Search

We develop a ground motion prediction equation (GMPE) for estimating the vertical ground motion amplitudes for crustal earthquakes in Taiwan. The data set used for the development includes strong-motion recordings mainly from Taiwan earthquakes (M3.5–7.6) and supplemented with large-magnitude earthquakes (M6.5–7.9) from other regions in the Pacific Earthquake Engineering Research Center (PEER) next generation attenuation (NGA)-West2 database. The functional form of the GMPE is similar to that of Phung et al. developed for the horizontal component (P20). The GMPE provides median and standard deviations of peak ground acceleration (PGA) and 5% damped pseudo spectral acceleration response ordinates of the orientation-independent average horizontal component of ground motion (RotD50) for the spectral period of 0.01–10 s. The vertical ground motion developed in this study can be paired with the P20 horizontal component model to estimate a vertical-to-horizontal (V/H) ratio that is unbiased. In the vertical component, we observe significant nonlinear site effects in the period of about 0.2–0.5 s, moderate nonlinear site effects in the period of about 0.01–0.04 s, and small nonlinear site effects in the period of about 0.05–0.075 s. Compared to our horizontal GMPE, anelastic attenuation is faster, VS30-scaling is reduced, and nonlinear site response is weaker for the vertical component.

The measurement of vital signs (such as respiration rate, body temperature, pulse, and blood pressure), especially during strenuous activities, is essential for physical performance and health monitoring. A variety of wearable chest band sensors have been developed, commercialized, and widely used in consumer and healthcare settings. The plethora of technology choices also means that each unique chest band sensor may require different data acquisition hardware and software systems, and data may not be transferable between platforms. Therefore, the objective of this work was to develop a low-cost, disposable, respiration sensor that could be attached onto any elastic chest band. The approach was to spray-coat graphene nanosheet (GNS)-based thin films onto unidirectionally stretchable elastic fabric to form a piezoresistive material. Snap buttons were incorporated at the ends of the fabric so that they could be attached onto any chest band, removed at any time, and replaced for a new data collection event. The resistive nature of the nanocomposite sensor means that they can be easily interfaced (e.g., using a voltage divider) with any existing data acquisition (DAQ) module while adding respiration monitoring capabilities. To facilitate testing of these nanocomposite respiration sensors, a miniature DAQ module with four sensing channels was also prototyped. Then, tests were performed with human subjects wearing a nanocomposite chest band and a reference commercial respiration monitoring chest band. Simultaneous measurements of subject respiration verified the respiration monitoring performance of these low-cost, disposable, nanocomposite fabric sensors.

We develop a ground motion prediction equation (GMPE) for estimating horizontal ground motion amplitudes caused by crustal earthquakes, based on an integrated data set that includes strong motion recordings mainly from Taiwan earthquakes and only from large magnitude earthquakes in the NGA-West2 database. This GMPE is developed for probabilistic seismic hazard analysis study, which is introduced as a part of Taiwan Senior Seismic Hazard Analysis Committee Level 3 projects. The functional form developed by Chiou and Youngs was carefully studied to determine the key modeling parameters needed to regress against ground motion in the target region. Using this functional form, the GMPE achieves considerable improvement over previously developed Taiwan GMPEs. In particular, the use of a high-order function in magnitude scaling enables representation of the saturation effects of large earthquakes. Moreover, consideration of focal mechanisms, depth effects, and dip effects are used to correct the magnitude scaling; consideration of nonlinear site amplification is conditioned on VS30and reference ground motion on rock; and consideration of basin depth effect is a function of Z1.0in correlation with VS30. In addition, ground motion data used in this study are not only expanded by more than three times as many earthquakes and records compared with a previous Taiwan model but also provide the metadata of these records that were not available or were previously incomplete. In this study, we compare the proposed model with the NGA-West2 models and discuss the regional difference in ground motion in terms of spectral shape, magnitude scaling, distance scaling, depth scaling, style of faulting, and site effects. We provide median and single standard deviations of peak ground acceleration and 5% damped pseudospectral acceleration response ordinates of the orientation-independent average horizontal component of ground motion (RotD50) for the spectral period of 0.01–10 s.

