequency domain for these sensors. To improve the reliability and performance of the thin PZT sensors, a methodology employing multiple thin PZT sensors of different sizes is proposed based on machine learning techniques and sensor data fusion.

Lu Cheng, Ali Nokhbatolfoghahai, Roger M. Groves, Milan Veljkovic
Entropy-Based Technique for Denoising of Acoustic Emission Signals

The acoustic emission (AE) method has been successfully used in recent years to monitor the condition of industrial and civil infrastructures. In AE, time-of-arrival (ToA) estimation is considered a key parameter for the accurate localization of a growing defect. This paper describes an entropy-based filtering approach for the ToA estimation of noisy signals and compares its performance to that of the commonly adopted Akaike Information Criterion (AIC). The proposed method consists in coarsening the input data using the Crutchfield-Packard algorithm and calculating the local (instantaneous) entropy. In the present study, we demonstrate that the local entropy of the background noise component differs from the useful (informative) signal. As a result, the approach permits filtering the noise component by selecting a proper threshold value. The proposed method has been tested on experimental data aimed at localizing a source of AE in a square $$1 \times 1$$ 1 × 1 m aluminum plate. The entropy approach allows an overreaching precision in the final localization of the targets compared to the classical AIC.

Denis Bogomolov, Evgeny Burda, Nicola Testoni, Irina Kudryavtseva, Luca De Marchi, Alexandr Naumenko, Alessandro Marzani
Piezoelectric MEMS Acoustic Sensor Array for Wideband Acoustic Emission Sensing

In this study, the design of an array of piezoelectric MEMS acoustic emission sensors are introduced. A piezoelectric multi-user MEMS process (Piezo-MUMPs) is used to microfabricate a 4 × 4 array diaphragms ranging from 100 kHz to 700 kHz on a 5 mm × 5 mm chip. The numerical models of sensor array are built to understand the responses of both individual elements and array. The sensitivities of these sensors are compared to demonstrate an improvement in the sensitivity of array configuration. These sensors can operate in air-coupled, solid-coupled and fluid-coupled applications for damage detection in solids. They can be used for detecting damage modes in materials by differentiating the frequency components at the sensor level. Low cost and mass fabrication of the developed sensor enables a dense distributed sensor network for cyber physical systems.

Talha Masood Khan, Mohammad Merei, Didem Ozevin
An Improved Shear-Horizontal Wave AE Sensor and an Explanation of SH Wave Mechanics

An improved torsional SH wave sensor is introduced that provides sensitivity to shear horizontal-type guided wave acoustic emissions. This sensor provides benefits that makes them excellent supplements for, and in some cases replacements for, conventional AE sensors. The SH wave sensors provide benefits including simpler velocity settings, more consistent source localization, and reduced environmental noise from sources such as rain and wind-driven particles. These advantages arise due to the distinct differences in the wave mechanics of the SH wave modes compared to Lamb wave modes (A0 and S0, for example). Note that the SH0 mode is non-dispersive with the highest group velocity of all SH-type modes and it features pure in-plane vibration. Another benefit of the SH AE sensors is that they provide sensitivity to the SH wave modes that are produced from almost every type of AE source, but have been almost entirely neglected during AE testing. This otherwise neglected wave mode carries significant portions of energy and information contained in acoustic emissions. The inability to detect this mode creates a limitation in source detection and characterization. The SH AE sensor can be used with conventional (Lamb-type) AE sensors for source detection, localization, and characterization.

Cody Borigo, Joseph L. Rose, Ronnie K. Miller

Vehicle-Based Indirect SHM for Infrastructure

Frontmatter
Damage Detection and Localization for Indirect Bridge Monitoring Exploiting Adversarial Autoencoder and Wavelet Transform

Indirect bridge monitoring provides an economical procedure to ensure reliable operation of the bridges by utilizing the vehicle responses passing over the bridge. The present work proposes a unified framework for damage detection, assessment, and localization using adversarial autoencoder and wavelet transform in an unsupervised manner. Vehicle acceleration signals from healthy bridge state only are considered for adversarial autoencoder model training. Estimated reconstruction error is used as a damage detection index. Pre-processing techniques of spectrum filtering, and signal averaging are adopted to improve the model performance. Further, the locations of the detected damages are estimated by employing wavelet transform along with signal pre-processing techniques. The approach is tested on numerically simulated vehicle acceleration signals which are obtained by considering a half-car model on a simply supported bridge using finite element analysis. Different damage scenarios with varying severity at different bridge locations have been considered. It has been investigated that the proposed approach is successful in detecting damages with varying damage severity alongside locating their respective locations. The proposed approach also outperforms other unsupervised learning techniques by detecting and isolating different damages effectively.

