Dynamics of Civil Structures, Volume 2

Within the project Dyna-TTB, vibrational tests have been conducted on eight high-rise timber buildings, in Europe. A main objective of the project is to gain knowledge about damping in timber buildings to assist in predicting the accelerations, at the top of a building, due to wind-induced vibrations.One of the buildings is Eken (the oak) in Mariestad in Sweden. That building is seven stories tall, thus questionable as a tall timber building, yet an interesting test object. The building structure is made up of glue laminated timber beams and columns stabilized with glulam trusses.Forced vibration were conducted on Eken with the aim to estimate the building’s dynamic properties from test data. Estimates of the eigenfrequencies, mode shapes, and their scalings are useful both in the calculations of wind-induced vibrations and to calibrate numerical models. However, the most important outcome is estimates of the modal damping values. The damping impacts the acceleration and thus the serviceability of the building, and at the same time, it is very hard to model damping. So, during the design phase, one must rely on previous test data (of which very few exist for taller timber buildings) and rule of thumbs. It is therefore important to gain knowledge about the damping for timber buildings in order to enable good designs of future and taller timber buildings.

The application of digital twin technology offers the potential to significantly enhance the reliability and performance of structural health monitoring techniques by providing detailed information on, and measurements of, the as-built properties and performance of structures. In addition, a digital twin provides greater support for the application of probabilistic methods for prognostication and risk assessment following model updating and diagnostics provided by structural health monitoring. To date, digital twins have been predominantly applied in the advanced manufacturing space, although the increased use of modular and prefabricated construction has driven interest in the extension of digital twin technology to both civil and nuclear structures. This chapter details the implementation of a structural digital twin and investigates its influence on the performance of a Bayesian vibration-based structural health monitoring approach. In this chapter, a digital twin is developed throughout the construction of a model-scale steel-plate composite modular wall in a laboratory environment. Throughout the design, fabrication, and erection of the specimen, design calculations, numerical models, LiDAR point clouds, physical measurements of geometric features, and quality control inspection data are integrated into a digital twin framework to develop an as-built representation of the structural geometry, material properties, and condition throughout the stages of construction. The modular nature of the construction results in different effective section properties for wall modules as the early-age elastic properties of the structural concrete are developed. This time dependency of the section properties is leveraged to produce a series of datasets where structural parameters in the model evolve due to aging. Probabilistic model updating using modal parameters obtained from experimental modal analyses is used for structural identification of the as-built model. Static loading of the specimen with supplemental instrumentation is used to validate the parameter estimates. The performance of the vibration-based structural identification is evaluated both with and without the as-built information produced by the structural digital twin framework.

The state-of-the-art OMA algorithms have been used to identify the dynamic parameters from output-only vibration data acquired in a testing campaign carried out on a remarkable 368 m high steel structure, namely, the Riga Television and Radio transmission tower. The structure is unique both in terms of the structural system and of societal relevance since it is a historical monument and a landmark for Riga, one of the capitals of the Baltic States. Two independent acquisition systems were used to measure the vibration responses of the tower at a total of 48 DOFs along its height.Each acquisition system is constituted of two 3D vibration sensors. One of the acquisition systems is used as a reference, and the other one is a moving system. The latter was relocated to different stories and antenna, and the former remained at the same (reference) storey throughout the test. Because the two different systems were not synchronized, advanced post-processing techniques were employed to synchronize the different datasets and subsequently identify the global modal properties of the tower.

The resonance of railway bridges that occurs under high-speed trains passage is an important academic and practical issue. The bridge design code in Europe (Eurocode) sets an upper limit of 5 m/s2 on the bridge deck acceleration for a slab type track. However, the performance of high-speed railway bridges regarding train-induced deck acceleration specified in Eurocode has not been sufficiently verified. In this study, a slab track type PC box girder bridge in Japanese high-speed railway is targeted, and multipoint acceleration measurement is conducted. Based on the measurement results, the global and deck local modal characteristics are identified with ERA method, and the maximum deck acceleration is evaluated. As a result, the maximum deck acceleration of the bridge is much lower than the Eurocode limit value of 5 m/s2 even under the resonance condition. Unlike previous reports for European composite box girder bridges, the main deck vibration modes of the bridge are over 40 Hz, thus the deck local vibration did not affect the evaluation of deck acceleration.