A ground motion prediction equation (GMPE) is presented for computing the median and standard deviation of peak ground acceleration (PGA) and 5% damped pseudo-spectral acceleration (PSA) for periods between 0.01 s and 5.0 s for probabilistic seismic hazard analysis (PSHA) and engineering applications in Taiwan. An integrated strong motion dataset consisting of two subduction earthquake regions was selected from 3314 recordings from Taiwan with M4.5 to M7.1 and 3376 recordings from Japan with M6.5 to M9.1. This dataset was then used to validate, and refit where necessary, the function form provided by Abrahamson et al. for application to Taiwan subduction earthquakes. The proposed model accounts for the extrapolation behaviors associated with the large-magnitude scaling and the near-source scaling terms, both of which were developed empirically by using the combined Taiwan–Japan dataset. The distance attenuation and site term were developed specifically for the Taiwan region. The site term is based on two parameters; the time-averaged shear wave velocity of the top 30 m depth ( VS30) and the depth-to-the-shear wave velocity horizon of 1.0 km/s ( Z1.0).

The objective of this study is to design and validate distributed strain field monitoring using a patterned nanocomposite “sensing mesh” that is coupled with an electrical impedance tomography (EIT) measurement strategy and algorithm. Although EIT has been used in other studies and in conjunction with a piezoresistive thin film for spatial damage detection, different strain components cannot be directly extracted from reconstructed EIT conductivity maps. Therefore, this study seeks to address this issue by patterning piezoresistive graphene-based thin films to form a mesh-like pattern. The high aspect ratio of each nanocomposite grid interconnect acts as a linear distributed strain sensor, capable of resolving strains along the entire length and direction of the element. This study first began with the design, fabrication, and characterization of the strain sensing response of a graphene-based thin film of high strain sensitivity. Second, the strain-sensitive film was spray-coated onto patterned polymer substrates to form the sensing meshes, which were then subjected to load tests. Upon validating distributed strain field monitoring through EIT, its applicability for field implementation and damage characterization was also demonstrated by instrumenting sensing meshes in the column of a seven-story reinforced-concrete building subjected to shaking table earthquake excitations. The large-scale shaking table test results successfully validated distributed damage detection.

This study aims to validate a piezoelectric driven-rod scour monitoring system that can sense changes in scour depth along the entire rod at its instrumented location. The proposed sensor is a polymeric slender rod with a thin strip of polyvinylidene fluoride that runs through its midline. Extraction of the fundamental frequency allows the direct calculation of the exposed length (or scour depth) of the slender rod undergoing fluid flow excitation. First, laboratory validation in dry conditions is presented. Second, hydrodynamic testing of the sensor system in a soil-bed flume is discussed. Each rod was installed using a three-dimensional-printed footing designed for ease of installation and stabilization during testing. The sensors were installed in a layout designed to capture symmetric scour conditions around a scaled pier. In order to analyze the system out of steady-state conditions, water velocity was increased in stages during testing to induce different degrees of scour. As ambient water flow excited the portion of the exposed rods, the embedded piezoelectric element outputted a time-varying voltage signal. Different methods were then employed to extract the fundamental frequency of each rod, and the results were compared. Further testing was also performed to characterize the relationship between frequency outputs and flow velocity, which were previously thought to be independent. In general, the proposed driven-rod scour monitoring system successfully captured changing frequencies under varied flow conditions.

Active control strategies are more powerful and attractive than passive and semi-active control strategies. An active control device, namely active mass damper (AMD), consists of mass, a guideway, and an AC-driven motor and can provide a widely applicable range of control forces with a very limited power requirement. This device can be more effective to suppress earthquake- or wind-induced vibrations to civil structures. Various innovative control algorithms have been developed to drive AMD against seismic loads, and these new or improved algorithms are also found as a key element in smart structure technology. Some research on active control strategies for the reduction of the seismic responses uses either full-state or, at least, velocity feedback; however, the accurate measurement of the displacement and velocity is typically unavailable from real-world structures. Instead, the control algorithms based on acceleration feedback are more feasible for the practical implementation of control system. In this study, an active control system is developed by integrating an AC-driven AMD and accelerometers. This system is comprised of a real-time embedded system with a control algorithm which executes the sliding mode control (SMC) with acceleration feedback. The acceleration feedback SMC controller is designed in accordance with an identified model of a model building. Subsequently, this control system is experimentally implemented and verified using shake table testing. Performance of the integrated system as well as the responses of the frame are evaluated and discussed to demonstrate the effectiveness of the acceleration feedback SMC algorithm in mitigating vibrations of seismically-excited structures.