Kirandeep Kaur, Mehri Makki Alamdari, Kai Chun Chang, Chul Woo Kim, Elena Atroshchenko
Structural Health Monitoring of Bridges Using Dynamic Vehicle Force

This study aims to investigate feasibility of a drive-by bridge inspection method using dynamic vehicle force. The dynamic vehicle force is identified using accelerations of a traveling vehicle. Dynamic vehicle forces when the vehicle travels on healthy and damage states of bridges are first identified. Differences of the identified dynamic vehicle forces from healthy and damage states of bridges are then considered as a damage sensitive feature for the drive-by bridge inspection. A least square minimization with Tikhonov regularization was utilized to estimate the dynamic vehicle force from acceleration data of the vehicle. A band pass filter is applied to the dynamic vehicle force before estimating the difference of the dynamic vehicle force. The range of the band pass filter was set around 1st natural frequency of the bridge. The feasibility of the proposed method was verified through simulation and a laboratory experiment. It was observed that the severer the bridge damage is, the larger the difference of dynamic vehicle force is, implying a possibility to detect bridge damages successfully by the proposed method.

Souichirou Hasegawa, Yukihiro Yano, Chul-Woo Kim, Kai-Chun Chang
Fundamental Study on Extracting Vibration of Pole Structure from Vehicle Footage

This study aims to discuss vibration extraction of a pole structure from a video processing as a preliminary investigation utilizing video footage from an inspection vehicle. The proposed method transforms the video footage into frequency domain, magnifies the featured frequency bands and then reconstructs it. The vibration patterns of the target pole is extracted from the magnified video, and vibration characteristics are estimated by a statistical approach. To investigate the feasibility of the proposed method, a full-scale laboratory experiment for a pole structure is conducted. Vibration characteristics from the video footage are compared with those identified from accelerometers to investigated identification accuracy using the video footage.

Daigo Kawabe, Chul-Woo Kim
The Behavior Analysis of Spatial Singular Mode Angle Due to Addition of Noise to the Data in an Actual Bridge Experiment

IoT progresses rapidly with the digitization of the world. In civil engineering, it is necessary to digitize the physical space by sensing. Complete IoT using a lot of sensors can realize cost-saving inspection and monitoring for important infrastructure such as bridges.In the transitional period of IoT, installation of sensors on bridges is cost-labor. On the other hand, there is “On-going Monitoring” that uses a sensor installed on a going vehicle. Spatial Singular Mode Angle (SSMA) shows the possibility of detecting bridge damage as screening index, however, the effect of noise generated in measurement on an actual bridge has not been evaluated enough in previous study. Noise for SSMA can be defined as signal noise or measurement error such as GPS error. Since SSMA can be assumed as a mechanical index based on signal analysis technology, it is an effective for evaluating the features of latent space and inference results of AI.This study carried out the experiment on four bridges (PC/RC has three and steel has one). The addition of noise is reproduced by addition of random noise or smoothing. The behavior of SSMA in data feature changes are evaluated by these comparative verifications.

Yuta Takahashi, Naoki Kaneko, Ryota Shin, Kyosuke Yamamoto
Effects of Operational Traffic Variability on iSHM

Detectability of damage-sensitive signal features in Structural Health Monitoring (SHM) of bridges is confounded by operational and environmental factors, including variation in traffic conditions. Vehicles act as moving force exciters and also represent moving additional masses. Studies of vehicle-bridge interaction suggest that complex behaviour can result from simple bridge structures interacting with small numbers of vehicles. Indirect SHM (iSHM), a paradigm in which vehicles are used as sensor carriers, introduces additional challenges regarding spatial and temporal data sparsity. However, as the bridge span and traffic volume increase, dynamic coupling between the bridge and each individual vehicle reduces, suggesting a possible optimum condition for iSHM in which available data quantity is increased and the influence of individual vehicle behaviour is reduced. This paper presents a study of bridge damage detection using data from moving vehicle responses, generated by Finite Element simulation. Varying traffic conditions are represented by statistical characterisation of force inputs and additional masses to the bridge deck. The effect of spatial-temporal availability of monitoring data on detectability of damage is investigated by synthesising the responses of a range of different combinations of sensor-carrying vehicles. The efficacy of vehicle-borne and bridge-mounted sensors for damage detection is compared.