Numerically simulating vehicle–bridge interaction (VBI) models within finite-element models (FEMs) has been a topic of interest over the last two decades. Its applications have been well-established in the structural health monitoring community for extracting the dynamic properties of bridges using instrumented vehicles. Often times, analytically generated surface profiles adopted from renowned standards such as the ISO-8608 are used to simulate road surface conditions and approach a more realistic model. However, previous analytical studies have indicated that current methodologies for modeling the effect of road surface roughness on the vehicle response tend to exaggerate the dynamic response of a vehicle and overshadow bridge frequencies in the vehicle response. To alleviate this issue, several studies have recommended relatively low-amplification roughness factors ( G d $$G_d$$ ) or have used a moving average filter (MAF) to de-noise analytically generated surface profiles prior to prescribing it into the FEM, but to the authors’ knowledge no experimental investigation has been conducted to validate these recommendations. In this chapter, a full-scale road test is conducted using a passenger vehicle instrumented with accelerometers. A vehicle model is developed using the bicycle concept to approximate the dynamic response of the tested vehicle while including road surface roughness effects. The results are then compared to observe whether the vehicle model contains higher acceleration amplitude values than those of the experiment while using ISO-8608’s G d $$G_d$$ factors for the appropriate road class observed in the test. It is concluded that low G d $$G_d$$ factors, which are commonly used in VBI studies, seem to underestimate the surface roughness effects and thus produce an unrealistic VBI model. Moreover, the use of higher G d $$G_d$$ factors without the implementation of a MAF tends to significantly exaggerate the vehicle’s response. The implementation of a MAF was successful in attenuating high frequency noise while also reducing the acceleration amplitude of the vehicle, but the G d $$G_d$$ value still needed to be relatively low to represent the data from the road testing. Finally, the study concludes with recommendations on how to improve the current approach for modeling road surface roughness in VBI problems and suggestions for future work.For feedback and support, please see the https://github.com/omarabuodeh1994/surface_roughness.git . No promises are made in terms of timely help from the author.

This chapter presents a very efficient method to perform the spectral analysis of structures subjected to both wind and wave loadings at second order. It hinges on a framework called the Multiple Timescale Spectral Analysis, which generalizes the Background/Resonant decomposition. It offers therefore a quick way to estimate the variances and the covariances of modal responses, with a small but controllable discrepancy. This method also helps to better understand the behavior of the structure at stake as it shows how the statistics of the modal responses are distributed between the background, the resonant and the inertial regimes.

This chapter describes how the modal properties of bridges can be identified by ambient vibration tests and modal analysis. For this purpose, a typical steel arch highway bridge in the Province of British Columbia, Canada, was selected and subjected to a series of ambient vibration measurements. Modal response analysis was then performed to identify the dynamic properties of the structure, including predominant natural frequencies and the corresponding mode shapes to support seismic assessment and upgrading of the bridge. The bridge was constructed in 1960 and consists of a 72.3 m long single span. The superstructure consists of three tied arches, with a steel ladder deck system comprised of transverse floor beams (located at the arch hanger points) and longitudinal stringers, supporting a cast-in-place concrete deck.The ambient vibration testing method was implemented using sophisticated methods of modal analysis. Vibration tests were conducted at the bridge in order to determine the dynamic modal properties (modal frequencies and mode shapes) of the bridge. The testing program consisted of static tests, speed test, and ambient vibration tests including multiple measurement setups. Tromino® velocity/acceleration wireless sensors were used for these measurements which were placed on predetermined locations. The computer program ARTeMIS was used to perform the system identification of the structure. The software allows to develop a 3D model of the structure and test points; the resulting mode shapes are displayed using this geometry. Two different techniques were used for modal identification: Enhanced Frequency Domain Decomposition (EFDD), and Stochastic Subspace Identification (SSI). These two modal identification techniques were used to cross-validate the results. The joint analysis of the signals measured in various strategic points of the structure made it possible to identify the modal configurations and the corresponding natural frequencies.As result, a total of 11 modes were identified in the 0–20 Hz range. Modal frequencies, modal damping, and mode shape were identified for each of the 11 modes. The modes associated with torsional response of the deck showed that the bridge supports are flexible. Video-motion analysis of videos obtained during the load tests were also used to estimate the vertical deformation of the arch near its center. The results were consistent with those obtained from a topographical survey during the load tests.

