European Workshop on Structural Health Monitoring

Earthquake is a natural hazard which is inevitable. Ductility ensures large inelastic deformation without significant loss of strength. It has been proven that ductile detailed structures give better performance than non-ductile detailed structures. In India after Bhuj 2001 earthquake, there is a change in design practices and lots of emphasis are being on ductility based design. But majority of structures which are constructed before 2001, do not satisfy criteria of ductile detailing. Experiment is performed for performance evaluation of Reinforced Concrete (RC) portal frames under lateral loading. Out of two portal frames, one frame confirms ductility based criteria given in Indian seismic code and another frame is not satisfying the criteria for ductile detailing. Experiments are performed on these portal frames to evaluate its performance under lateral load. Nonlinear static analysis is carried out. Results obtained through experiments are compared with numerical study performed through commercially available software. Study also confirms that the ductile detailed structure gives better performance than the non-ductile loaded under lateral load.

Seismic structural health monitoring (S2HM) has advanced significantly in the last three decades. However, currently there is no consensus on the need for real-time processing of data acquired during an earthquake. Numerous applications exist whereby S2HM-equipped systems record valuable seismic response data. A delayed use of the seismic data prohibits timely discovery of hidden damages in a structure which, in turn, possibly increases its vulnerability during events to follow – with increased risk to occupants. Such risks are of particular concern when, for example, there are long-distance/long period effects e.g. for tall buildings and long-span bridges that are significantly affected by events that originate at far distances. These phenomena necessitate near real-time monitored data to make timely data-based informed decisions on the health or performance of the affected structure. The paper discusses criteria for functionality and occupiability thresholds in actual applications.

We applied an integrated, non-invasive and non-destructive seismic and electromagnetic sensing for understanding the static and dynamic properties of the Gravina bridge and its interaction with foundation soils. The ‘Gravina’ is a bow-string bridge located in the city of Matera (Southern Italy) that extends for 144 m along a steel-concrete deck.For foundation soils characterization we executed 3 high-resolution geo-electrical tomographies, 1 bi-dimensional seismic array and two single station seismic noise measurements. The main structural characteristics of the bridge were evaluated through seismic and electromagnetic sensing. The seismic sensing was carried out with four accelerometers and twelve velocimeters (standard and low cost sensors) installed with different geometrical arrangement for real-time and on-demand ambient noise recordings, vibration tests and earthquake recordings. The electromagnetic data have been collected by placing the IBIS-S system below the deck of the bridge. Acquired data have been analyzed in frequency domain with the aim to evaluate the eigenfrequencies and equivalent viscous damping factors.

This paper aims to investigate uncertainties in damage identification results due to errors in modal parameter estimation results. Structural damage is simulated as regional stiffness loss at the column(s) and beam ends of a numerical frame type structure. In the damage identification stage, the first 4 modal parameters are used. Two different levels of noise are added to them to simulate uncertainty in modal parameter estimations. Noise levels are controlled by the coefficient of variation (C.O.V.). In order to quantify the uncertainty of the identified damage due to the variability of modal parameters, a full factorial analysis of variance (ANOVA), resulting in 16 combinations of input factors, is used. For each combination of input factors, 20 noise realizations are generated using Gaussian normal distributions with standard deviations scaled to the level of modal parameters. The results are presented in two formats: (1) Spread of the identified damage factors for all 320 identification runs with their statistical measures and (2) R2 values of the mean and standard deviation of the identified damage factors due to the variability of each input factor. The results of this investigation demonstrate that specific modal parameters have only influence on specific damage factors.

Sonic Echo/Impulse response (SE/IR) is an economical nondestructive method to collect information pertaining to the unknown bridge foundations. Studies have shown that factors such as the pile-to-soil stiffness ratio, the length-to-diameter ratio of the pile, the presence of the defects, the anomalies near the pile head, the quality of the sensor attachment, the striking method, and the hammer type affect the success of the SE/IR tests. In the current study, numerous SE/IR tests were performed on three known and unknown bridge foundations and the superiority of the time domain analysis to the frequency domain analysis is concluded as an outcome helping engineers conduct SE/IR tests efficiently. In addition, more affecting factors and difficulties specific to timber foundations are identified and discussed. The results presented in this study shows that factors such as the foundation condition, the environmental conditions, and the improper pile-pile cap attachment can also be the sources of difficulties in determining the depth of timber piles.

The predictive monitoring and maintenance of future transportation infra-structures will be based on intelligent technologies, such as smart wireless sensing devices. In order to efficiently manage the delivery of crucial information about the structural and environmental conditions detected by wireless sensing nodes, and to suddenly process or exchange the information above with different stake-holders (e.g., authorities, drivers, etc.), the forthcoming fifth-generation (5G) network should be properly exploited. Consequently, this paper aims at illustrating the main requirements for enabling the transmission of the information gathered by sensing devices specifically designed for monitoring the structural and environmental conditions of road pavements and carrying out maintenance and rehabilitation. Different types of sensors (i.e., able to detect accelerations, noise, temperature, humidity, fire and smoke) are included in each sensing device, located on the shoulder of the carriageway (non-destructive structural health monitoring method). Sensor data are sent to the Edge of the network for further data processing. Proper algorithms are used to derive the vibro-acoustic signature of the monitored road pavement from the vibrational and acoustical data, while environmental-related data are processed to carry out the real time detection of unexpected events (e.g., a fire or an accident) on/around the road infrastructure. To this end, based on the sensed data size and on the sensing nodes density, several network-side requirements (such as the amount of deliverable data and cell dimension) for enabling the transmission of sensing data over 5G networks are analyzed in this study. Results demonstrate that monitoring and maintenance activities should be designed bearing in mind communication and energy-related requirements and issues.

In this work we propose a Disaster Management System on 5G ultra Reliable Low Latency Networks that targets unprecedented reliability levels as well as low latency. In fact, referring to the 5G vision a Structural Health Monitoring system can be considered depending on the operational scenario: in the case of data collection and processing from sensors in monitored buildings, considering the high number of sensors installed, it can refer to the massive Machine Type Communications context. Vice versa, during a seismic event or just after it, the use case requires high reliability connectivity and, sometimes, low latency. Those features refer to the ultra Reliable Low Latency context. It seems interesting to evaluate and experiment the ability of 5G network to dynamically adapt to the changing scenario that this use case can provide. Moreover this work presents an innovative 5G architecture for Earthquake Early Warning that uses Structural Health Monitoring systems to detect a seismic event and to propagate a message reporting the event detection to all the buildings that may be damaged by the event.

This paper focuses on presenting a complete study on the dynamic behavior of Cahora Bassa dam (Mozambique), a 170 m high double curvature arch dam which has been under continuous vibrations monitoring since 2010. The installed Seismic and Structural Health Monitoring system was designed to continuously record acceleration time series in several locations in the dam body (crest gallery) and near the dam-rock interface, under ambient/operational vibrations and during seismic events, using uniaxial and triaxial accelerometers. The system was complemented with the development of software for automatic modal identification and automatic detection of seismic vibrations. The numerical simulations are carried out using a 3D finite element program, based on a solid-fluid coupled formulation to simulate the dam-reservoir-foundation system, considering dam-water dynamic interaction and propagation of pressure waves throughout the reservoir. The main experimental outputs are presented and compared with results from 3D finite element analysis, including the evolution of identified natural frequencies over time, vibration mode shapes, and the seismic response in accelerations. Finally the non-linear seismic behavior of Cahora Bassa dam is studied for an input accelerogram with a 0.6 g peak acceleration, considering the joints movements and a damage model for concrete.

Non-destructive imaging is largely encouraged as a preliminary investigation for damage identification on concrete structural surfaces. Cracks are basic signatures for any structure to initiate the damage. As the whole world is currently connected with lot of cameras all around for various purposes either it be for traffic studies, accident analysis, thefts, natural or human disasters. Alternatively, the same video frames obtained from cameras located in or on the structure can be analysed even for the structural health monitoring. This study aims at identifying the cracks from images mined out of the video frames apart from the crack propagation and length of the crack. Convolution Neural Network is used to train over the images from the video captured during the laboratory compressive strength experiment on a concrete cube to examine and estimate the crack properties. This methodology can be extended to the real-life scenario to alert the damages caused in the structures.

The implementation of systems for Structural Health Monitoring and Earthquake Observation is increasing in the last years owing to the development of new technologies which enable low-cost and small-size devices to be installed in large-scale or high-density applications. This paper introduces the implementation of monitoring systems, either for structural health assessment and earthquake observation. The applications are based in Sicily (Italy), a region characterized by a high seismic hazard and where the buildings are often old and vulnerable. The system relies on a MEMS (Micro Electro-Mechanical Systems) sensor, a 3-axial accelerometer which has been specifically selected in order to ensure the suitability for the specific applications: accelerations from 100 to 102 Hz. We present the details of the designed monitoring station, of the network architecture, and some of the recorded data.

To ensure the safety and the usefulness of civil structures, it is fundamental to visually inspect and survey its physical and functional condition. Current techniques in condition and safety assessment of large concrete structures are performed physically promoting to subjective and unreliable outcomes, costly and time-consuming data collection, and safety issues. This paper presents a study on less time consuming and less expensive alternative to the present methods of preliminary assessment for the detection of damages in structures. Henceforth, the focus is set on various vision-based methods for different parameters like cracks, corrosion and spalling which cause damage and deterioration of structures. Thus, a study is made on the current achievements and drawbacks of existing methods as well as open research difficulties are outlined to help both the structural engineers and the computer science researchers in setting a motivation for future research.

For data-driven vibration-based structural health monitoring (VSHM) systems to be considered reliable they must overcome the challenge of mitigating the environmental and operational variability (EOV) on the vibration features. This is particularly important in large and exposed structures such as wind turbine blades (WTB). This work aims to understand the influence of EOV, namely quantifying the influence of input variables on the selected vibration features. Understanding the specific sources of influence can facilitate better prediction of outliers as well as leading to a VSHM system less sensitive to EOV. This study uses an operational wind turbine with an undamaged and incrementally damaged WTB under three operating conditions (idle, 32 and 43 rpm). The approach calculates frequency transformation based features on the vibration responses obtained from an array of accelerometers along the WTB. Subsequently, the features are regressed on environmental and operational parameters (EOPs) via multivariate non-linear regression. The difference between the regression predictions and the actual feature values is used as a new feature. In parallel, to understand the influence of the EOV, inclusive and exclusive sensitivity analyses were conducted. These analyses compared the likelihood of a model based on one or all but one EOP, respectively, against a model using all the EOP. The results showed that the temperature has the largest influence, with respect to the considered EOP, on the regression likelihood. Ultimately, the obtained regression model was used to normalise the effects on the features and enhance damage detection.

The flexible design of the rotor and the tower coupled with load based control strategies, increase the need for a load monitoring system. These measurement systems require reliability, availability as well as low latency and high measurement sampling rates to cope with the aeroelastic issues of a load based control system. This work aims to quantify the influence of the strain measurement sampling rates and down-sampling methods on a Structural Health Monitoring (SHM) system for wind turbines. To evaluate these influences, fatigue and spectral analysis were performed on the measurements from elongation sensors which were installed in the rotor blade and tower. The primary results from the analysis showed differences of up to 0.3% in the equivalent fatigue load, which could be seen by comparing datasets recorded with the same sampling rates but down sampled with different methods.