Richard May
Weld Condition Monitoring Using Expert Informed Extreme Value Analysis

On-board acceleration measurements bear significant potential for the early detection of damage to railway infrastructure components. Condition assessment forms a complex problem in this case, due to the mobile nature of the On Board monitoring (OBM) solution, and the uncertainties associated with the rail-wheel contact dynamics, a lack of knowledge on the excitation sources (track, rail and wheel irregularities, parameter and self excitation) and the variability of the environmental and operational conditions. Welds are, amongst critical railway components, an essential element where high response amplitudes can occur. The monitoring of welds is still largely based on human assessment via typically visual means. We propose an automated approach to weld condition diagnostics via use of Extreme Value Analysis; an outlier detection schema which allows the early detection of flaws. In a second assessment phase, these potentially damaged welds are then assessed by experts during in-office and on-site inspections. The evolution of these OBM-based weld condition indicators is then tracked over time, leading to early detection of damaged welds.

Cyprien Amadis Hoelzl, Vasilis Dertimanis, Aurelia Kollros, Lucian Ancu, Eleni Chatzi
Vehicle-Based Indirect SHM of an Austrian Railway Bridge: Simulation and In-Situ Test

In recent years the application of vehicle-based indirect Structural Health Monitoring (SHM) to railway bridges has increased significantly and it has been shown that this method provides several advantages compared to traditional SHM methods. However, vehicle-based indirect SHM still entails several challenges that require further research. In this paper, the application of the vehicle-based indirect SHM method is demonstrated numerically and experimentally for determining the natural frequencies of an Austrian railway bridge. At first, the coupled equations of motion of the train-bridge multi-body model are presented and train crossing simulations are conducted numerically considering different train speeds. The vibration responses during train crossing are evaluated for both the train multi-body system and the considered railway bridge model. Different representative evaluation points are chosen at the wheelsets, bogies, and car bodies of the considered train. At second, the resonance frequencies of the bridge are measured in-situ by executing forced vibration tests applying closed-loop controlled electrodynamic shakers. Besides in-situ measurements of the bridge, the considered moving train is also equipped with accelerometers, and the vibration responses of both the bridge and the moving train are measured simultaneously during the duration of several train crossings. The recorded vibration responses are analyzed in the frequency domain and compared with the numerical simulation results. It is shown that the first longitudinal bending frequency of the considered railway bridge can be clearly identified from the computed frequency response spectra.

Michael Reiterer, Lara Betinelli, Andreas Stollwitzer, Janez Schellander, Josef Fink

SHM of Engineering Structures Using Smart Multifunctional Materials and Systems

Frontmatter
Structural Health Monitoring of FRP-Reinforced Concrete Bridges Using Vibration Responses

This paper evaluates the feasibility of using vibration-based structural damage detection methods to identify potential damage in FRP-reinforced concrete (FRP-RC) bridges. A series of dynamic load tests were conducted immediately after construction on a continuous three-span FRP-RC bridge deck to capture its dynamic responses under intact conditions. A detailed finite element (FE) model was developed and validated against the experimental results. This model allows for the simulation and study of the bridge dynamic behavior after cracking develops at different locations along the deck. The effects of cracking severity and location on the bridge were investigated using natural frequency and mode shapes changes. The numerical results showed that natural frequency and mode shape changes are sufficiently sensitive for detecting cracks in the FRP-RC bridge deck. Additional dynamic load tests are scheduled for this bridge so that experimental data, possibly obtained from a cracked deck, can be used to calibrate the FE model after some damage. While recognizing that corrosion is a non-issue for FRP-RC structures, the ultimate question that this research attempts to answer is how the deck flexural stiffness changes over time as the result of traffic loads and possibly predict a service life expectation based on stiffness degradation.

Nafiseh Kiani, Mohammad Abedin, Christian C. Steputat, Armin B. Mehrabi, Antonio Nanni
Strain Monitoring of a Structural Adhesive Bond by Embedding a Polymer Optical Fiber

In recent decades, adhesive bonding technology has become increasingly important in all industrial sectors, for example in the aerospace industry, the automotive industry or the construction industry. This is accompanied by an increased need for monitoring of adhesive bonds, especially for load-bearing, structural joints. In order to detect a possible failure at an early stage, this work is investigating a cost-effective sensor principle based on embedding of a polymer optical fiber (POF) into the adhesive layer. The strain and stress behavior of the adhesive bond are transferred to the POF and change its light-guiding properties. In the presented work, different light coupling conditions into the fiber (full excitation and angle-selective excitation) and different measurable quantities of the transmitted light decoupled from the fiber (photocurrent and far-field) are studied, to find an optimum sensor performance. The combination of a structural polyurethane adhesive with commercially available POF is investigated. The results show a high potential of the method for simple photocurrent measurement (transmission loss of 8% by half of maximum load) and an even more sensitive sensor effect for mode-dependent light power measurement.