This chapter aims to characterize the structural vibrations induced by footsteps to segment a sequence of gait patterns into critical gait phases (including stance phase and swing phase) for in-home gait health monitoring across various floor structures. Gait cycle segmentation is an essential step in quantitative gait assessments for early diagnosis and progressive tracking of neuroskeletal and neuromuscular disorders. Especially, in-home monitoring of peoples’ gait health is beneficial for low-income families and those who have limited access to medical services. Existing studies have adopted cameras, wearable devices, and force plates/pressure mats to segment gait cycles, but they have operational requirements such as direct line-of-sight, carrying devices, and dense deployment, which are not practical for continuous monitoring at an individual’s home. In this chapter, we develop a gait cycle segmentation framework through footstep-induced structural vibrations. The primary research challenges are the complex interplay of the: (1) gait phases and (2) structural properties with the vibration signals. First, gait involves a continuous sequence of multiple types of motions, making it challenging to separate them. Second, people’s living spaces have distinct types of floor structures, leading to difficulty of adapting our framework to multiple structure types. To address the first challenge, we leverage the main insight that human motions at the onset of each gait phase (e.g., heel strike and toe-off) involve unique types of excitation force (e.g., impulsive vs. friction forces). These forces incur peaks at distinct frequency ranges in the responses of the structure. Therefore, we separate gait phases by analyzing the structural responses over various frequency ranges. Second, to make our framework structure-agnostic, we formulate the structural influence on the vibration signals and extract structure-dependent features to represent such influence. Overall, our framework first identifies the structure-dependent dominant frequency ranges for each structure through a time–frequency-domain analysis and extracts vibration signals within these frequency ranges. It then detects time-domain peaks within each structure-dependent frequency range to identify the onset of gait phases. We evaluate our method on two different structures in a real-world setting and achieved consistent results with only a 5% average error in detecting various gait phases.

In recent years, tracking gait parameters of older adults in a non-intrusive way is attacking a lot of attention in the community due to their close correlation with health status. This chapter attempts to investigate the feasibility of using seismic accelerometers to detect human footsteps from floor vibration measurements for cadence estimation. Algorithms building upon high energy peaks in the time-domain acceleration signals were proposed to estimate cadence, and the proposed technique is called the peak acceleration for cadence estimations (PACE). A specialized cadence filter was also developed and implemented to improve the overall cadence estimation from three-step instant cadence estimations. To validate the proposed algorithms, a hallway with four accelerometers placed behind a wall was used as a testbed to measure the structural response generated by the footsteps. A subject walking at three different paces with wireless APDM sensors, the gold standard in health science to measure gait parameters, was used to generate ground-truth data for validation. The results showed that proposed algorithms successfully detect human footsteps with a high degree of accuracy and that the estimated cadence was highly correlated with the cadence measured by the APDM sensors. The specialized filter was able to clean out highly unlikely instant-cadence estimations that substantially improved the overall cadence estimation.

Vibration serviceability criteria for civil structures follow a three-step framework, namely the excitation source, the path and the receiver. The first step, which is also the focus of this study, deals with the characterization of human-induced loads. However, the design models reported in the current guidance and codes are very often overly conservative and cannot adequately represent the real nature of crowd excitation. In this work, we present a computer vision technique, based on the use of Digital Image Correlation (DIC), as a solution to this problem. In addition to a cheap and an easy to install set up, the system can provide a comprehensive assessment of the coordinate motion induced by occupying crowds of various sizes. To demonstrate the efficacy of the proposed method, the measured DIC data are compared to those coming from the accelerometers installed on multiple subjects while performing jumping activities on a real grandstand. Then, the vision-based approach is used to study and to quantify the level of synchronization among the individuals for a range of songs and metronome beats. Results demonstrate that the DIC technique achieves similar performance as the inertial sensors but overcomes some practical limitations related to these traditional systems.

This chapter summarizes the work investigating the capability of interfacing human control of vibrations with a new interface between vibrations and humans. The area of interest is structural dynamics, especially in experimentation and structural health monitoring (SHM) methods. The researchers developed a new interface to enable that the inspector maintain awareness of test structures while observing sensor data and inputting control on real time. Sensor data does not collect other contextual information of critical value to the operator during the experiment, therefore the human needs to observe the experiment in person to better assess the actual behavior of the structure as well as to identify the quality of the test. The researchers developed a new augmented reality (AR) interface in the laboratory that can also be utilized in the field that shares the information of dynamics with the operators on real time and further investigated the effect of this interface in the controlling abilities of humans with and without this interface. The results include time domain and frequency domain comparisons, as well as future laboratory applications including sensor deployment and augmented robotic control.

The damage detection capabilities of sensor setups are essential for any structural health monitoring (SHM) system. In this chapter, the performance of different subsets of sensor configurations selected from a set of 40 accelerometers is evaluated using metrics such as misclassification rate, false positive (FP), and false negative (FN) indications of damage. The subsets of sensor configurations are based on experimental data from a benchmark study that involved capturing the dynamic behavior of a full-scale steel bridge in undamaged and damaged conditions of the bridge. Several iterations with new subsets of decreasing size are generated by the elimination of random sensors. These subsets are then tested using Mahalanobis squared distance (MSD) as the novelty detection algorithm. Additionally, a manual selection of subsets is evaluated, where the sensors located farthest from the damages are eliminated. The results highlight the advantages of a dense sensor network and indicate a complex mechanism behind the damage detection capabilities of sensor networks with a clear trend of inverse proportionality between the sensor set size and FN indications of damage.