Guided waves are finding more applications for structural health monitoring of pipelines and other long, slender structures, particularly in the areas of corrosion and crack detection. Bolted joints are widely used in engineering structures in oil and gas, aerospace and civil structures. In practice, pipes with bolted joints are subjected to a variety of failure modes, including self-loosening, slippage, shaking, fatigue cracks and breakage. Guided waves technique is one of the promising techniques for detecting various damage types in pipelines, such as fatigue crack, impact damage, notches, holes and imperfect bolted joints.Guided waves technique for pipelines, involves transmitting guided waves along the pipe length. Using this method a relatively large region of pipe can be inspected from a single location. The system has the ability to transmit waves from a remote single location of the pipe and inspect inaccessible areas, such as road crossings and insulated pipes without causing any damages. The technique is especially sensitive for detection of damage in pipes. This technique allows a rapid screening of the all pipe; screening tools for fast assessment of large parts of installations shown to have a growing inspection potential.However most of linear guided waves techniques rely on baseline data which is the major drawback of the technique. In this paper a base line free approach is used to detect the imperfect bolted joints in pipelines using nonlinear guided waves. It is shown that imperfect joints generate contact acoustic non-linearity (CAN) which is a good indicator. The study shows that nonlinear wave packs carry important information about the quality of bolted joints in pipelines.

Thanks to their high strength to mass ratio, composite materials are now widespread in the aerospace industry. Nevertheless, this type of material is subject to internal damages like delamination. In order to detect and localize these damages, robust and precise Structural Health Monitoring algorithms exist for this purpose and has been validated experimentally. However, in order to avoid structures catastrophic failures and to estimate their residual life, there is still a huge need of reliable damage size assessment methods. In this paper, a damage quantification method is proposed. This strategy is based on the extraction of a damage size sensitive feature computed from damage imaging results. Here damage imaging stands for methods that use ultrasonic Lamb waves-based map of damage localization likelihood index. This feature is extracted from each labelled example of a training set in order to infer a mathematical model used to predict the area of a delamination of unknown damages. The proposed method is successfully validated on experimental data carried out on CFRP plate samples equipped with a piezoelectric transducers network.

The present study focuses on the development of amplitude and physics-based nonlinear parameters for estimating the material nonlinearity and eventually the remnant useful life of fatigued plate structures using Lamb and Rayleigh waves. In the numerical simulations, these waves are propagated through Aluminum (Al) plate structures, and as a result of material nonlinearity, a second harmonic is generated in the response. The spectral amplitudes of the second harmonic are then subsequently used in the amplitude-based nonlinearity parameters to evaluate the nonlinearity for different distances and for different stages of fatigue. The physics-based nonlinear parameter is dependent on the sub-structural evolution parameters and higher order elastic and plastic constants. As it is independent of distance and depends only on the percent of fatigue life, it can be used to construct a theoretical nonlinearity curve (TNC). The estimation of material nonlinearity for different fatigue life through the simulation and amplitude-based parameter is found to be in close agreement with the TNC under the predefined conditions of distance and cycles in the excitation signal. Thus, the knowledge of material nonlinearity parameters evaluated for the pristine and fatigued thick plate specimens using Lamb and Rayleigh waves is shown in the present study to be useful for evaluating the remaining useful life of the fatigued specimens with fair accuracy.

Doubly curved shells are used as structural members in space launch vehicles as part of propellant tanks, pressure bulkheads in aircrafts, submarine-hull, etc. SHM techniques for these structures are limited in the literature. The present study is based on the fact that higher harmonics will be generated in guided wave propagation in presence of Contact Acoustic Nonlinearity (CAN) type defects such as fatigue crack and de-lamination. The higher harmonics are generated due to non-linear interaction of the crack surfaces when the incident wave passes through these surfaces. Also, the study explores capability of non-linear Vibro-Acoustic Modulation (VAM) technique on doubly curved shell structures for the detection of CAN type defects. VAM is based on the response of the system where effects of modulation of low-frequency vibration (pumping vibration) on high frequency guided wave propagation (probing wave) are studied. In the presence of damage, the frequency spectrum of the response shows sidebands with respect to the frequency of the guided wave excitation. These non-linearity features of guided wave propagation are numerically and experimentally investigated on a doubly-curved-shell structure which has a CAN type defect of partially bonded attachment.

Optimising the O&M activities related to the proprietary infrastructure assets is crucial for a company successful management. Nowadays this topic is gaining even major hype due to the accessibility of new technologies such as big data. Such reasons push the industrial market towards a pursue of effectiveness and efficiency of maintenance strategies. This work inserts itself in this context: born from a collaboration with a company involved in the sector of telecommunications, it aims at the determination of an heuristic model able to estimate, as far as their towers are concerned, the probability of a structural failure. The initial phase is about the analysis of the company’s database to identify the variables to be considered in the study. Then the focus is shifted on the research of a synthetic index that could summarise the contribution of the aforementioned parameters. Subsequently, a sensitivity analysis aimed at testing the robustness of such index is carried out, followed by a subdivision of the entirety of the assets into homogeneous clusters in terms of loads and structural properties. The clusterization is intended to feed the “risk” axis of a risk-impact matrix to be used to tailor the O&M best practices to apply to the fleet of communication towers. The predictive capability of such representation will be assessed performing physical checks on a number of towers sampled from the matrix.

This study is concerned with locating surface defects that occur in rail tracks. Ultrasonic Rayleigh waves were used to investigate the rail track surface. To generate and sense these waves a fully non-contact laser ultrasonic transduction system was employed. The laser-generated signals are in general more susceptible to environmental noise in comparison with signals generated by other methods. Meanwhile, the quality of signals received from one point may vary in each time of measurement. Continues Wavelet Transform (CWT) is a practical tool in dealing with complicated signals. In this regard, CWT works better if its mother wavelet is carefully selected based on the nature of the analyzing signal. Seeing that, a library of mother wavelets was tailor-made for studying laser-based Rayleigh waves in rail tracks. Mother wavelets were designed based on characteristics of the incident wave packets after extensive measurements on rail tracks. For analyzing a signal, initially, the first biggest wave packet that is the incident wave is recognized. Absolute cross-correlation is then used to pick a mother wavelet from the library that has the maximum resemblance with the incident wave. Using such an approach, the irrelevant wave packets can be easily discarded, and surface defects are exposed.

Structural health monitoring (SHM) deals with the early detection of structural damages to prevent catastrophic failures and is expected to provide major improvements with respect to safety and maintenance costs. With the increasing development of aeronautic industry, composite materials are being more and more widely used. In this case, the SHM of composite structure is crucial, especially the monitoring of local delamination of plies in composite materials. This paper presents an investigation on the detection of delamination type damage in carbon fiber reinforced polymer (CFRP) composite plates based on nonlinear acoustic effects. The LASER shock wave technique is used to generate realistic delamination in the composite plates. A damage index (DI) is proposed in this paper based on total harmonic distortion (THD) to evaluate the nonlinear acoustic effects induced by delamination. Experiments are conducted on four plates containing different sizes of delamination, including one undamaged plate for reference. Results show that acoustic nonlinearities are generated due to the presence of a realistic delamination damage, and the proposed DI is appropriate to evaluate the influence of the delamination size on the nonlinear acoustic effects under different excitation amplitudes.

In the last few years, researchers have paid more and more attention to Shear Horizontal (SH) waves propagation characteristics as new approach used for damage detection. In particular, the fundamental SH0 mode is interesting due to its non-dispersive characteristics and single-mode existence in a certain range of frequency. These features offer promising applicability for developing a new Structural Health Monitoring technique. In order to examine damage detection features of the SH0, it is necessary to first investigate it via numerical simulations. Thus, in this paper, a new modelling approach is developed, based on the Local Interaction Simulation Approach (LISA), which allows to selectively simulate the propagation of SH waves. Both linear and nonlinear material definitions are taken into consideration to investigate propagation features of the aforementioned waves. In the latter case, the Landau-Lifshitz model and the Green-Lagrange strain-displacement relation is used. Furthermore, a local type of nonlinearity, such as a crack, is introduced to the model as well. The high-order harmonics generation is investigated for various cases, depending on the particular presence of the nonlinearity source. Based on the simulation results, the influence of propagation distance on the magnitude of high-order harmonics is evaluated and a comparative analysis is carried out in order to distinguish the sources of the nonlinearity. Presented results demonstrate that LISA is a sufficient tool for the SH-wavefield analysis.

Subfloors are layered structures, consisting largely of porous building materials, such as screed. They are often suffering damage from tap water leakage, which is a typical problem in buildings, and which has largely contributed to repair costs of almost 3 billion Euro in 2018 alone in Germany. In this context, especially mould plays a role, which is both destroying the structure and posing severe health risks.To determine the damaging effects of moisture, it is necessary to know the respective processes occurring in building materials, especially to quantify the amount of moisture and its progress in the material. In this study, humidity sensors are used to derive the material moisture experimentally.Capacitive sensors recording the relative humidity are embedded into the screed and in the insulation materials such as expanded polystyrene, extruded polystyrene, perlite and glass wool. For the application in screed, the sensors need to be shielded against the aggressive alkaline materials. To ensure an appropriate exchange with the environment, a permeable membrane is requested. Different membrane materials have been investigated regarding their robustness and their permeability.In the first experimental setup, two humidity sensor arrays with seven individual sensors are embedded in homogeneous screed samples. The measured corresponding relative humidity of the screed is converted to the material moisture based on the approach of Hillerborg. In a second experimental setup, a layered structure of a complete subfloor is built in a box of 0.8 m times 0.8 m. The humidity sensors are positioned in the different insulation materials of various thicknesses. By adding water, leakage damage is simulated and its progress and effect is investigated experimentally.The investigations point at the question if the observed moisture is able to generate damage such as mould. The moisture and corresponding humidity values are discussed. It will be shown that this low-cost hygrometric approach can be used easily for moisture monitoring of screed and insulation materials as well.

An experimental Structural Health Monitoring System has been set on one pier of the Millau Viaduct end of 2018. The results of more than one year of continuous strain measurements at a high sampling rate are released, focusing on different Data Analysis features which have been used on the large amount of raw data collected.The Monitoring System is a set of 16 Optical Strand strain sensors synchronized by a Data Acquisition System which enables real-time high-sampled measurements. All raw measurements are gathered on a distant cloud and available through the internet.Three different Data Analysis features are discussed, which give results on various phenomena from the same strain measurements, considering different time scales: Firstly, averaged measurements are combined in order to get an estimation of the displacement of the pier top under the effect temperature variations, secondly the high-rate sampled measurements are used to assess the effects of the usual traffic on the bridge pier and detect vehicles which effect in terms of strain is the most significant. Finally, the data gathered during periods of strong winds is used for Dynamic Identification of the vibration modes of the pier by the mean of an Eigensystem Realization Algorithm.

This article contributes in the research direction of the application of Machine Learning techniques in bridge safety assessment and it lays basis to further improve the accuracy of safety assessment including analysis of real data.The communication puts forward the process and model of scale measured points correlation of bridge monitoring system on the frequency domain as a tactic to control the influence of a railway device (crossing) located on the top deck of a railway bridge. The process and model are put forward mainly for the characteristics of the damage detection for long-term assessment, going from an intensive multi-sensor monitoring system to a softer one. Finally, a Gradient-Boosting multi-regressor method has been developed to be easily implemented in a warning system that provides predictive skills to the current preventive maintenance strategy. The method is validated by simulating the undamaged and abnormal scenarios with Monte Carlo method.