Josef Weiland, Michael Luber, Katharina Rostan, Alexander Schiebahn, Rainer Engelbrecht, Uwe Reisgen
Electromechanical Testing of Smart Lime Mortars for Structural Health Monitoring

Masonry structures are characterised by low tensile strength and limited ductility. Excessive static or high-cyclic loading or seismic excitation can lead to large localised strains and cracking. It is therefore essential to monitor the response of masonry structures to external loading, especially in the case of historic buildings and infrastructure. The present work aims at designing novel smart intervention materials for multifunctional application in historic masonry structures as a means of SHM, simultaneously structurally and chemically compatible with the in-situ material. The materials investigated consist of lime mortars mixed with different conductive micro- and nanofillers dispersed in the binder. Smartness stems from the materials’ enhanced piezoresistivity, namely the constitutive relation between strain and electrical resistivity. Through application as a repointing agent in existing structures, these materials can be used as deformation and damage sensors. Electromechanical testing employing cyclic compression was conducted on mortars with different doping levels of three conductive fillers: graphite powder, carbon nanotubes and carbon microfibres. The electromechanical study involved the determination of the piezoresistive gauge factors of the different mixes for determining the optimal doping level for each employed filler. The mortars were evaluated in terms of piezoresistive sensitivity and structural application scalability.

Anastasios Drougkas, Vasilis Sarhosis, Muhammed Basheer, Antonella D’Alessandro, Filippo Ubertini
On the Numerical Modeling of Interlaminar Sensors in a Composite Stiffener: Optimization Under Fracture Mechanical Aspects

In this work, the progressive delamination of a layered composite stiffener due to transversal loads is investigated using finite element analysis. The material parameters are determined by qualitative optimization using existing experimental data. Since continued load application leads to a sudden increase in delamination, potential sensors for Structural Health Monitoring are integrated interlaminar. The focus of this work is on minimizing their weakening effect. For this purpose, a suitable criterion is developed and different sensors in terms of position, geometry and material properties are applied. It is shown that a suitable sensor design can reduce the weakening by up to 97.3%. A further enhancement with 99.8% damage reduction can be achieved by using improved materials.

Max Linke, Rolf Lammering
Metrological Evaluation of New Industrial SHM Systems Based on MEMS and Microcontrollers

Microcontrollers are becoming popular in Structural Health Monitoring (SHM) systems, as they can manage sensors, process data and meet requirements compliant with the cloud and Internet of Things (IoT) paradigms. Similarly, micro electro-mechanical system (MEMS) sensors are spreading for monitoring applications, given their appealing costs. Considering the importance of data for a SHM-targeted decision-making process, a conscious use of these technologies requires a deeper analysis from a metrological point of view, to ensure reliability and robustness of the provided data. Consequently, in a multi-node sensing architecture, concepts like sensitivity of each node or data synchronization becomes of uttermost importance, especially if modal parameters extraction is sought. This paper is intended as a warning for designers of SHM monitoring architectures: can we simply replace standard sensing devices with low-cost systems and expect they perform as well as their “stronger brothers”? The answer to this question is tentatively provided by discussing results obtained in a testing campaign performed on some reinforced concrete beams, dynamically tested through two different monitoring systems: a standard, high-performance system exploiting high-sensitivity piezoelectric accelerometers and a low-cost MEMS digital accelerometers-based one, not coupled to a high performance data acqusition system, rather. Modal parameters are considered as the target measure to assess the performance of the two systems.

M. Brambilla, P. Chiariotti, F. Di Carlo, P. Isabella, A. Meda, P. Darò, A. Cigada
Monitoring Road Infrastructures with Self-sensing Asphalt Pavements

Structural health monitoring (SHM) of road pavements is an essential task, which can help the decision-making process for timely maintenance actions. Embedded sensors are typically used to collect long-term monitoring data. However, the main drawbacks of intrusive sensors concern the risk of premature damage and the incompatibility of the sensors with the host material. Self-sensing asphalt mixtures can be used to overcome these limitations. These kinds of smart materials can autonomously monitor their strain and damage states without the need for embedded sensors. The sensing mechanism is based on the piezoresistive effect, consisting of a change in the electrical conductivity of the material when subjected to external loading. To endow the asphalt mixture with piezoresistive function, a proper amount of conductive additive should be incorporated without compromising the mechanical performance of the pavement.The present work aims to design piezoresistive asphalt mixtures for the development of SHM and traffic management systems. Multi-walled carbon nanotubes (MWNTs) and graphene nanoplatelets (GNPs) were added to the asphalt mixture with this purpose, and the piezoresistive response was tested at laboratory scale. The results show that piezoresistive asphalt mixtures have excellent self-sensing properties and can be effectively used for SHM, traffic detection and weigh-in-motion applications.