First passage time (FPT) maps and histograms of FPT are known to be capable of detecting slight deliberately operated structural changes in a steel strip. In this chapter, the question of the localization of the damage is tackled. It is studied by comparing the predictions of a 2-D updated finite-element model and the observed experimental results. We numerically simulated various damage scenarios by modifying the damage location until a good match of FPT histograms was found with the experimental investigations of virtually damaged structures. The detection and localization process was proven to be successful, and we attribute it to the large sensitivity of the FPT maps and histograms to slight changes in the model.

This chapter presents results from measurements on the Rykkjem ferry dock in Møre and Romsdal county in Norway. The study was conducted as a part of a bigger project that aims to examine the resilience of critical coastal infrastructure when exposed to extreme events. The work presented herein includes a qualitative assessment of recorded time series and describes results from the modal analysis. It is further discussed how long-term monitoring of the existing ferry docks can improve understanding of underlying dynamics, ferry-induced loads and ongoing deterioration processes for ferry dock bridges and as a result of that, improve design safety and the economic aspects when it comes to maintaining this critical part of infrastructure.

Track segments welded together form a continuous welded rail (CWR). Although CWRs are widely used, they are prone to buckling in warm seasons. To prevent rail buckles, accurate estimates of the axial stress and rail neutral temperature (i.e., the temperature at which the axial stress is zero) are needed. This study proposes a noninvasive method to determine the axial load in CWRs using numeric models and field test data. A general finite element model under varying boundary conditions and axial stresses was formulated. The model was then validated experimentally by testing a real track in the field. During the experiment, the rail was subjected to the impact of an instrumented hammer, and the triggered vibrations were recorded with conventional accelerometers. These vibration frequencies were compared with numerical predictions to estimate the neutral temperature of the rail using a machine learning algorithm. The estimates showed good agreement with the measurements conducted by a third independent party that used a very cumbersome approach based on strain gages.

The prospect of informed and optimal decision-making regarding the operation and maintenance (O&M) of structures provides impetus to the development of structural health monitoring (SHM) systems. A probabilistic risk-based framework for decision-making has already been proposed. The framework comprises four key submodels: the utility model, the failure-modes model, the statistical classifier, and the transition model. The cost model consists of utility functions that specify the costs of actions and structural failures. The failure-modes model defines the failure modes of a structure as combinations of component and substructure failures via fault trees. The statistical classifier and transition model are models that predict the current and future health-states of a structure, respectively. Within the data-driven statistical pattern recognition (SPR) approach to SHM, these predictive models are determined using machine learning techniques. However, in order to learn these models, measured data from the structure of interest are required. Unfortunately, these data are seldom available across the range of environmental and operational conditions necessary to ensure good generalisation of the model.Recently, technologies have been developed that overcome this challenge, by extending SHM to populations of structures, such that valuable knowledge may be transferred between instances of structures that are sufficiently similar. This new approach is termed population-based structural heath monitoring (PBSHM).The current paper presents a formal representation of populations of structures, such that risk-based decision processes may be specified within them. The population-based representation is an extension to the hierarchical representation of a structure used within the probabilistic risk-based decision framework to define fault trees. The result is a series, consisting of systems of systems ranging from the individual component level up to an inventory of heterogeneous populations. The current paper considers an inventory of wind farms as a motivating example and highlights the inferences and decisions that can be made within the hierarchical representation.

Corrosion of internal reinforcements represents a serious issue in prestressed concrete structures, as it progressively decreases the load bearing capacity of components, hence contributing to reduce the service life of the whole structure and potentially inducing unexpected and uncontrolled cracking phenomena. A proper assessment of the health status of a structural element is the key priority of any Structural Health Monitoring (SHM) system. As SHM systems are finally getting the credits they deserve, given their relevance in keeping structures monitored and hence in contributing to people life safety, the focus has shifted towards the development of automated approaches to promptly detect incipient damages potentially jeopardizing the structural integrity of the target system.Within this context, this chapter, which describes a part of a wider research program targeted to the development of robust strategies for SHM of civil structures, aims at discussing a preliminary study carried out to identify the potential correlation between corrosion levels in pretensioned concrete elements and their dynamic behavior. Several pretensioned concrete beams of the same geometry (cross section 200 × 300 mm; total length 3700 mm) have been subjected to an artificial corrosion process that was induced through electrolytic cells by dipping the beams within a 3% saline solution, up to half of the height of the cross section. Four-point bending tests were performed up to failure. Dynamic impact tests were also performed to identify any eventual variation in terms of dynamic behavior of the beams. The chapter shows that increased corrosion induces wider changes in the dynamic behavior of the structural elements.