We present a simple and fully automatable vibration-based Structural Health Monitoring (SHM) alert system. The proposed method consists in applying an Automated Frequency Domain Decomposition (AFDD) algorithm to obtain the eigenfrequencies and mode shapes in real time from acceleration measurements, allowing to provide a diagnosis based on a Support Vector Machine algorithm trained with a database of the modal properties in undamaged and damaged scenarios accounting for temperature variability. The result is an alert system for controlling the correct performance of the structure in real time with a simple but efficient approach. Once the alert is triggered, the undamaged mode shapes (which could be previously stored in a database of modal parameters classified by temperature) and the current (damaged) mode shapes, can provide guidance for further application of Finite Element Model Updating (FEMU) techniques. The method is trained and validated with simulations from a FE model that is calibrated employing a genetic algorithm with real data from a short-term vibration measurement campaign on a truss railway bridge in Alicante (Spain).

This paper proposes slab vibration model coupled with pier structure on continuous girder bridge. Vibration mode properties are expected as promising indicators in bridge health monitoring. However, there are various kinds of bridge structures depending on geographical conditions. Especially in the case of a continuous girder where an intermediate support is elastically supported by a portal pier with steel beam, vibration coupling with pier structure should be considered for modal analysis of slab vibration. Previous research of slab vibration mode without pier vibration coupling could not see whether extracted modes are physical modes or not. We proposed a slab vibration continuum model on 2-continuous-span girder with a single degree of freedom kinematic system at pier position, and estimated the vibration modes. RC-slab vibration modes were experimentally investigated on a real expressway bridge, which has 2-continuous-span steel girder where the intermediate support is elastically supported by a portal pier with steel beam. As a result, coupling vibration mode which deflected at pier position was estimated theoretically from proposed model. In experiment, the mode was observed by analyzing slab acceleration response measured at 14 points in the bridge. It is concluded that the proposed slab vibration model is effective for modal analysis, and it can be useful for bridge structural health monitoring.

Due to the importance of reinforced concrete structures for modern society, damage assessment during the entire life-cycle of such structures has become a special interest in non-destructive testing. Using embedded ultrasound sensors in combination with other measurement methods, numerical modeling and self-made data collectors, tailored specifically for monitoring tasks, the German research group DFG FOR CoDA aims to investigate and develop novel methods for damage detection and rapid model updating in reinforced concrete structures. In the first stage of the project, besides the development of custom-built, low-cost data collectors, ultrasonic transducers are embedded in a large, reinforced concrete specimen on a BAM test site near Berlin. In this experiment, the influence of changing environmental conditions (mainly temperature) on the ultrasound signal is investigated using coda-wave interferometry. The results show a correlation between changes in temperature and ultrasonic velocity. Such changes must be taken into consideration in a long-term monitoring setup to distinguish between reversible and permanent changes. By correcting the data using a linear relation between concrete temperature and velocity change to remove the seasonal trends and by low-pass filtering the data to remove daily variations can remove most of the temperature influence on the ultrasound measurements.

Ultrasonic Phased Array technology has seen significant development in recent years. A phased array sensor generates stronger ultrasonic beam and facilitates beam steering without physically moving the sensor probe by controlling the excitation of the sensor’s piezo-electric elements. This allows faster and wider ultrasonic scanning compared to conventional sensors. The major challenge for ultrasonic beam focusing and beam steering by a phased array transducer is to control the excitation frequency. In this paper an efficient excitation frequency algorithm has been developed using Distributed Point Source Method (DPSM) to generate stronger, focused ultrasonic beam. DPSM is a mesh free semi analytical technique that has been used for solving a variety of engineering problems such as ultrasonic wave propagation in different mediums. Ultrasonic field calculation in two semi-infinite mediums like liquid-liquid and liquid-solid with regular ultrasonic sensors have already been calculated using DPSM. In this paper, a new approach of modeling liquid-solid interface by a phased array sensor using DPSM has been developed. For a phased array sensor, the angle and the strength of the acoustic beam depend on the number, placement and activation frequency of the point sources. An excitation algorithm for the point sources has been developed to generate strong focused acoustic beams. The time phasing is also used to calculate the acoustic field for a case where a phased array transducer is placed in a semi-infinite liquid which continues onto a semi-infinite solid medium.

Performance of any Structural Health Monitoring (SHM) system strongly depends on a set of sensors which are distributed over the structure under investigation. Optimal deployment of sensors on large scale structures such as tied-arch bridges is quite a challenging problem. Moreover, deployment of a sensor network consisting of different types of sensors (accelerometers, inclinometers or strain gauges) over a large scale bridge renders the task of optimization even more demanding. In the present study, a conventional sensor placement method for distribution of a homogenous sensor network is expanded to the heterogeneous case. First, the basic equations governing the estimation error will be recalled. Then, the Fisher information matrix is assembled using normalized translational and rotational mode shapes. Finally, a computational procedure is proposed which allows optimal sensor positions to be selected among thousands candidate locations. The effectiveness of the proposed strategy is demonstrated using a realistic example of a tied-arch bridge located in Poland.

Under strong ground shaking, buildings may suffer damages leading to strength and/or stiffness degradation. The estimates of such damages may be obtained by identifying parameters, defining a nonlinear model of the building behavior, from the measured building vibration responses under seismic excitation. There are several techniques, like nonlinear system identification and finite element model updating, which may be used in detecting such damages. However, these methods are typically computationally expensive, and often have associated convergence issues. In this work, a fast damage detection technique is developed for detecting damages in buildings under seismic excitations. The method uses the vibration responses of the building recorded during the seismic event, along with the measured ground motion. This measured data is used to estimate the Park and Ang damage index, representative of the level of damage in the building. The ductility demand and hysteretic energy dissipation, necessary in defining this index, are directly estimated from the measured data, bypassing the need of structural parameter identification, making the damage detection computationally faster. The method is illustrated using experimental data from a laboratory scale four story reinforced concrete frame, progressively damaged through shake table tests, with the inter-story hysteretic behavior modeled using the Bouc-Wen model.

The prevention of unfavourable machine-induced vibrations represents a crucial issue for the design of industrial facilities. A special attention is required for the structural assessment of the load-bearing members, that should be optimally designed with the support of specific input parameters. The characterization of the expected vibration sources, together with a reliable structural model, is in fact a key step for the early design stage.In this paper, a case-study eyewear factory is investigated. Its layout takes the form of a two-span, two-story precast concrete frame. The lack of customer/designer communication resulted in various non-isolated Computer Numerical Control (CNC) vertical machinery centers mounted on the inter-story floor. Accordingly, the floor started to suffer for severe resonance issues.This research study focuses on the dynamic investigation of the structure. An efficient, coupled experimental-numerical approach is presented and validated for early predictive studies. Based on field experiments on the floor, but also on the machinery components, the most unfavourable conditions are first detected and characterized with the support of accelerometers and video-tracking displacement acquisitions. The experimental outcomes are then further assessed with Finite Element (FE) numerical models, giving evidence of the accurate predictability of resonance issues.

The Foz Tua hydroelectric development is located in the north of Portugal at the mouth of the Tua river, a tributary of the Douro river, and is equipped with 270 MW of power capacity, making it a very important asset in the country’s energy production capacity. Its reservoir is accomplished through a 108 m high concrete arch dam whose construction was concluded in 2017.The arch dam has been equipped with a vibration-based structural health monitoring system, which is composed by a set of accelerometers that were radially disposed over the two upper visit galleries. The accelerometers are connected to a set of digitizers distributed in the dam, being the synchronization of the data assured by GPS.This paper describes the addressed monitoring system, as well as the results obtained during the first months of operation, such as the characterization of accelerations (maximum and effective values) and the automatic identification of the dam modal properties. Additionally, the influence of operational conditions on modal properties is preliminarily studied, namely the effect of reservoir water level variation on the tracked natural frequencies.

A primary challenge in the design of reliable and long–lasting Structural Health Monitoring (SHM) systems consists in ensuring real–time functionalities through cost–effective solutions. As such, energy–aware architectures demand the joint optimization of data sampling rates, on–board storage requirements, and communication data payloads. These requirements became particularly crucial with the development of mesoscale SHM systems, where the periodic gathering of signals from increasingly denser sensor networks made the data management task a primary issue. In the specific context of vibration–based SHM, where structural responses exhibit peculiar spectral profiles characterized by a sparse frequency content concentrated around the natural frequencies, the Compressive Sensing theory inspired compelling approaches for data collection and gathering to central processing units. The current work combines such advanced sub–Nyquist sampling procedures with a low-cost/low-power miniaturized Smart Sensor Network targeted on the extraction of vibration signals. The network is constituted by several recording nodes equipped with MEMS accelerometers and microcontrollers which are arranged in clusters, and microprocessors-based cluster heads in charge of data decompression and feature extraction for the characterization of the structural integrity.

Real-time structural health monitoring (SHM) acquires countless importance when applied to large-scale civil infrastructures, where the damage should be managed immediately to avoid both economic and human loss. Recent studies in the field of real-time identification of bridges generally assume linear time-varying (LTV) structural models, justified on the grounds that continuously varying traffic load may slightly change the structural behavior over time. Time-varying load also involves non-stationary input excitation, which cannot be modeled as Gaussian white noise, as in the traditional output-only identification methods, and may be characterized by time-varying frequency spectrum which could affect the effectiveness of commonly used identification algorithms. In this paper, the Modal Assurance Distribution (MAD) is employed for the dynamic identification of LTV structures. Based upon the instantaneous operating deflection shapes (ODSs) evaluated through the wavelet packet decomposition, the MAD represents the instantaneous ODS similarity between narrow-band signal components, highlighting the presence of time-varying modal responses. Compared to the most used traditional time-frequency representations (TFRs), representing the distribution of energy through the time-frequency plane, the MAD enables a clearer reading of the modal responses, facilitating their extraction for real-time damage identification. The practical application to a benchmark structure shows the potential of the MAD as a novel TFR which could give rise to a new family of system and damage identification methods.

The passive imaging based on the Green’s functions reconstruction from ambient noise correlation became a promising technique in structural health monitoring. Here, this approach is used to detect and locate linear defects (cracks, holes...) in thin reverberant plates with a small number of sensors. Correlation matrices before and after defect occurrence are estimated from friction noise. Based on a dispersive backpropagation algorithm in a thin plate, the differential matrix of correlations (before and after defect) is used for defect localization. This technique shows satisfactory results for linear defects, but refers to a measurement on a baseline healthy sample, which can be strongly affected by environmental conditions. In this context, an active baseline-free damage localization method that uses a repetitive pump-probe experiment, is proposed. A series of experiments are conducted in a thin aluminum plate using 7 PZTs sensors glued at known positions. One transducer generates a high frequency probe wave with central frequency 20 kHz, while a continuous low frequency pump of 1 Hz is produced by a shaker. A steel ball pressed against the plate to mimic a nonlinear defect is considered. The aim here is to produce solid-solid contact that will be modulated by the pump wave, as would be the case for instance in fatigue cracks. In order to enhance this effect, signals recorded at different times (corresponding to different loading states of the contact) are subtracted and back-propagated to locate the origin of the modulation.