In probabilistic seismic hazard analysis (PSHA), the standard practice is to select a set of appropriate ground motion prediction equations (GMPEs) and assign weights on the logic tree, especially for regions where strong motion data are sparse and where no indigenous GMPE exists. Subjectively assigning weights to a set of models usually has the disadvantage of not obtaining mutually exclusive and collectively exhaustive models because of sparse or unavailable data. Therefore, the development of logic tree weightings in PSHA remains a major challenge. In this study, a distance metric measure for GMPEs is first analyzed to show how a set of GMPE’s prediction models can be partially reconciled by using high-dimensional information visualization techniques. Visualization of a large suite of GMPEs onto a 2-D graphical map provides a powerful theoretical framework that can guide the selection of a set of representative models. These models are considered mutually exclusive and collectively exhaustive, and have the ability to represent the center, body, and range of ground-motion distribution in a logic tree analysis. Second, determination a set of weights for PSHA are estimated based on the residuals, likelihood and EDR-index which are known as data – driven weight. The other weight type is non-data driven which is calculated based on the probability of a random model generated on a Sammon’s map. The methods presented here, that improve consistency in the weight assignment, can help to reduce overall epistemic uncertainties and offer a way of assigning weight on the logic tree. Finally, an example for assigning the weights on the logic tree for PHSA are discussed.

Summary Structural damage assessment under external loading, such as earthquake excitation, is an important issue in structural safety evaluation. In this regard, appropriate data analysis and feature extraction techniques are required to interpret the measured data and to identify the state of the structure and, if possible, to detect the damage. In this study, the recursive subspace identification with Bona-fide LQ renewing algorithm (RSI-BonaFide-Oblique) incorporated with moving window technique is utilized to identify modal parameters such as natural frequencies, damping ratios, and mode shapes at each instant of time during the strong earthquake excitation. From which the least square stiffness method (LSSM) combined with the model updating technique, called efficient model correction method (EMCM), is used to estimate the first-stage system stiffness matrix using the simplified model from the previously identified modal parameters (nominal model). In the second stage, 2 different damage assessment algorithms related to the nominal system stiffness matrix were derived. First, the model updating technique, called EMCM, is applied to correct the nominal model by the newly identified modal parameters during the strong motion. Second, the element damage index can be calculated using element damage index method (EDIM) to quantify the damage extent in each element. Verification of the proposed methods through the shaking table test data of 2 different types of structures and a building earthquake response data is demonstrated to specify its corresponding damage location, the time of occurrence during the excitation, and the percentage of stiffness reduction.

Summary Tracking modal parameters and estimating the current structural state of a building from seismic response measurements, particularly during strong earthquake excitations, can provide useful information for building safety assessment and the adaptive control of a structure. Therefore, online or recursive identification techniques need to be developed and implemented for building seismic response monitoring. This paper develops and examines different methods to track modal parameters from building seismic response data. The methods include recursive data-driven subspace identification (RSI-DATA) using Givens rotation algorithm, and RSI-DATA using Bona fide algorithm. The question on how well the results of RSI-DATA reflect the real condition is investigated and verified with a bilinear SDOF simulation study. Time-varying modal parameters of a four-story reinforced concrete school building are identified based on a series of earthquake excitations, including several seismic events, large and small. Discussions on the different methods' ability to track the time-varying modal parameters are presented. The variation of the identified building modal frequencies and damping ratios from a series of event-by-event seismic responses, particularly before and after retrofitting of the building is also discussed. Copyright © 2017 John Wiley & Sons, Ltd.

Since tracking modal parameters and estimating the structural current state from building seismic response measurement can provide useful information for building safety assessment, online or recur...