In several existing structures and infrastructure systems, the loads and responses are hard to be accurately measured especially when the structural systems are partly or wholly inaccessible or located in harsh conditions (e.g., offshore structures). The chapter proposes a frequency-domain technique to estimate the force and response of vibrating structures using limited response signals. The frequency response function of a structure can be reconstructed either based on an updated finite element model’s properties or identified modal parameters by a data-driven approach. Next, the components of the frequency response function corresponding to a few degrees of freedom, whose response measurements are available, can be selected to create the admittance function and, consequently, to estimate the applied loads and predict the response of any degree of freedom. The technique is implemented on the finite element model of an offshore platform exposed to wave loads. The estimated wave loads and response of the submerged elements indicate the proposed technique’s competence in simplicity and efficiency compared with other methods.

Population-based Structural Health Monitoring (PBSHM) aims to gain additional insights on the health of a structure when using data available across a population of similar structures, as compared to the insight available when using only data from a single structure. Before knowledge can be transferred across structures, the similarity between structures (or substructures) within the population must be established. The first paper in this series explored the use of Graph Neural Networks (GNNs), to compute similarity measures via an Irreducible Element (IE) model representation of structures stored within the PBSHM database. While the work explored so far uses a pure topological matching to determine the similarity, this chapter builds upon the aforementioned research and explores the viability of matching using the recently introduced Canonical Form (CF).

The monitoring of structural displacements is crucial because displacements can reveal critical information about the health of civil structures. Despite this, accurate measurements of structural displacements remain a difficult task. The fusion of a vision camera and an accelerometer has previously been explored to estimate structural displacements, but only in-plane displacements can be estimated. This chapter describes a three-dimensional structural displacement estimation method that fuses measurements from a consumer-grade camera and a triaxial accelerometer mounted on a target structure. An accelerometer-aided computer vision algorithm and an adaptive multirate Kalman filter are integrated to efficiently estimate high-sampling three-dimensional displacements from low-sampling vision measurements and high-sampling acceleration measurements. All parameters associated with the computer vision algorithm are automatically calibrated without any prior knowledge or ad-hoc thresholding. Experimental validation of the proposed method is performed on a four-story building model under varying excitations. Displacements were accurately estimated with a root mean square error of less than 2 mm.

This work is concerned with the acoustic emission of helical guided waves generated during corrosion in cylindrical steel structures. The energy that is emitted during the corrosion progress in the steel is collected by means of acoustic emission hits and a correlation is established between the amplitude of these hits and the corrosion through a b-value analysis. The methodology was validated through an accelerated corrosion experiment, and a linear relationship between the b-value and the corrosion evolution was observed.

Structural damage detection and prediction under various type of demands has been a significant area of research over the past few decades. Applications of machine (ML) and deep learning (DL) to this topic have provided essential insight into damage detection and prediction in many engineering disciplines, including structural health monitoring (SHM). This study mainly focuses on implementation of DL-based algorithms to help identify and classify imposed structural damage to a full-scale bridge deck mock-up whose structural response was monitored under varying loading conditions and damage levels. Strain time-history data, which represents a healthy, undamaged, state and three incremental damage states, was collected from the field experiments. Supervised ML was used to construct a dedicated two-dimensional (2D) convolutional neural network (CNN), which can extract and classify features, using sensor readings as input “images.” The proposed 2D-CNN model utilized four fully connected, dense layers with pooling operations integrated after each layer. Rectified linear unit (ReLU) and SoftMax activation functions were used in the hidden and last output layers, respectively. The experimental data was split into training, testing, and validation sets. Damage labels and corresponding images were initially known for the training dataset. The constructed CNN model was trained, and damage location and a high prediction accuracy were obtained for training, validation, and testing datasets.