In general, vibration-based condition assessment techniques rely on monitoring the changes in the modal frequencies of a structure, which may not provide an objective diagnosis in case of missing prior information. Instead, tracking and measuring the higher harmonics of the natural frequencies caused by any damage in the dynamic response of the structure can be used for the damage quantification. This type of nonlinear vibration-based monitoring can be realized by performing higher-order spectral (HOS) analysis, which is widely popular for the inspection of machinery systems, whereas limited studies are available regarding the civil engineering structures.As a rare application, herein, a HOS based diagnostic technique is adapted to estimate the deterioration in corroded reinforced concrete members. The proposed diagnostic technique, which utilizes the higher order spectral analysis, is demonstrated on two laboratory-scale RC poles. The poles are first subjected to the impact vibration tests, and then the recorded transient signals are analyzed with the proposed technique to extract the diagnostic index which is a measure for the damage. The preliminary test results manage to discriminate the corroded pole from the intact one, whereas further study is planned to investigate the sensitivity of the diagnostic index with respect to the level of the damage.

Traditional bridge health monitoring methods rely on a large number of sensors, but due to the high cost of sensors and its installation, and time-consuming installation process, those methods are not suitable for the rapid and effective health monitoring of a large number of medium and small-span bridges. However, the method based on vehicle-bridge coupled vibration theory (VBI) to extract structural modal parameters to identify the damage from vehicle body response, such as frequency and mode shape, is often difficult to obtain accurately in practical applications. In order to solve this problem, a simple method is proposed in this paper to extract the transformation features related to structural damage from the acceleration response of passing vehicles to identify the damage. First, the transformation characteristics related to the bridge structure damage are extracted from a large number of vehicle body acceleration responses by combining Mel-frequency cepstral analysis with Teager energy operator in the signal undecoupled state, and the mathematical statistical model of these transformation characteristics is constructed. Then, the structural damage is identified by analyzing and comparing the statistical distribution of these transformation features. It over-comes the deficiency of uncertainty of single test result. The numerical simulation and test results show that this method can effectively identify the structural damage, and well reflect the degree of structural damage by taking the degree of trans-formation characteristic difference as the index.

Carbon-fibre reinforced composite laminates are extensively used in aerospace, automotive, wind energy and marine engineering structures due to their light-weight advantage, high-energy absorption capability, fire resistance, high stiffness-to-weight ratios and construction flexibilities. This work is mainly focused on the analysis of nonlinear ultrasonic guided wave propagation and breathing-debond source localisation in a stiffened composite structure. In the process, the finite element method based 3D numerical simulations has been carried out on a stiffened composite structure using a preassigned network of piezoelectric transducers (PZT). From the analysis of the results, it is observed that the presence of plate-stiffener breathing-type debonds produces higher-harmonics in the registered PZT signals. Based on the identified differential parameters in the higher-harmonics, the breathing-debond source locations are effectively identified by using a fast and efficient baseline-free SHM strategy that uses Fast-Fourier-Transform of the registered sensor signals from the target structure to detect single as well as multiple breathing-debond locations in the stiffened composite structure.

In recent years, the possibility of exploring outer space has captivated interest among various stakeholders around the globe . Be it for space tourism, for unmanned or manned planetary explorations or for the health status assessment of satellites, new developments in asset monitoring systems are envisaged to ensure the robustness and reliability of these missions. Structural Health Monitoring (SHM) is one such technology that can bring us one step closer to this goal by asserting increased levels of safety and breaking down the overall mission costs. By using intelligent sensor networks for diagnosis and prognosis of the asset condition, SHM ensures the integrity of the assets at every step of the mission. However, implementing SHM solutions for space mission have not received much consideration due to complexities that arise from several factors including, environmental conditions, measurement reliability and unavailability of adequate standards. This article dwells deeper into understanding the capabilities of the currently available SHM sensor technologies under the influence of these factors. Following the analysis, remarks are made on promising technologies and the potential they behold in future space missions.

There is a continuing need for reassessments of existing bridges. The validity of reassessment results depends to a large extent on the information used for the calculations. In the meanwhile, the application of non-destructive testing (NDT) methods on concrete is suitable for gathering quantitative information about individual structures that are both relevant and accurate. Such measured information can be explicitly incorporated into probabilistic models used for the bridge reassessment. This way, the level of approximation of the considered model and therewith the validity of the reassessment results can be increased.The purpose of this contribution is to introduce and to apply the developed approach of incorporating non-destructively gathered measurement results (instead of deterministic information and assumptions) into a reassessment model of a typical prestressed concrete road bridge and to outline the advantages. An essential part is the quality evaluation of the non-destructively measured information, that deals primarily with two questions. Could the object or parameter to be obtained reliably detected and if, how accurate are the inspection results achieved? Therefore, the importance of the combination of a probability of detection (POD)-approach and measurement uncertainty calculations is emphasized. With regard to the introduced case-study it is shown, for which structure parameters an assumption deviating from the actual (and measurable) situation has a particularly strong (and possibly arithmetically unfavorable) influence on the structural reliability. Measurements on such parameters are particularly beneficial for a reliable and robust reassessment. In conclusion, the individual reassessment results without consideration and with consideration of evaluated non-destructive inspection results are compared.

In the smelting industry, the knowledge of the integrity of furnaces is a critical information because it allows well-informed decision-making during furnace operation, allows adjustments in furnace efficiency, and to prolong its remaining lifetime. To inspect furnace integrity, multiple methods have been explored. Particular characteristics of the furnace and the process make those solutions in most cases unique. This work explores the Ground Penetrating Radar-GPR method to evaluate the thickness of the refractory wall of an electric furnace during its operation in the production plant of Cerro Matoso SA, which is one of the world’s major producers of ferronickel. Results showed that the GPR method is limited by the metallic shield around the wall of the furnace because of the refraction of the signals, however a reliable measurement can be performed by locating the antenna in direct contact with the furnace’s refractory wall. In the latter configuration, it was possible to find different thicknesses at each measurement point and also to detect the phase boundary limit between the refractory wall and molten material.

Fiber reinforced polymer (FRP) composite systems are widely used to repair structurally deficient constructions thanks to their good corrosion resistance, light weight and high strength. The quality of the FRP-substrate interface bond is a crucial parameter affecting the performance of retrofitted structures.In this study, ultrasonic testing have been used to assess the quality of the bonding. In the case of FRP laminates adhesively bonded to concrete, high scattering attenuation occurs due to the presence of concrete heterogeneities. The substrate material behaves almost like a perfect absorber generating a considerable number of short-spaced echo peaks that make the defect echo not distinguishable. In order to avoid scattering, waves longer than the discontinuity have to be used, but this expedient makes bonding defects undetectable.The presented technique is based on the energy distribution measurement of ultrasonic signals by means of a statistical parameter, named Equivalent Time Length (ETL). A preliminary numerical study involving a 1-D system with a material discontinuity was performed. 2D finite element (FE) analyses were also performed. The experimental study involved laboratory FRP reinforcements bonded to concrete substrates with imposed well-known defects, and seismic retrofitted concrete walls. The experimental and the numerical findings showed that the ETL is sensitive to the presence of bonding defects in the sense that lower values mean higher reflection of wave energy (low quality of bonding) and higher values mean lower reflection and higher penetration through the bonding (good quality of bonding).

Transportation infrastructures can benefit from structural health monitoring in terms of pavement management systems and risk management. Pavement cracks, both visible and concealed, impact road agency budget but unfortunately there is lack of nondestructive methods to assess them. Consequently, the objectives were confined into setting up and improving a nondestructive, acoustic- and sensor-based method. An experimental investigation that was carried out on an asphalt concrete road pavement, aiming at deriving the Structural Health Status (SHS) of road pavements based on their acoustic response to a proper mechanical excitation (acoustic signature). The method was applied using as sensor device a microphone-based electronic system, which is able to gather only the ground-born sounds. Sensor data (i.e., the acoustic responses) were analyzed in three domains of analysis, i.e., the time, the frequency, and the time-frequency domain. Consequently, meaningful features (e.g., energy and entropy of the Continuous Wavelet Coefficients, spectral centroid) were extracted and used to derive the SHS of the road pavement under investigation, which represents a valuable information for different stakeholders (e.g., authorities, drivers, etc.). Results show that by using a small number of meaningful features and by applying a hierarchical clustering procedure, it is possible to recognize the variation over time of the acoustic signature of the infrastructure due to the presence and the propagation of internal and external cracks. Hence, the proposed method can be efficiently used to monitor the SHS of road pavements during their lifetime, and, consequently, to improve pavement management systems and risk management processes.

Chloride ingress in reinforced concrete infrastructures is of crucial importance when considering structural health monitoring applications in marine environments. It indeed leads to the depassivation and corrosion of steel and hence to the degradation of the whole infrastructure. The present study reports the development of an embedded wireless autonomous sensor dedicated to the monitoring of corrosivity of concrete initiated by such ingress. The sensor is based on the ultra high frequency (UHF) radiofrequency identification (RFID) technology. The communication between a commercial RFID reader and a specific optimized embedded antenna in concrete is experimentally demonstrated. In the last part of the study, a resistive corrosion sensor connected to the RFID chip is proposed for the evaluation of chloride ingress.

Detecting damage in the early state is crucial in assessing structural integrity. Most current vibration-based damage detection methods use frequency shifts to assess the damage, observed as a change of the positions on which the peaks in the spectrum are located. However, accurate estimation of the natural frequencies can be challenging due to the raw frequency resolution obtained for short signals. We propose in this paper a signal post-processing algorithm that permits obtaining a spectrum with significantly enhanced resolution, without being necessary to increase the length of the signal. The super-resolution is obtained by overlapping numerous spectra calculated for the signal cropped iteratively. The spectral peaks are distributed in accordance with a pseudosinc function, which is asymmetrical, but the estimated frequencies are close to the real one. By interpolation, we improve the estimate. Moreover, by applying a correction term we find the true frequency. The algorithm is implemented in a Python application that can be linked to any virtual instrument developed in LabVIEW. The algorithm is tested for signals with known frequencies, in the absence and presence of noise and for real-world signals. It provides accurate results that permit observing the occurrence of damage in the very early state.

Monitoring the State-of-Health of vibrating mechanical systems is useful but complex. Besides challenges associated with dynamical behaviors of the systems monitored, supervision tasks are complex with respect data acquisition, feature extraction, and/or statistical modeling for feature classification. Data acquisition strategy addresses sensor types, quantities, and locations. Feature extraction task details the selection and processing of features sensitive to a change/fault present and if required the development of statistical models for change/fault classification. In this contribution, the above-mentioned challenges associated with supervision are explained and detailed using an elastic mechanical structure applying the Probability of Detection method. Previously solved problems relating to simultaneously accessing all mentioned challenges are briefly repeated for understanding. This serves as a prelude to the newly developed data driven noise analysis and improved detection procedure. An experimental example using different real sensor types in combination with mechanical modifications of an elastic beam is presented. The adapted Probability of Detection method helps to determine a suitable feature, sensor type, and position for least change/fault detection. In this article a new data driven noise analysis approach is introduced to ensure optimal sensor-specific flaw size detection. Optimality in this context is related to the selection of the appropriate feature and threshold values for desired false alarms. The noise analysis permits the selection of a decision threshold (threshold beyond which change/fault is considered present in a signal) with the corresponding detectable flaw size and related false alarm rate. Selecting different sensors implies changing the signal distribution character and the decision threshold. This change results in different values and hence can be exploited to decide the optimal sensor. The implemented noise analysis allows a trade-off between flaw size detection and probability of false characterization of faults with 90% detection at 95% reliability level. The novel approach provides a graphical representation that illustrates the diagnostic capabilities of a sensor as its decision threshold is varied.