Given the probability distribution function relating to the recurrence interval and the occurrence time of the previous occurrence of a fault, a time-dependent model of a particular fault for seismic hazard assessment was developed that takes into account the active fault rupture cyclic characteristics during a particular lifetime up to the present time. The Gutenberg and Richter (1944) exponential frequency-magnitude relation uses to describe the earthquake recurrence rate for a regional source. It is a reference for developing a composite procedure modelled the occurrence rate for the large earthquake of a fault when the activity information is shortage. The time-dependent model was used to describe the fault characteristic behavior. The seismic hazards contribution from all sources, including both time-dependent and time-independent models, were then added together to obtain the annual total lifetime hazard curves. The effects of time-dependent and time-independent models of fault [e.g., Brownian passage time (BPT) and Poisson, respectively] in hazard calculations are also discussed. The proposed fault model result shows that the seismic demands of near fault areas are lower than the current hazard estimation where the time-dependent model was used on those faults, particularly, the elapsed time since the last event of the faults (such as the Chelungpu fault) are short.

This study presents a structural health monitoring method for damage identification and localization of a structure, which incorporated with the principal component analysis (PCA) based data compression and pattern recognition. Through the distribution of sensing node in the structure, an experimental twin-tower steel structure on shaking table test, the dynamic response at different point in the structure is collected. Two different damage scenarios are created in the structure: (1) buckling of the first story bracing member of tower A, (2) buckling of the 2nd floor bracing member of tower A. To extract the features from the vibration measurement, first, stochastic subspace identification and recursive subspace identification methods were applied to extract the modal parameters of their structural system. Then the FRF-based damage assessment using PCA data compression and the scalogram-based novelty detection are used to obtained patterns in some significant respect on damage assessment. Finally, based on the change of flexibility matrix of the system, damage location can be identified..

Summary Damage assessment of a structure involves acquiring and identifying dynamic characteristics of the structure and using these characteristics to evaluate behavior and performance. In this study, an unsymmetrical three-story steel structure (fabricated with one weak column in the first floor) was tested on shaking table and subjected to a series of earthquake excitations with increasing level of excitation back to back. Besides, white noise excitation was also applied in between the earthquake excitation to serve as the reference state. Both the traditional sensing system (accelerometer and linear variable differential transformer) and the local optical tracker system were implemented in the structure to collect the vibration-based responses. For operational modal analysis, structural response from white noise excitation will be used in this study. First, the traditional system identification using global response data is used (multivariate autoregressive (AR)-model) to extract system natural frequencies and mode shapes from all different set of white noise responses after earthquake excitation. The migration of AR-coefficient ellipse error from each sensor response was used to identify the damage location. Second, blind source separation technique was used to identify the modal contribution of the structure from each test, which provide information to detect the damage severity. Finally, from the local optical tracker array data, the principal component analysis was applied to quantify the earthquake-induce local stress of the structural member. Combine the result from damage detection using global measurement and the identified local element stress, one can locate and quantify the damage. Copyright © 2015 John Wiley & Sons, Ltd.

The effects of earthquake-induced basin amplification are caused by the interaction of wave-fields with a basin boundary, which depend on complex source to site distances, basin geometry (topography), and sediment distribution within the basin. In this study the identification of long-period waves induced by strong earthquake motions through basin topography together with local site effects of the basin is investigated. Through multivariate singular spectrum analysis (MSSA), a unique analysis tool for extracting tendencies and harmonic components in geophysical time series is used to extract the long-period wave of basin response caused by the seismic events. Seismic response data of Taipei Basin were used to examine the existing of the lowest dominant frequency of the basin caused by basin topography. Finally, the seismic-induced ground motion data can be separated to local site effects and the basin motion caused by the effect of topography.

Recently, there has been remarkable progress in the development of data acquisition systems and statistical damage detection algorithms in the field of vibration-based structural health monitoring (SHM). However, many of them have not been validated by experimental data. This paper presents and analyzes a shake table experiment of two three-story steel structures. The structural damage was introduced in a systematic and controlled way and the structures were tested with different levels of earthquakes. Three types of analyses were conducted. First, a wireless sensor system designed for SHM, SnowFort, was demonstrated to have the same accuracy as the conventional wired sensors. Second, the rotation algorithm is applied to estimate residual displacements in the structures and achieves excellent agreements with the displacement sensor measurements. At last, a continuous wavelet transform-based damage detector is applied to the experimental data. The results highlight that the wavelet model parameters are sensitive to the damage state.