Data-driven approaches to damage detection are very common in the field of structural health monitoring (SHM) due to the possibility to be adopted without the need for a model of the monitored structure. These approaches rely only on the information contained in data acquired by sensors, which must be extracted through the adoption of appropriate damage features, i.e., synthetic indexes highly correlated to the structural state. In an unsupervised learning perspective, i.e., when data referring to damage are not available prior to the monitoring, the damage is detected when the damage feature shows a significant variation with respect to a reference set, containing data referring to the initial healthy condition. A critical aspect for unsupervised learning data-driven approaches is related to the fact that, usually, changes of damage features due to environmental and operational variations (EOVs) can be greater than those caused by damage. Many approaches are proposed in the literature to tackle this problem, most of which are validated on simplified cases, under controlled laboratory conditions and where, often, the effect of the damage is only simulated, resulting in a difficult translation to real applications.In this work, attention is paid to the development of an automatic damage detection strategy for axially loaded beam-like structures, that can be used without the supervision of an expert and that allows for identifying a real state of damage, under the effects of an uncontrolled environment. More in detail, the case study of tie-rods (i.e., tensioned metallic beams used to balance lateral forces of arches and walls of civil structures) is addressed. In previous work, the authors showed that when multiple vibration modes are considered together, patterns of modal parameters associated with damage are different from those due to the effects of EOVs, allowing for defining effective damage features. In this chapter, the strategy is further developed, with a focus on the automatization of the strategy for real applications. This means not only dealing with EOVs, but also developing a successful automatic data cleansing strategy, to automatically detect and discard corrupted results obtained when the operational modal analysis algorithms fail due to unfavorable operating conditions. The validity of the proposed framework is demonstrated on real long-term monitoring data and in presence of real corrosion damage, which is a rare case-study in the field of SHM.

The principle of the cloud-based Flamenco software is that the operating vibration response data is uploaded by the owner of the structure, then the modal parameters are automatically extracted, and the results are visualized by the owners on a web browser (like Google Chrome, Firefox, etc.). The software consists of a cloud-based web application with front-end and back-end data, meaning that the clients will always have access to its latest version, regardless of the operating system installed on their computers. Since it is essential for the owner of the structure to nondisclose his operational data and the extracted information, only the owner has access to these data. In this chapter, the principles for implementing the OMA extraction features are outlined, and how to check the operating data for sensor errors and how to remove influence from time-varying operating conditions are also explained. The software is illustrated on a simple case, where operating data from a wind-loaded structure is analyzed.

There is a clear need for floor vibration control technology that is high performance, easy to install, commercially competitive, sustainable and reliable over the long term. This technology can be used to retrofit existing structures that are exhibiting vibration problems and also to increase the efficiency and reduce embodied carbon of new-build floor structures. Active mass damping of floors in office, commercial, healthcare, laboratories, residential and other types of buildings has been the subject of a great deal of academic research over the years. This work has focused on the development of control algorithms and performance studies using off-the-shelf accelerometers, control systems and shakers. The newly developed CALM®FLOOR active mass damper (AMD) system integrates all of these components into a single unit, which is easy to install and is able to deliver active control forces to massively enhance damping in floor structures.This chapter describes a program of analysis and testing applied to a building floor structure subject to pedestrian excitation both without and with the presence of the AMD system in operation. It was found that the AMDs were capable of significantly reducing floor responses by around 75% for this fairly typical composite steel-concrete floor system supporting an open-plan office. Deployment of AMDs throughout the building would clearly have the potential to significantly improve the performance of the whole of the environment, with very minimal disruption given the very small size of the AMDs compared with other more significant interventions such as tuned mass dampers or structural modifications.

Recent advances in passive structural control systems have included devices that exploit nonlinear behavior. The explicit inclusion of nonlinearities allows these passive devices to be designed to have behavior and performance that varies with different load types and amplitudes. The variable inertial rotational mechanism (VIRM) is an example of a nonlinear passive control device and consists of a mechanism that converts linear motion into rotational motion and an attached flywheel that includes masses that can move radially inside the flywheel. The radial motion of the VIRM flywheel masses results in the flywheel moment of inertia continuously varying during the response of the device. Despite a potentially small physical mass, the VIRM can provide to a system large added mass effects that can vary greatly depending on the flywheel moment of inertia. The large and variable mass effects provided by the VIRM can significantly shift the natural frequency and reduce the response amplitude of an underlying structure. While the VIRM has been investigated numerically by a number of authors, the experimental study of these devices has been limited. Moreover, most of the studies have considered semi-active or active variable inertia flywheels. The investigation of passive VIRMs are rare. This study aims to address these gaps in knowledge and experimentally investigate the response modification and pseudo resonance frequency changes of an underlying structure produced by the VIRM considering different loading conditions. For this experimental investigation, a VIRM was designed and fabricated that utilizes a lead screw and a flywheel that contains masses connected to springs that can move radially in the flywheel. This VIRM was then attached to a single-degree-of-freedom structure and subjected to different excitation types using a shake table. With data from these experimental tests, the overall fundamental frequency and the response of the system was evaluated using the experimentally estimated system transfer functions. The results of this study show that the inclusion of the VIRM reduces the response amplitude and significantly shifts the pseudo resonance frequency of the underlying structure and that these shifts in pseudo resonance frequency are highly dependent on the loading amplitude.