Computer vision-based techniques for modal analysis and system identification are rapidly becoming of great interest for both academic research and engineering practice in structural engineering. For instance, this is particularly relevant in fields such as bridge or tall building monitoring, where the large size of the structure would require an expensive sensor network, and for the characterisation of very slender, highly-flexible structural components, where physically-attached sensors cannot be deployed without altering the mass and stiffness of the system under investigation. This study concerns the latter case. Here, an algorithm for the full-field, non-contact extraction and processing of useful information from vibrational data is applied. Firstly, video acquisition is used to capture rapidly very spatially- and temporally-dense information regarding the vibrational behaviour of a high-aspect-ratio (HAR) prototype wing, with high image quality and high frame rate. Video processing is then applied to extract displacement time histories from the collected data; in turn, these are used to perform Modal Analysis (MA) and Finite Element Model Updating (FEMU). Results are benchmarked against the ones obtained from a single-point laser Doppler vibrometer (LDV). The study is performed on the beam-like spar of the wing prototype with and without the sensors attached to appreciate the disruptive effects of sensor loading. Promising results were achieved.

Within databases designed for population-based structural health monitoring, diagnostic information can be transferred between structures allowing inferences to be made across them. Information metrics can be developed for this case, where similarities and differences between data collected for monitoring of structures can be evaluated easily without relying on an in-depth, physics-based understanding of the data. By doing so, feature extraction for monitoring will be faster and more informed than through current methods.This paper focusses on adopting the maximum mean discrepancy, to find the distance between probability distributions of tool wear data from tools in a population, in order to find similarly-behaving tools for diagnostic and prognostic purposes.

Experimental and theoretical investigations of Lamb waves excitation and sensing by rectangular piezoelectric transducers perfectly and imperfectly mounted on the surface of a plate are presented in this study. Out-of-plane velocities measurements via laser Doppler vibrometry and voltage signal measurements by the piezoelectric transducers are compared in the context of SHM. The semi-analytical hybrid approach to simulate Lamb waves excitation and sensing by rectangular PWAS in a plate is presented. Reasons for the deviations between the simulated and the measured signals are analyzed. The effects of partial debonding of the piezoelectric transducers are considered and influence of the debonding on sensing abilities is analysed using obtained mathematical model.

This paper reviews recent active and passive results obtained at the Laboratory for Active Materials and Smart Structures (LAMSS) of the University of South Carolina, USA. The active SHM research has focused on detecting various types of composite damage using guided-wave interrogation and sensing. The composite damage considered covered seeded delaminations associated with barely visible impact damage (BVID). The seeded delaminations were created through the insertion of thin Teflon-film patches during composite fabrication. Multiple delaminations at the same x-y location were also studied. [+45/90/−45/0]nS quasi-isotropic layup plates were considered. The Teflon insert specimens were modeled and tested using guided waves transmitted and received with piezoelectric wafer active sensors (PWAS). Scanning laser Doppler vibrometer (SLDV) measurements were also performed.The passive SHM research was focused on recording acoustic emission (AE) wave signals. The major thrust of the AE work focused on detecting AE signal created during fatigue loading of aerospace-grade sheet-metal coupons. Both low-cycle fatigue (LCF) and high-cycle fatigue (HCF) tests were performed. The AE signals were analyzed with the scope of finding specific signatures associated with fatigue crack growth. It was found that not all crack-originating AE signals were associated with fatigue crack growth. In fact, some AE signals were recorded in fatigue-cracked specimens even after the crack growth was stopped by reducing the load to a level below the crack-growth threshold. These AE signals were attributed to clapping and/or rubbing of crack faying surfaces. Similar AE signals were also observed on fatigue-cracked specimens subjected to low-frequency lateral-vibration resonances.

Bearings are the most common mechanical components in machines. Once a bearing fails (or components in it), other adjacent components or the machine itself are effected up to failure. Therefore, bearing health condition is of great interest in practice. Several benchmark datasets are developed to evaluate development in bearings health state (diagnosis) and remaining useful lifetime (prognosis). Among these datasets, Case Western Reserve University (CWRU) dataset is one of the most cited ones used to validate the performance of different diagnostic approaches. Over recent years, a significant amount of research approaches are developed using CWRU data. Most approaches are focused on specific performance parameters like detection rate or accuracy etc. The main problems in connection with CWRU dataset use are: no overview about latest results is available. Furthermore several results published are not complete, for example published accuracies rate without false alarm rates.In this contribution an overview about the development change over the last years, the approaches applied, and specifically the results obtained will be given. Additionally, the new approaches emerging in recent years like deep learning (DL) also in combination with fusion methods and related performance will be given in comparison with conventional machine learning (ML) methods. Special care will be given to the completeness of published results also in combination with shown robustness. As outcome of this contribution the newest and best results are noted, furthermore a recommendation how to complete research work using benchmark dataset will be given. Although most approaches using CWRU dataset as benchmark get high accuracy, for further bearing fault diagnosis research, more and more suitable measures as well as other datasets are needed for increased performance evaluation.

This paper investigates, in detail, the robustness of a previously introduced approach to output-only structural system identification using the random decrement method and unscented Kalman filter (RD-UKF) [1]. Unscented Kalman filters have been widely used for structural system identification and damage detection purposes. These filter’s divergence in estimating the desired states of a structural system with unknown excitations is a well-known weakness, considerably limiting their application. To overcome this difficulty, the current study initially employs the random decrement method to extract a system’s free decaying response from its measured responses. Subsequently, it applies an unscented Kalman filter to the extracted free response in order to estimate the system’s dynamic properties. Our previous study demonstrated this method’s proficiency. The present study conducts further sensitivity analysis to show the RD-UKF method’s robustness vis-à-vis different uncertainties in the process of identification. First, we estimate the stiffness and damping matrices of a three-degrees-of-freedom (DoF) system with three different kinds of excitations. Next, we examine the RD-UKF method’s robustness in 100 experiments (Monte Carlo simulation). Besides, it will be shown that the method is robust in addressing uncertainties related to mass distribution and missing data (sensor malfunction or a loss of communication connectivity) during the modelling and measurement process. The results of the study show that the RD-UKF method is sufficiently robust for all the uncertainties of the system identification process.

To show the effectiveness and sensitivity of SHM systems, proof of concept-experiments have been state of the art for quite some time. For the detailed analysis of SHM systems, frequently numerical modeling is used. Its quality is shown with a comparison to experimental data. Deviations are often called to be caused by the influence of environmental and operational conditions as well as simplifications in the setup of the model.Another aspect, often neglected in guided wave-based SHM, are inaccuracies during production leading to deviations from the nominal structure and SHM system setup. An intelligent structure using active guided waves for SHM consists of the structural component, piezoelectric transducers, data acquisition and data analysis units. Production processes may lead to small deviations in the structure itself, deviations of the bonding process for the transducers as well as geometric inaccuracies, i.e., production-induced variance.For guided wave-based SHM, a case study is presented, which shows the effect of production-induced variance for a simple experimental setup consisting of an aluminum plate with rectangular piezoelectric transducers. The influence highly depends on the type of data acquisition and feature extraction. Transducer signals as well as laser Doppler vibrometer signals and different extracted features are evaluated. Using a very simple artificial change of the structure by adding an acrylic additional mass, it is shown that the effect of this change is approximately the same scale as production-induced variance. It is a significant influence, which needs to be taken into account when aiming for model-based sensitivity analysis.

One main limitation to the implementation of an SHM system on real structures is the difficulty to accurately define the load boundary conditions and the material properties, possibly leading to damage misclassification, especially with heterogeneous materials like composites. In this framework, the inverse Finite Element Method (iFEM) enables to reconstruct the complete displacement, and thus, the strain field starting from discrete strain measures without any a priori knowledge of the loading condition and the material properties. Structural assessment is then performed by computing an anomaly index identifying discrepancies between the strain reconstructed and measured in some testing positions and exploiting the latter for computing the Mahalanobis distance to further highlight discrepancies. Though the anomaly identification framework is general for any arbitrary component geometry and damage type, the procedure is experimentally verified with a CFRP reinforced panel subjected to a compressive load with propagating delamination generated from bullet damage.

This paper presents a novel concept for vibration-based feature extraction to identify damages in cutting discs of Tunnel Boring Machines (TBM). Defect frequencies resulting from repeated interaction of rock and disc defects are analysed. The data set is represented by the normal force acting on the edge of a cutting disc and the rock. Two different methods, the Hilbert transform and the complex demodulation, are used to generate the envelope of the time series, which was used to analyse whether the signal shows a feature representing an existing defect in the frequency domain. For the first proof of concept two numerical models were used - a multi-body system and a peridynamics 3D model simulating time series of normal forces. With both models, the linear motion of the disc on a rock sample with constant velocity was simulated. An experimental setup, mechanically similar to the simulations, was used in two experiments for further comparison. All implemented defects could be detected using vibration data of forces and one of the proposed data analysis techniques.

Stringers are commonly used as longitudinal stiffeners and load carrying elements in most composite aeronautical parts such as the wing skin panels, fuselage or empennage. Each stringer has necessarily two ends -stringer run outs- and are actually critical areas requiring in many cases not just a detailed structural analysis during the design phase but also a follow-up during in-service operation. The paper describes the demonstration and reliability of the online inspection of stringer disbond detection by permanent distributed optical fiber sensors integrated into the adhesive line. A set of test specimens with sensing fibers have been manufactured and tested under a controlled process that have enabled the creation and progressive growth of disbonds. The interrogation of the optical fiber sensors during the tests as well as the correlation with conventional ultrasonic inspection have demonstrated the potential of the technology for in-service inspections. The integration of this optical fiber technology in future aircrafts would enable operators to identify the initiation of damage debonding in these areas, without the expense or time required to take the structure out of service. Ideally, the technology will also determine the damage type, location and size as well as the structure’s health prognosis.

Vulnerability of structures to damage during their service time brings up the necessity of design and implementation of an intelligent procedure to assure the health of the structure. In the sight of this requisite, current work deals with extending the capability of a dual Kalman filter (DKF) state estimation scheme to assist vibration-based health monitoring methods. This is met by estimating the response of the structure for locations at which a sensor cannot be placed. The capability of the DKF method in the estimation of states of a linear system with an unknown input has been presented in various recent works. In this paper, a DKF approach incorporated with a reduced order structural model (in this case an aluminum plate) is utilized to obtain an estimation of applied force and the response of the structure in terms of acceleration, velocity, and displacement. These estimations are based on measured accelerations at a limited number of points on the aluminum plate as well as the state-space model of the dynamic system. Numerical simulations and experimental works are performed to obtain the mentioned datasets. To assess the robustness of the method concerning various conditions, the effect of the frequency, as well as type of the function of the input force on the validity of the method, is presented. Moreover, it is shown to what extent the number of selected modes in model reduction procedure can influence the accuracy of the DKF technique.