Federico Gulisano, Thanyarat Buasiri, Andrzej Cwirzen, Juan Gallego
Multifunctional Super-Fine Stainless Wires Reinforced UHPC for Smart Prefabricated Structures

Owing to their excellent mechanical, electrical, thermal, electromagnetic characteristics, micro diameter and high aspect ratio, super-fine stainless wires (SSWs) can form widely-distributed reinforcing and conductive network in ultra-high performance concrete (UHPC) at low dosage, thus endowing UHPC with multifunctional properties including enhanced mechanical performances and durability as well as smart property. In this paper, the mechanical, electrically conductive, and self-sensing performances of SSWs reinforced UHPC were investigated. The modification mechanisms of SSWs to UHPC were revealed by microstructure, electrochemical impedance spectroscopy and intrinsically electrical conductivity analysis. The results show that the 1.5 vol. % of SSWs can increase the flexural strength, flexural toughness of UHPC unnotched beams and equivalent flexural strength of UHPC notched beams by 103.2%, 146.5% and 80.0% respectively. Meanwhile, the dynamic impact toughness and dissipate energy of SSWs reinforced UHPC with the strain rate ranging from 94 /s to 926 /s are increased by 43.5% and 58.2%, respectively. The electrical resistivity is reduced by six orders of magnitudes and the electrochemical impedance spectroscopy only shows small flat arcs in the first quadrant in UHPC containing 0.5 vol.% of SSWs in diameter of 20 μm. The gauge factor of UHPC reinforced with SSWs in diameter of 8 μm can reach up to 22.5, 94.9 and 43.6 under cyclic compression, monotonic compression and flexure, respectively. The microstructure analysis indicates that the modification effect of SSWs on UHPC results from the extensive network of SSWs in UHPC, the inhibition on micro-cracks propagation and the pull-out and stripping of SSWs under loading. SSWs reinforced UHPC has great potential for developing pre-cast structural elements or cast-in-place joints for prefabricated structures, which will achieve the monitoring for structural key positions and can be efficiently used for health monitoring and safety assessment of prefabricated structures driven by the sensing information of multifunctional concrete. This will further avoid the issue concerning high cost of large scale application of multifunctional concrete. In addition, the smart pre-cast structural elements can be reusable and replaceable. Therefore, developing SSWs reinforced UHPC for prefabricated structures can make infrastructures safer, more durable and sustainable.

Sufen Dong, Siqi Ding, Baoguo Han, Jinping Ou
Framework for Strain Measurements at Cyclic Loaded Structures with Planar Elastoresistive Sensors Applying Electrical Impedance Tomography

In the field of Structural Health Monitoring (SHM), strain is an often-used parameter for the evaluation of the structural integrity. A rather new approach to efficiently monitor the global strain field of a mechanical structure is to employ planar elastoresistive sensors as surface coating in combination with Electrical Impedance Tomography (EIT). Furthermore, the EIT approach simultaneously allows the monitoring for damages of the sensor area. The aim of this experimental research is the evaluation of homogeneous strain states under cyclic loading using planar elastoresistive sensors applying EIT. The purpose is to develop an experimental framework that involves various methods for strain evaluation (e.g., EIT, Montgomery method, strain gauge) in order to have a suitable test setup for new EIT evaluation methods and sensor material properties (e.g., set-in effect) including long-term behavior. In the future, this framework shall allow the investigation and improvement of EIT reconstruction for non-static loaded structures. In the present study, the considered specimen is a cyclic loaded tensile test coupon. For this first investigation, static load steps discretize the cyclic loading. Additional strain measurement methods, such as a traditional strain gauge, are applied for validation.

Jonas Wagner, Christoph Kralovec, Daniel Kimpfbeck, Lukas Heinzlmeier, Martin Schagerl
Impedancemetry for Cure Monitoring and SHM of CFRP

During the manufacturing of airframe structures made of CFRP, a protocol of cure monitoring is essential to ensure production security and product quality. In this work, a cure monitoring method based on electrical impedance ( $$\bar{z}$$ z ¯ ) measurements is presented. The electrical impedancemetry method enables us not only to monitor the degree of cure, but also to detect any faults at different stages of manufacturing of composite laminates. According to reference signatures, curing processes could be controlled, resulting in cost savings and quality improvements of composites for aeronautics. In this study, the proposed approach does not require a dedicated sensor as the functionalized composite is used as a sensor of its own state for self-sensing purpose. It has been previously shown [1–3] that thermomechanical and rheological measurements can be used as references of critical points to be monitored such as liquefaction, gelation, and vitrification of the matrix. During curing, these critical points but also faults could be detected using the presented approach. A first bench was developed using an electrical impedance analyzer (Hioki IM3570). This apparatus is expensive, bulky and monochannel thus a new home-made low-cost multi-channels bench has been developed. It would enable us to measure surface or bulk impedance changes of CFRP laminates in real time not only for cure monitoring but also for mechanical monitoring purpose.