Friction-based dampers have gained attention as a cost-effective way to provide structural control during natural hazards. However, the dry friction interfaces in these systems result in a highly nonlinear damping response during the reversal of damper travel, termed damper backlash. Moreover, the stick-slip phenomena intrinsic to the sliding response of dry friction interfaces make the accurate modeling of friction-based structural dampers challenging. Dynamic friction modeling for structural dampers currently relies on analytical models to approximate the damper’s response at a current location given the damper’s state and average out the complex system responses during travel reversal or stick-slip movement to obtain a model of the system’s performance. In this chapter, we propose the use of a deep learning model to capture the temporal dynamics of the system that when combined with the LuGre friction model provides a physics-informed machine learning approach for inferring the damping force of a dry friction interface given the state of the model. Specifically, this chapter uses a long short-term memory model to infer the LuGre friction model’s parameters. A methodology for parameter identification using truncated backpropagation through time is given, which allows for real-time updating. Model validation is performed using a 9 kip rotary friction damper designed for high damping performance and mechanical simplicity. The model is validated with data from real natural hazard events and used in a real-time hybrid simulation. The performance, reliability, and accuracy of the deep-learning-based friction model are discussed.

Damping devices installed in building structures increase resilience against natural hazards such as earthquakes and high winds. Structural control devices are divided into passive, semi-active, active, and hybrid systems. Semi-active vibration control has received considerable attention because it combines the reliability of passive control systems with the versatility to adapt like active systems but with a smaller magnitude of power consumption. Among semi-active devices, variable-friction dampers are promising because they only require a variable clamping force to a surface to dissipate mechanical energy into heat. Factors that drive the adoption of new technology in structural engineering are driven by cost saving, ease of use, technology effectiveness, and reliability of response. Based on these design goals, this research focuses on the design and characterization of semi-active cam-lever friction damper devices. The proposed device applies a normal force to frictional surfaces through slipping bolts that are attached to a cam-double-lever mechanism. This configuration consists of a cam-lever that has a varying radius cam attached to a lever and a slider-crank mechanism that transforms the rotational movement of the first lever into a linear movement of an actuator. This provides a large mechanical advantage to easily adjust the position of the levers and change the tension of the slipping bolts. The feasibility of this device is studied with a small-scale prototype using additive manufacturing of components and an Arduino microcontroller to change the position of the levers. The device is installed in a single-DOF structure that is subjected to harmonic motions and earthquakes using a shake table. The results show that the mechanical advantage and the speed of response of the system are a function of the geometry of the components of the cam-levers. It is also shown that the slipping bolts can have a minimum pre-tension to make the device work as a passive device if the actuation system fails. This initial pilot study opens pathways in advanced mitigation strategies for smart structures.

Tuned mass dampers (TMDs) are proven to be effective in reducing both the acceleration and displacement responses of civil structures subjected to earthquake. The basic idea behind the use of a TMD in civil structures is to create an alternative path for the flow of mechanical energy in the dynamic system of primary structure to deviate the input seismic (or wind) energy from entering its key structural components such as beams and columns. This is achieved by storing and dissipating the input seismic energy in the TMD itself as it oscillates with the vibration of primary structure. The process of energy dissipation in the TMD can be carried out using solid friction by allowing the mass of TMD to slide over a surface attached to the primary structure rather than using viscous damping that is usually provided by viscous fluid dampers connecting the mass of TMD to the primary structure. The former method is relatively simpler to implement and it has a lower cost of installation, operation, and maintenance as well. A TMD that uses friction for energy dissipation is termed as frictional TMD (FTMD). Although the energy dissipation mechanism of FTMD is simple and cost-effective, it is susceptible to stick-slip motion, a highly nonlinear phenomenon occurring at low velocities when the dynamic state of moving mass abruptly shifts from the sliding phase to the sticking phase or vice versa. In particular, during the sticking phase when the mass of FTMD sticks to the friction surface, the FTMD can neither store nor dissipate the input seismic energy. The objective of this chapter is to study how stick-slip motion can affect the energy dissipation capability a FTMD and the seismic performance of primary structure. For this purpose, a 3DOF dynamic model is employed to model the interaction of FTMD with the primary structure (two-story base-isolated building) during the stick-slip motion. The friction force is described by a modified version of the Karnopp friction model in which a sticking velocity is defined to characterize the boundary between the sticking and sliding phases and perform a parametric nonlinear time-history analysis of 3DOF dynamic model. The numerical results show that for a given ground motion acceleration it is feasible to adjust both the mass of FTMD and the normal force in such a way that the intensity of ground motion remains above the breakaway level to avoid sticking, allowing the FTMD to slides continuously during the earthquake.