In the last decades, noise pollution has become a criticality, especially in residential areas. In more detail, the traffic noise produced by the interaction between tire and road surface (rolling noise) represents one of the main sources of urban noise. Tire characteristics (type/construction, size, belt stiffness, tire damping, non-uniformity, rubber hardness, wear and ageing, retreaded, studded, tread pattern and porosity, and tire cavity content) and road properties (e.g., acoustic absorption, surface texture, porosity, and mechanical impedance) greatly affect rolling noise. In particular, the mechanical impedance of pavement is defined as the ratio of a force applied on a structure to the induced velocity, where these latter are frequency-dependent vectors. Despite efforts and studies, mechanical impedance real effect on rolling noise is still a critical issue. Consequently, this study aims at shedding the light upon the relationship between acoustic response and mechanical impedance of road pavements. By using an impact hammer and a 3D accelerometer, several tests were performed on different types of samples and materials according to the EN 29052-part 1. Results were derived in terms of mechanistic (modulus, damping ratio, dynamic stiffness) and acoustic parameters. Based on results, both changes of the structural health status of pavements and their mechanical impedance affect the acoustic response.

Adhesive bonded joints are more and more applied in modern structures. However, manufacturing defects and particularly harsh operative conditions might cause local de-bonding and catastrophic failures. Structural Health Monitoring and Non-destructive Testing procedures are, then, needed for evaluating the in-service structural integrity of adhesive bonded joints.In this research, an adhesive bonded single lap joint, whose both adherends are manufactured using a carbon fiber reinforced polymer composite, is subjected to constant amplitude fatigue loading. During such a test, the integrity and damage condition of the joint is continuously monitored by acoustic emission, while the test itself is periodically interrupted in order to apply micro-computed tomography to the specimen, with the aim to investigate the real features of the developing fatigue damage.Results show that monitoring by acoustic emission, after suitable elaboration and filtering my means of pattern recognition algorithms, allows identifying and characterizing effectively the development of fatigue damage in adhesive bonded joints.

A case study is presented in which the first steps are made towards the development of a structural health monitoring (SHM) data fusion framework. For this purpose, a composite single-stiffener panel is subjected to compression-compression fatigue loading (R = 10). The carbon-epoxy panel contains an artificial disbond of 30 mm, which was created using a Teflon insert during manufacturing and placed between the skin and the stiffener foot. Under the applied fatigue load, the disbond is expected to grow and its propagation is monitored using two SHM techniques, namely acoustic emission (AE) and Rayleigh-scattering based distributed fiber optic strain sensing. Four AE sensors are placed on the skin, thereby allowing for disbond growth detection and localization. On each stiffener foot, fiber optic sensors are surface-bonded to monitor the growth of the disbond under the applied fatigue loading. The distributed strain measurements are used to localize and monitor the disbond growth. The strength of each technique is utilized by fusing the data from the AE sensors and the fiber optic sensors. In this manner, a data-driven approach is presented in which a data fusion of the different techniques allows for monitoring the damage in the stiffened panel on multiple SHM levels, including disbond growth detection and localization.

Real-time Structural Health Monitoring (SHM) of aeronautical structural components is a technology persistently investigated the last years by researchers and engineers to potentially reduce the cost and/or implementation of scheduled maintenance tasks. To this end, various types of sensors have been proposed to serve this role, e.g. piezoelectric, acoustic emission, and strain sensors. In the present paper, a strain-based SHM methodology is proposed for skin/stringer disbond propagation health monitoring. Fiber-optic strain sensors with engraved Bragg gratings are utilized in order to evaluate the propagation of artificially-induced disbonds at single-stringered composite panels. The specimens are subjected to a block loading compression-compression fatigue spectrum. Longitudinal static strains are periodically acquired during quasi-static loadings every 500 cycles. A Health Indicator (HI), based on strains received from the stringer’s feet, is proposed and utilized to monitor the disbond growth. The evolution of this indicator is experimentally monitored throughout the lifespan of the specimens. The present paper verifies and consolidates via actual fatigue experiments the potential of the proposed static-strain based HI developed from numerical data in our previous work.

Direct or indirect effects provoked by foreign object impacts on aeronautical structures, represent a major concern for military and civil aviation. The problem potentially intensifies with the adoption of composite materials, especially if Barely Visible Impact Damages (BVID) are generated in the structure. The knowledge of whether an impact event has happened and if it has produced a damage, is highly desirable allowing maintenance improvements and the management of risky situations. This can be achieved developing an Impact Monitoring (IM) system, eventually integrable with other monitoring systems for the implementation of a Predictive Maintenance (PM) philosophy.This work deals with the problem of the development of the conceptual scheme of an Impact Monitoring system; it can be considered composed of two parts: (i) a passive impact monitoring part and (ii) an active damage monitoring part. The former part is dedicated to the diagnosis of Low Velocity Impact (LVI) events, meaning the detection, localization and reconstruction of the force exerted on the structure by the foreign object. The latter part is dedicated to the diagnosis of an impact damage, meaning the detection of the damage presence and its qualitative estimation. The IM system is then applied to LVIs on composite structures, typical of aeronautical applications.

A multi-element piezoelectric sensing capability is applied to in situ wavenumber-frequency modal decomposition of acoustic emissions (AE) generated by low-velocity impact on a fibre-reinforced polymer composite panel. The modal signatures of the impact AE are shown to be dominated by a low-frequency antisymmetric (A0) Lamb wave, with little discernible difference in signature observed between non-damaging and damaging impacts. An artificial delamination induced mid-thickness in the same panel under quasi-static loading is also considered, for comparison. For this case, the modal signature was found to be dominated by a symmetric (S0) Lamb wave. The prospects and challenges for characterizing impacts based on modal decomposition of AE are briefly discussed in light of these results.

Dynamic measurements under known moving loads yield a novel procedure for the elastic modulus assessment of existing concrete bridges. The bridge deck is modelled as a single-span, simply supported Euler–Bernoulli beam excited by a travelling force. The elastic modulus assessment derives from an Ordinary Least Square procedure with a Bayesian uncertainty estimation, obtained by approximating the known term of the governing equations due to the travelling force with a square wave signal. The authors validated the procedure on six full-scale concrete girders of the A24 motorway in Italy and compared the results to the values obtained via in situ static load tests and further tests on concrete specimens. The procedure represents a straightforward test devised for supporting the drafting of quality control plans and the prioritization of the interventions.

This paper proposes a free vibration region selection method in acceleration signals for bridge health monitoring systems. Recent development in these systems, vehicle-bridge interaction based approaches are widely used for bridge damage detection including techniques like vibration modal analysis. Theoretically, these analysis techniques require the analysis region that is forced or free vibration for further calculations. With application to damage detection, selection of free vibration region, that is, after the vehicle has passed from bridge is crucial in modal analysis. In conventional systems, free vibration is selected based on amplitude-thresholding techniques on the signal. However, in real system deployments, these threshold based methods are sensitive to vehicle types and bridge structures, which requires manual calibrations during system installation. The performance of these threshold-based methods also degrades when a vehicle is followed by another vehicle. We propose an efficient unsupervised method to select free vibration region after vehicle-bridge interaction, considering the passage of multiple vehicles over a bridge. Our proposed method consist of two parts, first is vehicle detection, which includes non-parametric Bayesian modelling of transformed acceleration responses to automatically detect passing vehicle. Second part is region selection, which includes vehicle’s rear axle detection for determining start of free region boundary. In our real experiments on 2 continuous-span bridge at expressway in Japan, the proposed method precisely selected the free vibration region without using any additional sensor information even in multiple vehicle passage.

Unmanned Aerial Vehicles (UAVs) are increasingly being utilized for the structural health assessment of on and off-shore structures. Visual inspection is the usual methodology for acquiring data from these structures, but there is often a need for contact based structural measurements, for example to assess local thickness on corroding structures. Conventional UAV platform dynamics have not traditionally allowed for such contact measurements. The limited dynamic control afforded by fixed plane rotor UAVs means that forward thrust (to apply contact forces for surface transduction) is only possible by tilting the whole platform, thus taking the UAV into a non-stationary state and limiting positional accuracy. An over-actuated UAV platform (with fully vectored thrust capability) may provide the required contact force for such thickness measurements whilst maintaining stable hovering next to the structure. The authors herein present a contact based ultrasonic thickness measurement technique, whereby an ultrasonic wheel probe deployed from a UAV was used to make single point and scanned measurements across a surface to provide a set of local thickness measurements. A 5 MHz, dry-coupled, dual-element, ultrasonic wheel probe is used to measure the thickness of an aluminum sample plate with thicknesses of 8.2 mm, 4.5 mm and 3.2 mm, and a precision stepped calibration block with size from 31.5 mm to 17.5 mm in steps of 1 mm, then steps of 0.1 mm down to 16.5 mm over a total length of 500 mm. The thickness resolution obtainable from the ultrasonic wheel probe was typically 0.1 mm, and the positional accuracy attained from the over-actuated deployment platform was 16.6 mm when performing single point measurements.

This paper studies a machine learning algorithm for bridge damage detection using the responses measured on a passing vehicle. A finite element (FE) model of vehicle bridge interaction (VBI) is employed for simulating the vehicle responses. Several vehicle passes are simulated over a healthy bridge using random vehicle speeds. An artificial neural network (ANN) is trained using the frequency spectrum of the responses measured on multiple vehicle passes over a healthy bridge where the vehicle speed is available. The ANN can predict the frequency spectrum of any passes using the vehicle speed. The prediction error is then calculated using the differences between the predicated and measured spectrums for each passage. Finally, a damage indicator is defined using the changes in the distribution of the prediction errors versus vehicle speeds. It is shown that the distribution of the prediction errors is low when the bridge condition is healthy. However, in presence of a damage on the bridge, a recognisable change in the distribution will be observed. Several data sets are generated using the healthy and damaged bridges to evaluate the performance of the algorithm in presence of road roughness profile and measurement noise. In addition, the impacts of the training set size and frequency range to the performance of the algorithm are investigated.

Vectorization of the code for simulation of guided wave propagation problems based on the spectral element method is presented. In the code, flat shell spectral elements are utilized for spatial domain representation. The implementation is realised by using Matlab Parallel Computing Toolbox and optimized for Graphics Processing Unit (GPU) computation. In this way, considerable computation speed-up can be achieved in comparison to computation on conventional processors. The implementation includes an interpolation of wave-field on a uniform grid. The method was tested on experimental full wave-field data measured by scanning laser Doppler vibrometer. Good agreement between numerical and experimental results was achieved. Due to relatively short computation time, large data sets can be generated by using the proposed implementation. The large data sets are especially useful for deep neural network training or other soft computing methods opening up new possibilities in health monitoring of metallic and composite structures.

Although smart textile materials have significant importance because of their advanced technology, they haven’t replaced the conventional electronics completely. Nevertheless, these smart textile materials are now developed into the fabrication of in-situ structural health monitoring systems for structures and wearable technologies. The objective of this study was to develop a flexible microscale conductive fiber for in-situ strain monitoring applications by depositing uniform coating film of silver (Ag) nanoparticles on the surface each filament of nylon yarn by electroless plating process without jeopardizing the integrity of each material. The sensitivity of this Nylon/Ag conductive fiber was calculated experimentally, and the gauge factor (GF) was found to be in the range of 21–25 which showed a high sensitivity of the conductive fiber to the applied strain. Then, Nylon/Ag conductive fiber was fractured under tensile loading and a good agreement between the electromechanical response of the conductive fiber was found with repeatability of the results. Then, this Nylon/Ag conductive fiber was inserted in composite specimens at four different directions i.e. 0°, +45°, and 90° respectively in each ply and specimen was machined in a star shape in which each leg represented the direction of the individual position of the Nylon/Ag conductive fiber. The composite star specimen was then subjected to tensile cyclic loading and results showed excellent reproducibility in the mechanical behavior of composite specimens and electrical signals of each conductive fiber although, the conductive fiber in each position showed distinct response because of their respective direction. The increase or decrease in the resistance of the fiber sensor signified the presence of tensile or compressive strain respectively and the intensity of the signal quantified the amount of deformation. The results demonstrated the way to design a cost-effective microscale smart textile for strain monitoring. This Nylon/Ag conductive fiber can then be used in in-situ structural health monitoring even in high strain applications because of their good sensitivity, flexibility, and stability.