Huikangyue Bao, Philippe Marguerès, Philippe Olivier
Interface Engineering of Embedded Mechanoluminescence-Perovskite Self-powered Pressure Sensor for Improved Performance

There is an increasing need for the development of novel self-powered, inexpensive, and flexible pressure sensors with the potential for structural health monitoring (SHM) applications. The mechanoluminescence (ML)-perovskite pressure sensor is a promising integrated device that integrates the light-emitting principles of mechanoluminescence and the light-absorbing properties of perovskite materials. Long-term stability is crucial for continuous in-situ SHM using embedded sensors. This study reported a high-stable all-inorganic carbon-based perovskite photodetector integrated with an ML layer of ZnS:Cu. Interface engineering was employed in the modification of the electron transport layer (ETL) by the addition of a MgI2 layer. Compared with devices without modification, devices based on the modified ETL showed an increase in response time and overall performance. Further studies showed improved sensor performance for impact events when embedded in a carbon fiber composite structure. The results show that the sensor exhibits distinct signals when subjected to different load conditions and can be used for the in-situ SHM of advanced composite structures with promising long-term stability.

Lucas B. Carani, Vincent O. Eze, Okenwa I. Okoli
Smart Bricks for Monitoring Strain in Full-Scale Masonry Structures: Recent Advances and First Field Application

Many masonry constructions have a cultural or historical value which needs to be preserved in time. Because of the aging of construction materials, lack of maintenance, exposure to amplified service loads or natural hazards, and the occurrence of structural pathologies, such as foundation settlements, ancient masonry structures often demand for retrofit interventions during their service conditions. Such interventions are aimed at restoring/improving their structural integrity for in-time preservation and to ensure a safe living of their occupants. Structural Health Monitoring (SHM) systems can be effectively used as a source of knowledge of the structural performance of masonry buildings to optimize such structural interventions. Recently, new piezoresistive smart sensors, similar in appearance to the traditional clay bricks and therefore called “smart bricks”, were developed to achieve diffuse strain monitoring in masonry structures, thus facilitating the large-scale deployment of monitoring systems to such a building typology. This paper presents the recent advances achieved in smart brick technology, including the first field application of such smart sensors for monitoring strain in a full-scale masonry building prototype subjected to controlled damage. The production of smart bricks, their deployment within the masonry load-bearing structure, and the first results of the experimentation are presented.

Andrea Meoni, Antonella D’Alessandro, Giorgio Virgulto, Nicola Buratti, Filippo Ubertini
Multifunctional, Smart, Non-Newtonian Polymer Matrix with Improved Anti-impact Properties Enabling Structural Health Monitoring in Composite Laminates

Autonomous Structural Health Monitoring (SHM) has been introduced in composite structures extensively over the last decade in an attempt to proactively monitor potential internal defects, however active/passive control of their integrity status still remains a challenge. In this work, a novel, non-Newtonian multifunctional polymer with unique active/passive capabilities is proposed for impact protection and SHM of composite laminate structures. This Polyboro siloxane(PBS)-based polymer with unique shear-dependant energy absorption characteristics, owed to a phase transition occurrence within its polymeric network, was utilised as scaffold for ferromagnetic iron particles which enabled the manufacturing of the multifunctional matrix for Glass Fibres Reinforced Polymer (GFRP). The iron particles were positioned in the polymer matrix, which was reinforced with glass fibres and employed as outer ply of a laminate structure. Their presence enables a dual functionality of the multifunctional layer: firstly, in the presence of a magnetic field, triggers the phase transition of the polymeric network offering protection to the laminate in case of impacts, and secondly, postimpact allows for the assessment of the internal integrity of the component, acting as an embedded heat source for active Infrared (IR) Thermography. The ability of the iron particles to initiate the phase transition was investigated by means of Low Velocity Impact in the presence/absence of a magnetic field and the laminates were then examined by means of induction thermography, for the evaluation of the internal damage. Results revealed that iron particles in the presence of a magnetic field led to an enhanced protection of the composite laminates, significantly reducing the extent of the internal damage. This novel, low-cost multifunctional layer provides a unique solution for the protection of composite materials, addressing their inherent weak resistance in out-of-plane direction and providing affordable SHM, thus opening new perspectives for smart structural materials which are in great demand in engineering sectors.