Modal parameters identification research based on using vibration measurements can reflect the true dynamic behavior of a structure while analytical prediction methods, such as finite element models, are less accurate due to the numerous structural idealizations and uncertainties involved in the simulations. Since time–frequency analysis for non-linear and non-stationary signals is extraordinarily challenging for structural response under extreme loading. To capture features in these signals, it is necessary for the analysis methods to be local, adaptive and stable. Classical approach to compute its instantaneous frequency is to consider the amplitude–frequency modulated formulation of its complex (or analytic) signal extension, via the Hilbert transform. The aim of this paper is therefore to present and discuss some non-parametric methods of time frequency analysis improved with respect to the classical implementation, and compare their performance under different conditions with respect to the signals to be examined. In particular, the following methods will be presented: Modified Complex Morlet Wavelet with Variable Central Frequency (MCMV+VCF) Enhanced Time-Frequency Analysis Through SVD-based WPT Synchro-Squeeze wavelet transform method (SSWT) Reassigned Smoothed Pseudo Wigner-Ville Distribution (RSPWVD) To demonstrate the applicability of the proposed method responses from synthetic signals and instrumented responses are used to demonstrate the capability of feature extraction of the methods, analysis on the synthetic data as well as the response data.

Structural damage assessment under external loading, such as earthquake excitation, is an important issue in structural safety evaluation. In this regard, appropriate data analysis and feature extraction techniques are required to interpret the measured data and to identify the state of the structure and, if possible, to detect the damage. Among these techniques tracking modal parameters and estimating the structural current state from its seismic response measurement can provide useful information for structural safety assessment, therefore, on-line or recursive identification technique needs to be developed for structural seismic response monitoring. In this paper, the recursive subspace identification algorithms based on matrix inversion lemma algorithm (RSI-Inversion) with oblique projection technique was developed. Forgetting factor with enlarge window is introduced in the RSI-Inversion to emphasize the latest state of the time-varying system in this method. In addition to identifying the instantaneous dynamic characteristics of the structural system using RSI, a two-stage damage detection algorithm incorporated with the identified results from RSI will also be applied to localize and quantify the structural damage. Seismic responses of a base-isolated bridge are used to verify the proposed identification and the damage assessment algorithms, i.e. specify its corresponding damage location, the time of occurrence during the excitation, and the percentage of stiffness reduction.

The objective of this study is to discuss three different methods on operational modal analysis: Covariance-driven stochastic subspace identification (SSI-COV), Refined frequency domain decomposition (rFDD) and Multivariate autoregressive (MVAR) model. First the SSI-COV method is employed. Through the proposed two steps of evaluation criteria on the discrimination of spurious modes from the stabilization diagram, identification on the correct models can be elaborated. Besides, discussion on the identification of harmonic component from stabilization diagram using the concept of singular value spectrum generated by refined frequency domain decomposition (rFDD) is also presented. Combine the SSI-COV, rFDD and the proposed criteria to remove the spurious modes, one can accurate estimate the structural modal dynamic properties. Comparison on the final stabilization diagram with respect to the model order spectrum using multivariate AR model is also presented. As an application, an ambient vibration test of 8-story steel frame from shaking table test is used to demonstrate the proposed algorithms. Discussion on the missing data to the influence on the result of identification is also presented. Compensation on the missing data to enhance the stabilization diagram.

Critical non-structural equipment, including high-precision equipment in the technology facilities, life-saving equipment in the hospitals, data storage equipment in the communication, computer, and data centers, etc., is vulnerable to vibration, and on top of that, the failure of these vibration-sensitive equipment will cause severe economic loss. In recent years, a lot of research has been conducted towards evaluation of semi-active control strategy for earthquake protection of vibration-sensitive equipment. Various innovative control algorithms have been studied to compensate the inertial loading, and these new or improved control strategies, such as the control algorithms based on the linear-quadratic regulator (LQR) and the sliding mode control (SMC), are also developed as a key element in smart structure technology. However, except to the advantage of simplicity and variability, both LQR and SMC are (centralized) full-state feedback controller and the state vector needs to be presented through a state estimator or compensator if it is not measured during earthquake excitation. On the other hand, considering decentralized control strategy, the controller is only evaluated using the response in the vicinity of control devices, thus minimizing the wiring and sensor communication requirements. However, the very limited information obtained and used by decentralized control strategy also restrains the control capability from reducing the response where without installing sensors. The aim of this paper is to develop a decentralized control algorithm on the control of both structure and non-structural equipment simultaneously to overcome the limitations of decentralized control through combining the advantage of wavelet analysis. This wavelet-based decentralized control algorithm will be simulated based on a frame with an equipment isolation system and a magnetorheological (MR) dampers located on the top of first floor numerically. The performance and robustness of wavelet-based decentralized control algorithm as well as the response of primary structure are evaluated and discussed through simulation study to demonstrate the efficiency of proposed control algorithms.