Guided wave propagation in composite pipe has multi-modal and dispersive characteristics. In this paper, guided wave propagation in composite pipe is solved by a semi-analytical finite element (SAFE) method. The theoretical framework is formulated using finite element method (FEM) to describe the displacement fields in the waveguide cross-section, while displacement fields in the wave propagation direction are assumed analytical solutions. The dispersive solutions are obtained in terms of phase velocity and group velocity. Knowledge of guided wave propagation properties in composite pipe is beneficial for practical nondestructive testing and structural health monitoring. The SAFE method is validated by comparison with numerical results by ABAQUS. Also, experimental results from group velocity measurement on a composite pipe are presented, showing the feasibility of this SAFE method.

Stress wave propagation (SWP) technique is a simple and cost-effective non-destructive testing technique which can be effectively employed for the health monitoring of timber utility poles. In this paper, Artificial Neural Network (ANN) pattern recognition algorithm is used for the classification of stress wave responses obtained from testing in-service timber poles. Thirty in-service timber poles in Victoria, Australia are tested which belong to different timber species and varying geometric parameters. The tested poles are uprooted and subjected to full scale bending tests in order to determine the failure moments. Health status of each pole is defined based on the ratio between the failure moment and the design moment capacity. 252 stress wave responses are obtained from the field testings by the application of different impacts. An ANN model is developed to classify these signals based on the defined target groups according to the health status. The mobility spectrum of the pole responses in the low frequency region and the pole diameters are selected as the inputs to the ANN model. The performance of the developed ANN model is evaluated by calculating some performance parameters. Further, Support Vector Machine (SVM) and k-nearest neighbors (k-NN) algorithms are also applied to the same data set for classification. The performance of each technique is compared to select the best performing method. Results of this study showed that the developed ANN model outperforms the other techniques for the condition assessment of timber poles using the stress wave propagation technique.

Advanced capabilities of ultrasonic SHM in thin plates, like identification of damage type and compensation for environmental effects, regularly depend on the knowledge and analysis of specific wave propagation modes. Antisymmetric wave propagation modes have been identified as being particularly useful for these purposes because of their relatively slow propagation velocity and associated short wavelength at these frequencies.Recent studies have found that location of shear deforming (d15) piezoelectric actuators and sensors at the neutral axis of a beam or plate-like structure exclusively actuate and sense antisymmetric wave propagation modes, rejecting symmetric modes.This paper presents results from recent investigations into the properties of ultrasonic wave generation and detection using d15 piezoelectric transducers internally embedded within structures, including effects of transducer placement through the structure’s thickness, off the neutral axis.Experimentally validated simulations found that locating a d15 actuator inside a structure, but off of the neutral axis increases deflections indicative of symmetric waves but did not diminish antisymmetric deflections. While the overall trends were similar, the specific results varied with frequency. Simulations and experiments were also performed to investigate the ability of systems employing d15 transducers to detect bond line defects in laminate beams.

The dispersive properties of Lamb waves can be utilised for material characterisation because the frequency-wavenumber-relationship, as well as the group velocity, depend on material parameters. These dependencies make a non-destructive estimation of an elastic constant possible. This preliminary study investigates the sensitivity of dispersion curves caused by a change in elastic constants. The Scaled Boundary Finite Element Method is used to compute special dispersion curves, which show the sensitivity value of the frequency and group velocity as a colour value. This representation allows for easy identification of patterns and local effects. Two sets of dispersion curves are presented, one set for a steel plate and the other set for a plate made of a carbon fibre reinforced polymer. In general, we notice that the sensitivity often increases with the frequency and that higher-order modes seem to be more suitable for material characterisation. Moreover, specific modes respond to material changes while others are relatively unaffected, which must be taken into consideration for material characterisation.

The technological advancements in the sensor design and fabrication process brought about a new generation of smart sensor nodes to be used for Structural Health Monitoring (SHM) purposes, which are concurrently capable of data sensing and processing in situ. This is the case of GWs-based monitoring applications, where the capability of the state-of-the-art transducers to generate custom signals inspired new potentials for acoustic data communications without the need for external cabling. Thus, information about the structural integrity might be transferred between sensor nodes permanently attached to the structure and exchanged across the monitored mechanical waveguide as a numerical damage indicator. Here, a combination of square-wave excitation sequences and frequency-division multiplexing (FDM) is explored for simultaneous communication with multiple nodes. In detail, the problem of selecting the most appropriate carrier frequencies is specifically tackled, by proposing two different strategies for structural aware SHM data communication systems. A Multiple-in Multiple-out (MIMO) miniaturized smart sensor network, consisting of low-power and low-cost sensor nodes, was deployed to prove the effectiveness of the advanced solutions. Transducers were positioned in a spatially distributed and permanently installed network. Cable-free exchange of encoded information across a square metallic plate as well as on a stiffened carbon-fiber reinforced plastics (CFRP) panel is achieved.

Information about exact material properties may be of great importance in many areas where CAD/CAE software is used. It is also a key component of properly operating model-based SHM systems. Unfortunately, composite laminates producers are not providing sufficient and/or precise enough materials data sheets to meet such requirements. This is the reason why material properties identification techniques are attracting considerable interest.This paper presents a new, non-destructive elastic constants identification technique based on Lamb wave phenomenon. Experimental dispersion curves are obtained by 3D Fourier transform of full wavefield time responses registered in a tested sample by scanning laser Doppler vibrometer. Numerical dispersion curves, generated by a semi-analytical element model, are optimized to match experimental dispersion curves. By minimizing the discrepancies between two sets of data, the elastic constants are identified.Two approaches are tested, where the Genetic Algorithm is used to fit dispersion curves in the wavenumber-frequency domain for chosen propagation angles or angular profiles in the wavenumber-angle domain for chosen frequencies. The direct approach was used in which C-tensor components where optimized.

In the last decades, ultrasonic guided waves have proven to be a promising tool for structural health monitoring (SHM). For a number of reasons, narrowband burst signals are widely used to excite structures in order to reduce the impact of multimodal wave propagation and dispersion. This paper addresses a different approach using broadband random excitation signals. While burst signals are advantageous for damage localization and compensation of environmental and operational conditions, the interference of stochastic waves resulting in a complex wavefield could be more sensitive to structural changes, including defects. Based on promising experimental results published recently, potentials and limitations resulting from random excitation are investigated in this paper. Sensor signals are simulated using the time domain spectral element method for a carbon fiber composite plate and twelve piezoelectric transducers. The simulated sensor signals are analyzed using different statistical methods, including the Nullspace-based Fault Detection algorithm known from vibration-based SHM, to compute damage indices for the intact and damaged states of the plate. Moreover, wavefield images computed by the root mean square (RMS) are presented. Detected defects and non-visible damage positions are compared and the results are discussed.

Nowadays the composite material is becoming essential for aerospace structures due to the weight reduction. The lightweight composite structures bring with some problems like accidental damage. The support of Structural Health Monitoring (SHM) systems in overall Structural Integrity (SI) management in those cases is in continuous growth. Airbus Defence and Space is developing a SHM System able to diagnose acci-dental events/damages during aircraft operation to provide additional information for maintenance program application. In this paper it is presented the evaluation of this SHM system for a composite fuselage cockpit structure in the diagnosis of mechanical impacts (events) and resulting damages. The system has been applied to composite complex and large structure using the background knowledge of previous studies on composite reinforced flat panels. An analytic elastic wave propagation 2D/3D model for composite material structures supports the event and damage diagnosis. The results indicate the particularities of SHM diagnosis for events and damages in a real composite structure.

The application of guided waves for detection of partially debonded interfaces or zones of imperfect contact between sub-layers and defects near an edge (surface-breaking defect) in laminate thin-walled composite is considered here theoretically and experimentally. The effect of imperfect contact between sub-layers of a specimen on dispersion and amplitude properties of elastic edge and Lamb waves propagating in a laminated composite structure is analysed. Edge wave interaction with a surface-breaking defect at the edge of the plate has been experimentally and numerically investigated. It is demonstrated that the amplitudes of edge waves caused by reflection and scattering of guided waves are high enough to be employed in NDE and SHM.

Low frequency guided wave inspection using the torsional, T(0,1), mode is routinely used in the petrochemical and other industries for the detection of corrosion patches, the detection threshold being typically around 5% cross section loss, though better sensitivity is obtained on simple pipe configurations not suffering from general corrosion. It has been shown in a blind trial that switching to a permanently installed system operating in SHM mode can improve the sensitivity to about 1% cross section loss and this is very attractive in corrosion monitoring applications. Later work has shown that the detection limit could be reduced to below 1% cross section loss if the compensation for environmental changes, particularly temperature, could be improved. This paper presents a new temperature compensation method involving both overall signal stretching, analogous to the well-known baseline stretch technique, and a further, location-by-location adjustment; this gives significant further improvements in performance. A commercial permanently installed monitoring system giving both local thickness measurements at the transducer location and long-range monitoring for corrosion over 10 s of metres from the transducer position is described. The system enables frequent measurements to be taken, the results being delivered to the operator via a wireless link. The benefits of the frequent readings enabled by the automatic data collection and transmission are discussed. Initial results presented here indicate that this enables defects as small as 0.1% cross section loss to be detected.

Over the last two decades, a wide variety of metal and composite structural health monitoring techniques have been developed. Most of the tests on composite material reported in academia are run on flat and rectangular structures, but real-world parts are more complex than these simple structures. Usually, the physical features of real-world structures are complex. These parts are large, asymmetric, and non-flat structures. They are made out of several attached pieces and might include holes or fixing elements. The wave transmission does not only depend on the usual test conditions (type of sensor, frequency, and waveform) and composite material properties (anisotropic behaviour and high attenuation), but also on the physical features of the structure under test (irregular shape, curvatures, obstacles,…). As a result, the guided waves used for monitoring show a hard to predict behaviour that can be considered chaotic. This paper introduces the preparation and performance of the SHM laboratory tests carried out on an airplane’s leading edge made of composite. During the tests, specific equipment was used for the generation and acquisition of ultrasonic guided waves. The goal of the tests is to adapt the monitoring techniques applied on simple structures to real-world structures. The tests compare guided wave emission techniques with one and many piezoelectric transducers. The research shows the difficulties to monitor real-world specimens and points out the means and set-up to overcome them.

Structural components with waveguide geometry can be probed using guided elastic waves. Analytical solutions are prohibitive in complex geometries, especially in presence of structural discontinuities or defects. The Global-Local (GL) approach provides the solution by splitting the waveguide in “local” and “global” regions. The “local” region contains the part of the structure responsible for the complex scattering of an incident wave. What happens in this region cannot be reproduced analytically. The “global” region is regular and sufficiently far from the scatterer, in order to exploit known analytical wave propagation solutions. The proposed GL approach discretizes the local region by regular finite elements, and utilizes the efficient Semi-Analytical Finite Element (SAFE) method in the global region. Kinematic and mechanical constraints ensure the displacements and stresses continuity at the global-local interface. The evaluation of the energy of reflected and transmitted waves is used to check the before-after scattering energy balance. Numerical results are shown with regard to the specific cases of a composite skin-to-stringer assembly used in modern aircraft construction and a railroad track with a common section. The effects of different damage configurations are analyzed in both cases studying the reproduced scattered spectra related to specific incident waves. The results can be useful to select the best incident mode-frequency range in order to best identify specific defects in these structures.