Konstantinos Myronidis, Marco Boccaccio, Michele Meo, Fulvio Pinto
Smart Fiber-Based Sensor Systems for Hydraulic Engineering

A wide variety of damage mechanisms occur in hydraulic structures. The most important of these are pre-damage/assembly, crack/leakage, slope failure, erosion, groundwater intrusion into walls, etc. Depending on the application, the use of smart fiber-based sensor systems to sense undetectable damage can prevent high financial losses. By using smart fiber-based sensor systems in hydraulic structures, the users of this technology are able to continuously monitor the condition of the structures and derive extrapolations for required maintenance. As a result, undesirable conditions such as leakages in water distribution systems, dams and dikes can be avoided and measures for repair and maintenance can be planned in a more targeted and cost-efficient manner. In addition, the application of intelligent sensor systems leads to performance-based design and operation of hydraulic structures, since sensor technology can be used to obtain information about the condition of the structure and actual load cases. In this article, different fiber-based sensor systems are presented which can be used in particular for monitoring tasks in hydraulic engineering.

Gözdem Dittel, Martin Scheurer, Thomas Gries
Broadband Micro-gyroscope Signal Amplification for Enhanced Measurement Sensitivity

Recent developments in the field of structural health monitoring incorporate research on the level of networking as well as on the level of enhancing the used measurement technology. Utilizing these concepts urges new enhancements in the micro-sensor technology. As an example, micro-gyroscopes play an important role within the network of sensors in monitoring cracks or other impairments of structures, specially at points of rotation. A good example is the measurement of hinge rotations in large structures. Enhancing the performance of micro-gyroscopes is normally determined among others by the scale factor (sensitivity), Angle Random Walk and bias stability, which are dependent on the design as well as the control of a micro gyroscope. Our attention here is mainly given to micro-ring-gyroscopes, which offer some advantages with respect to the conventional comb-based micro gyroscopes, mainly due to the inherent geometrical symmetry. However, increasing the system’s sensitivity has been for long coupled to an increase of signal noises, thus, impairing the other performance measures. Applying high forced excitation on gyroscopes, leads to electric feed-through due to parasitic capacitances, thus increases noise. On the other hand, electronic amplification methods adds more electronic noise to the output readout signal. Avoiding such complications encouraged considering “mechanical” amplification techniques, which could enhance the mechanical system’s response before getting to the read-out electronics. To this end, many researchers have followed another alternative to the common forced excitation, which is the parametric excitation. Parametric excitation of micro gyroscopes started more than one decade ago, however, it was only useful at resonance frequencies, which causes complexity of the control system due to the required precise frequency tuning. In our contribution, however, we introduce a novel method of excitation in the field of micro-sensor technology, that is, a phase-shifted bimodal parametric excitation, which offers parametric amplification at “non-resonant” frequencies as well.

Barakat Ahmed A., Peter Hagedorn
A Sensitivity Study of Different Actuators for the Electromechanical Impedance Method in 3D-Printed Material

The electromechanical impedance (EMI) technique assesses the health of structures in the frequency domain using piezo-actuators. EMI employs a high-frequency range and is a promising method to be used for the evaluation of lightweight structures. This study focuses on understanding the comparative sensitivity of different actuators to damage presence in additively manufactured (AM) polymer structures. An acrylonitrile butadiene styrene (ABS) plate printed with horizontal layup was used for the investigation, where 2 PZT and 1 MFC actuators were fixed on the surface. Initially, damage was simulated by moving small magnets away from the actuators on both sides of the plate. Further, an impact damage was created at the center of the plate to compare the damage sensitivity of the three actuators. While both actuator types have shown a good damage sensitivity and coverage, a better sensitivity was observed in the case of the MFC actuator, as compared to PZTs. The attained damage index results of impedance and conductance are promising for the assessment of AM polymer structures.