The drive to maximize the wind-energy harnessing capabilities of modern societies is gradually leading toward the design of floating wind turbines (FWT). To guide such designs, accurate numerical models that predict the dynamic behavior of the systems are crucial to ensure their structural reliability. This chapter presents a new Simulink implementation of the FWT modeling with a focus on the wave-platform interaction. The hydrostatic and hydrodynamic forces applied to the floating platform are calculated using a numerical and an analytical approach. The numerical approach makes use of the open-source boundary element method (BEM) code, Nemoh. In the analytical approach, which is initially limited to a planar response of the FWT, a matched eigenfunction expansions method is examined to evaluate closed-form solutions of the velocity potential and the resulting force. This second approach offers deeper insights into the dynamic system and potentially higher computational efficiency compared to Nemoh. Both approaches are implemented as Simulink subsystems that are integrated with the remaining system in the time domain. The large variety of libraries in Simulink also enables the detailed modeling of other physics including aerodynamics, structural dynamics, and controls. The aerodynamic loads are applied at different cross-sections along the blade using the unsteady blade/element momentum method. While their flexibility can be accounted for, the blades and tower are modeled as rigid bodies in the present study, and the effect of the mooring lines is taken into account as a resulting stiffness matrix. The industry-standard ROSCO controller is also employed. Validation against the most popular tool OpenFAST is carried out on the 5-MW ITIBarge FWT, and the overall agreement demonstrates the capability of the developed Simulink model to perform accurate dynamic analysis for FWTs.

Rhythmic jumping can be expected on grandstands during music and sports events and imparts dynamic load on to the structure. This often results in safety and serviceability concerns. Human interaction with vibrations is the key aspect that influences both human and structural response and therefore evaluating this interaction is important. To this end, this paper has compared four methods of analysing rhythmic jumping on vibrating structures using kinematic data. Vector coding method was found to be the most informative. The original vector coding procedure has been modified in this paper to propose a new strategy for classifying the coordination patterns between feet and platform motion. The method can be used to evaluate whether a jumper can achieve a target frequency and target timing of the jumping as well as to quantify the variability in jumping over time. In addition, the method enables quantification of duration of contact phase of a jumping cycle.

The design for structural vibration induced by human walking is currently based on loading models obtained on rigid surfaces. It is largely unknown how footstep forces would be modified due to human-structure interaction if the surfaces are vibrating. The 2-year project, vPERFORM, aims to identify vertical vibration conditions under which the human-structure interaction occurs and model the interaction to reflect experimental observations of human walking on a vibrating structure. Experiments were carried out in a state-of-the-art facility, VSimulators, to investigate the influence of vibration amplitude, frequency and exposure time on the interaction during normal walking on a treadmill placed on a vibrating platform. Fifteen test subjects were recruited, and ground reaction forces and kinematics data were recorded using force plates and motion capture systems, respectively. In addition, the metabolic cost was also measured to clarify the role of energy optimization in human walking in a vibrating environment. This paper presents details of the experimental program as well as selected preliminary results on ground reaction forces of human walking on the vibrating platform.

Designing a vibration-sensitive facility, such as a laboratory building, as part of a tenant-improvement or renovation project can introduce significant challenges. Often in these projects, the existing building structure has not been designed to meet the vibration requirements of planned sensitive instruments or research, and therefore mitigation measures must be implemented to achieve the desired criteria. This paper will discuss structural dynamic evaluation, analysis, and design for vibration-sensitive facilities, with a focus on specific issues associated with tenant improvement and renovation projects. Via a series of case studies, various strategies for reducing vibration on an existing structure will be presented, including pre and post mitigation measurement results for each case. These case studies will help to illustrate conditions where certain approaches, including structural retrofits, the use of tuned mass dampers, the use of nonstructural elements, and other strategies, may or may not be effective or feasible.

Preliminary results of vibration serviceability evaluation of a new, modular steel floor framing system for commercial building structures are presented. The proposed system intends to increase the speed of construction by eliminating the placing of a concrete deck and thus reduce the time from conception to occupancy for steel building structures. The relatively lightweight floor system has been studied for vibration serviceability, and the results presented herein are a summary of the analytical research conducted before the experimental phase of the project. The analytical work presented in this study includes the vibration serviceability assessment per AISC Design Guide 11 (Vibrations of Steel-Framed Structural Systems Due to Human Activity) and computational simulations using finite element software.