Masonry buildings are particularly prone to structural pathologies and brittle failures, typically caused by excessive stresses/strains, differential foundation settlements, aging of materials, and natural hazards, such as seismic events. Monitoring the health state of this type of structures during their service life plays a fundamental role in the identification of incipient damages or critical conditions and the optimization of maintenance interventions. In light of that, the Authors recently developed a novel class of sensors, called smart bricks, for structural health monitoring of masonry constructions. These novel sensors consist of fired bricks made by doping fresh clay with conductive stainless steel micro fibers that enhance the piezoresistive capability of the composite. Smart bricks are equipped with copper plate electrodes and can be deployed within masonry constructions, as normal bricks, for monitoring changes in strain, modifications in load paths, and development of damages. This paper deals with an investigation on the effectiveness of smart bricks for the estimation of strain under increasing compression loads, in particular when sensors are deployed within a typical structural setting. With this aim, smart bricks’ strain measurements are compared with those of traditional strain gauges applied onto each tested sample. Furthermore, numerical simulations are carried out for reconstructing strain field maps of a masonry wall subjected to eccentric axial compression tests, by exploiting strain measurements outputted by smart bricks embedded within the load-bearing structure. Overall, results have confirmed the effectiveness of the novel sensors in strain measurements under increasing compression states.

Graphite, an allotropic form of carbon with high electrical conductivity, in the range of $$2\times 10^5$$ 2 × 10 5 to $$3\times 10^5$$ 3 × 10 5 S $$\cdot $$ · m $$^{-1}$$ - 1 , is a more affordable alternative to carbon nanotube nanoinclusions in the fabrication of conductive multifunctional cement-based materials, such as smart concretes, used in strain monitoring. The enhancement of piezo-resistivity is one possible functionality of graphite inclusions that has not yet been explored in depth in the literature. In order to bridge this gap, the authors investigate the piezo-resistive strain-sensing response of graphite-cement composite materials. The composite samples were prepared with different amounts of graphite inclusions and experimentally subjected to electro-mechanical tests. The study discusses the improvements in conductivity, strain sensitivity, and signal linearity achieved with graphite inclusion. Because of the easier dispersion and lower cost of graphite particles, the investigated composites can be scaled up to large concrete elements, useful to create smart road pavements enabling intelligent weigh-in-motion sensing as intended in this research. Results demonstrate that multifunctional self-sensing composite pavements doped with graphite are capable of strain sensing with high linearity and sensitivity. In particular, it was found that a 20% graphite-to-cement ratio exhibited the best properties in terms of gauge factor, drift, reproducibility, and linearity.

Different strategies may be adopted to avoid ice formation, such as power-consuming active systems and passive coatings. Several categories of surface treatments with superhydrophobic/icephobic behavior have been developed in the last decade. The goal of the coating application is to repel water droplets, delay ice nucleation and significantly reduce ice adhesion. However, surface treatments alone are not sufficient to guarantee icing protection in a wide range of humidity and temperature conditions. They should be considered as a complementary solution to traditional protection active systems to reduce their power consumption and environmental impact. This study concerns the early stage of development about a hybrid system, characterized by a low energy consumption and based on both passive techniques, the superhydrophobic/icephobic coating, and an active one, ultrasound, to remove ice build-ups from treated surfaces. Preliminary tests are conducted on a coated metal plate and the results coming from the investigation are presented.

The workload evaluation is of great importance for human error avoidance training, particularly in the use of complex systems that requires different and concurrent activities. The excessive workload harms human performance even with adverse outcomes. In the aviation field, certain flight maneuvers, such as take-off and landing, are characterized by great attention and workload demand to the pilot. Thus, a system capable of measuring pilots’ workload levels during flight could be beneficial to increase pilots’ performance. This work aims to study the initial feasibility of a device called Cockpit Pilot Warning System that monitors the pilot workload level during flight. With this aim, an experimental campaign using a Level-D business aircraft flight simulator is conducted. Two sensors are used to acquire biological signals: a thermographic camera is used to obtain pilots’ Face Temperature Variation (FTV) while a Heart sensor is used to acquire their Heart Rate (HR). The nervous system modifies FTV and HR in response to stressing or high workload events and can thus be used to monitor pilots’ workload that affects their performance. The workload measurement with the thermographic camera is an indirect measurement, particularly indicated in aviation, since it is contactless. It does not interfere with the concentration and leaves pilots’ freedom of movement, thus not affecting their working functions.

We present a numerical study of highly nonlinear solitary wave interaction with adjacent bone microstructures towards the development of a novel diagnostic scheme for site-specific bone quality assessment. High-resolution finite-element models of the trabecular bone microstructures in the femoral head are generated using a topology optimization-based bone microstructure reconstruction scheme. Using the finite-element models, a hybrid finite-element/discrete-element method is developed to examine the characteristic features of the reflected highly nonlinear solitary waves in a granular chain with adjacent damaged bone microstructure models for the prediction of partial fracture due to the development of osteoporosis.

During the last decades, the increased availability of continuously monitored structures has attracted the attention of the Structural Health Monitoring (SHM) community towards the development of automated techniques capable of continuously providing useful information to timely assess the health state of a structure. Over the years, especially the SHM procedures based on Operational Modal Analysis (OMA) have proved to be effective tools for the continuous assessment of large infrastructures and ancient constructions.Within this context, the paper presents the development and validation of a vibration-based novelty detection strategy based on the application of pattern recognition models to the identified natural frequencies, with the latter being used as damage-sensitive features. The methodology presented herein is based on the forming of a decision boundary through the use of a Support Vector Machine (SVM) model: hence, SVM is exploited to separate data into two classes, associated to two different structural conditions (i.e., undamaged and damaged), without any prior assumptions on the propriety of the data.The robustness of the developed approach is exemplified using the natural frequencies automatically identified during the continuous monitoring of a historic masonry tower. Due to the occurrence of a far-field earthquake, the tower underwent structural damage demonstrated by a slight permanent variation in the natural frequencies. The obtained results highlight the capability of the proposed approach to automatically reveal slight damages in structures without any user interaction and without performing any removal of environmental and operational effects.

Masonry arch bridges in India indicate the heritage value of the nation. Most of these bridges had been in service for hundreds of years and yet being serviceable even today for transportation purposes indicates the robustness of the design and construction methodology. But, some of these bridges are abandoned due to its deterioration and absence of knowledge to retrofit these structures. Lack of proper maintenance and retrofitting could eventually damage the structural integrity as these structures are old enough to deteriorate and are prone to repeated weathering and unforeseen natural calamities such as earthquakes, floods, etc. In this study, a very old masonry arch bridge ‘Puranapul’ bridge inaugurated in the year 1578 across the river Musi in Hyderabad is considered for investigation of its health through basic visual inspection and non-destructive testing. Furthermore, the same is numerically modeled using the available finite element analysis software ANSYS in three dimensions for assessing the basic mode shapes of the structure and its behavior in different loading conditions.

This work reports the development of two novel software solutions, named MOVA and MOSS, for the autonomous management of integrated monitoring systems. MOVA and MOSS, Italian acronyms of “MOnitoraggio delle Vibrazioni Ambientali’’ and “MOnitoraggio dello Stato di Salute’’, respectively, offer online operational modal analysis (OMA), pattern recognition, feature extraction through data fusion, and automated novelty detection capabilities. The functionalities of the developed codes are illustrated through the application case study of the monumental Consoli Palace in Gubbio, Italy. The palace was uninterruptedly monitored since July 2017 until August 2019 with a mixed static/dynamic/environmental monitoring system, and the SHM system has been recently upgraded in July 2020 with a considerable increase of the number of sensors deployed in the palace.

One of the most remarkable structural elements characterizing the Milan Cathedral is its main spire, reaching the height of about 108 m and supporting the statue of the Virgin Mary. The Main Spire, built in Candoglia marble and completed in 1762, is about 40 m high and stands on the tiburio of the cathedral (i.e., the prismatic structure with octagonal base built around the main dome). The spire consists of a central column which is connected through a spiral staircase to 8 perimeter columns, with each column being stiffened by a flying buttress. The structural arrangement is completed by (i) metallic clamps and dowels, connecting the marble blocks, and (ii) metallic rods, connecting the perimeter columns to the central core.A large monitoring system has been recently designed and installed in the Milan Cathedral, aimed at enhancing the knowledge and assisting the condition-based structural maintenance of the historic building. The new monitoring system includes temperature sensors and seismometers (electro-dynamic velocity sensors) at 3 levels of the Main Spire as well as a weather station at the top of the same spire.After a concise historic background on the Main Spire of the Milan Cathedral and the description of the sensing devices installed in this sub-structure, the paper focuses on the dynamic characteristics of the spire and their evolution during the first year of monitoring.

One of the main problems concerning the field of Structural Health Monitoring (SHM) is the unavailability of data from different structural conditions. This is especially true for civil structures, where the collection of data from different damage states is often infeasible or economically inconvenient, particularly when dealing with architectural heritage structures. In the last few years, this issue has been addressed by using a Transfer Learning (TL) strategy, which allows one to transfer the knowledge obtained from systems where several conditions are known, to different (but related) systems, for which limited data are available. In particular, recent studies have demonstrated the effectiveness of Domain Adaptation techniques, a subcategory of transfer learning, for both homogeneous and heterogeneous populations. By transferring knowledge, these methods improve the classification of different structural conditions. This paper shows results from the application of a domain adaptation technique - Transfer Component Analysis (TCA) - between the monitoring data of a structure and those of its Finite Element Model (FEM). The FEM is a precious resource for this purpose as it allows one to simulate manifold system conditions and obtain the related data without affecting the real structure. The case study considered here is the Sanctuary of Vicoforte, a monumental building from the 17th century located in Italy, equipped with a permanent static and dynamic monitoring system. The research has shown promising results in distinguishing, via a Relevance Vector Machine (RVM) classification, different environmental conditions affecting the building.

In the context of relevant seismic events that recently hit Italy, like L’Aquila 2009, Emilia 2012 and the Central Italy seismic sequence 2016, there has been an increasing scientific interest on Cultural Heritage buildings’ assessment, with key concepts like the preventive conservation and condition-based maintenance. In this regards, low-cost and non-destructive vibration-based Structural Health Monitoring systems can provide very useful information on the global dynamic and structural behavior, enabling detection of small structural damages that occurred during earthquakes, even far-field ones of moderate intensity. This paper presents a methodology aimed at addressing the rapid post-earthquake damage localization and quantification tasks in historic masonry structures, based on Operational Modal Analysis (OMA) and non-linear Incremental Dynamic Analysis (IDA). While the OMA-based damage detection approach was already presented in previous work by the authors, this paper focuses on the IDA-based part of the methodology. Validation is presented through application to a medieval masonry structure: the bell tower of the Basilica of San Pietro located in Perugia, Italy. It is a monumental Cultural Heritage (CH) building permanently monitored since December 2014. The numerical FEM model together with experimental continuous vibration data and those recorded during the 2016 Central Italy seismic events are successfully exploited for earthquake damage localization and quantification.