Shishir Kumar Singh, Mohammad Ali Fakih, Paweł Malinowski

Human Performance Monitoring

Frontmatter
Integrated Vision-Body Sensing System for Tracking People in Intelligent Environments

An integrated sensing architecture and “big data” analytics that leverages real-time human biometric monitoring with state-of-the-art human activity recognition (HAR) can open exciting new possibilities in human health and performance monitoring. We present a framework for integrating data streams from computer vision detection algorithms and on-body sensors through a series of experiments on human subjects with wearable sensors. These experiments were carried out in the MCity facility at the University of Michigan. Our tests utilize urban environments of Mcity to obtain human activity data streams from a network of accessible cameras and wearable biometric sensors transmitted via LoRa to an access point. Data are then forwarded to Amazon’s cloud computing platform, Amazon Web Services (AWS). Once on AWS, GPU servers running real-time HAR algorithms can access and integrate biometric sensor data. We show the results of computer vision tool development using image/video based object detection, tracking, and mapping. Our work of developing real-time on-body device combined with device-free activities recognition will advance capabilities of monitoring personnel in their environments for variety of industrial and defense applications.

Gabriel Draughon, Jerome Lynch, Liming Salvino
Wearable Sensor Platform to Monitor Physical Exertion Using Graphene Motion Tape

A primary concern during physically demanding activities is the risk of musculoskeletal injury. Sensing systems that monitor muscle activity can provide early warnings of physical fatigue, thereby improving the safety and effectiveness of labor. This work presents one such system in the form of a power-efficient, wearable sensing platform to infer distributed muscle exertion in real time. The system uses “Motion Tape,” which is a new type of skin-strain sensor fabricated by integrating graphene nanosheet thin films with commercially available kinesiology tape (K-Tape) that is then attached to human subjects. Here, Motion Tape is interfaced with the Urbano IoT sensor node, which includes an onboard microprocessor, wireless transceiver, analog-to-digital converter, input/output peripherals, and memory. In contrast to existing body sensor networks that require many sensors and nodes to monitor physical activity, the system presented herein captures distributed muscle engagement and skin strains over larger areas with fewer sensor nodes. This paper offers an overview of the system hardware design, the embedded software used to compress raw Motion Tape signals, and a preliminary validation on human subjects performing bicep curls.

Aaron Appelle, Yun-An Lin, Emerson Noble, Liming Salvino, Kenneth J. Loh, Jerome P. Lynch
Stranger Detection and Occupant Identification Using Structural Vibrations

Human-induced structural vibration provides valuable information about humans who interact with the structure, including people’s identity, characteristics, activities and health status. Among them, person identification is crucial because it is the premise for any personalized services. Our prior work using footstep-induced structural vibration has been promising in identifying a fixed group of people, but it is restricted to registered occupants, leading to errors whenever a stranger appears. Therefore, we introduce a stranger detection and occupant identification system using footstep-induced structural vibrations. There are two main challenges in developing this system: 1) the strangers’ walking patterns are unknown before being observed, and 2) the probability of the presence of a stranger varies at different times and locations. To overcome the first challenge, we model the occupants’ and strangers’ footstep-induced floor vibration data as a mixture of Gaussian distributions with varying number of components, representing the changing number of registered people. To address the second challenge, we reformulated the prior estimation in the mixture model by introducing an interpretable parameter that represents the expected probability of observing a stranger. Through an experiment conducted on a wood-framed structural platform, our method achieves an average accuracy of 89.2% in person identification among 10 people.

Yiwen Dong, Jonathon Fagert, Pei Zhang, Hae Young Noh
Fractional Calculus as a New Perspective in the Viscoelastic Behaviour of the Intervertebral Disc

The spinal column is the load-bearing structure of the human being along with its components, which together build a strong, resistant, and stable structure, but there are a few different pathologies from which it can suffer, such as herniated discs. The intervertebral disc acts as a shock absorber and ensures the spine’s great capacity to support high loads and different states of stress, thanks to its viscoelastic properties. Some studies have attempted to describe the viscoelastic behaviour of the intervertebral disc using classical rheological models, such as the Kelvin-Voigt, or multi-parameter models. Even if these models partially describe the viscoelastic response of disc, all viscoelastic characteristics are not fully captured. This article aims to present the current studies on the biomechanics of intervertebral disc and to introduce a new approach using the powerful mathematical tool of fractional calculus. With fractional rheological models, it could be possible to formulate a fractional law that can fully describe the viscoelastic behaviour of the intervertebral disc. This new approach could lead to a breakthrough in the study of herniated pathologies by understanding how the intervertebral disc is damaged and identifying strategies to deal with these pathological problems.

Vincenza Sciortino, Donatella Cerniglia, Salvatore Pasta, Tommaso Ingrassia
Backmatter
Metadaten
Titel
European Workshop on Structural Health Monitoring
herausgegeben von
Prof. Piervincenzo Rizzo
Prof. Alberto Milazzo
Copyright-Jahr
2023
Electronic ISBN
978-3-031-07254-3
Print ISBN
978-3-031-07253-6
DOI
https://doi.org/10.1007/978-3-031-07254-3