Tracking modal parameters and estimating the structural current state from building seismic response measurement can provide useful information for building safety assessment, therefore, on-line or recursive identification technique needs to be developed for building seismic response monitoring. In this paper, three recursive subspace identification algorithms are developed. One is the recursive subspace identification with Bona-fide LQ renewing algorithm (RSI-BonaFide), one is recursive subspace identification based on matrix inversion lemma algorithm (RSI-Inversion) either with oblique projection technique or with orthogonal projection technique. A forgetting factor is introduced in the RSI-Inversion to emphasize the latest state of the time-varying system in this method. Two different sets of building seismic response data are used to verify the applicability of the proposed methods. One is the 3-story steel structure with abrupt change of story stiffness shaking table test in laboratory, the other is the seismic response analysis of a 9-story mid-story isolation building.

Dynamic displacement of a structure is an important indicator of structural health condition after an earthquake. The displacement sensors, such as the linear variable differential transducers, cannot be used with an earthquake because the displacement of the instrument is hard to be separated from those of the structure. Recently, many new low-cost microelectromechanical sensors have both 3D accelerometers and 3D gyroscopes on board. These sensors provide simultaneous and high accurate measurements. In this paper, we will utilize these sensors to estimate dynamic displacement. Specifically, we firstly propose to use a complementary filter to combine the acceleration and gyroscope signals. Then, we apply digital signal processing techniques to improve the estimation accuracy further. The proposed algorithm shows highly accurate results on a data set obtained from an indoor shake table experiment

This paper presents a recent shake table experiment on a 4-story steel frame with predictable damage. The yielding of column in the first story was produced to represent damage and the deterioration changed the structural properties. Using acceleration measurement, the damage can be obtained and compared with visual inspection of the structures. In addition, a structural damage detection algorithm based on time-frequency representation was validated based on the experimental data

Summary Civil engineering structures, such as reinforced concrete frames, exhibit nonlinear behavior when subject to dynamic loads, such as earthquakes. The ability to detect damages in structures after a major earthquake will ensure their reliability and safety. Innovative analysis techniques for damage detection of structures have been extensively studied recently. However, practical and effective local damage identification techniques remain to be developed for nonlinear structures, in particular hysteretic reinforced concrete (RC) structures. In this paper, a smooth hysteretic model with stiffness and strength degradations and with the pinching effect is used to represent the dynamic characteristics of RC frames. A system identification method capable of detecting damages in nonlinear structures, referred to as the adaptive quadratic sum-square error with unknown inputs (AQSSE-UI), will be used to detect damages in hysteretic RC frames. The performance of the AQSSE-UI technique will be demonstrated by the experimental data. A one-third-scale two-story RC frame has been tested experimentally on the shake table at National Center for Research on Earthquake Engineering, Taiwan. This two-story RC frame was subject to different levels of ground excitations back to back. The RC frame is firstly considered as a time-varying linear model with rotational springs at joints, and the tracking of the degradation of the time-varying stiffness parameters is carried out using the AQSSE-UI technique. Then the same RC frame is considered as a nonlinear structure consisting of plastic hinges at joints following a smooth hysteretic model. Experimental results show that the AQSSE-UI technique is quite effective for tracking (i) the stiffness degradation of time-varying linear structures and (ii) the nonlinear hysteretic parameters with stiffness and strength degradations as well as the pinching effect. Copyright © 2015 John Wiley & Sons, Ltd.

Summary This paper addresses the issue of structural system identification using earthquake-induced structural response. The proposed methodology is based on the subspace identification algorithm to perform identification of structural dynamic characteristics using input–output seismic response data. Incorporated with subspace identification algorithm, a scheme to remove spurious modes is also used to identify real system poles. The efficiency of the proposed method is shown by the analysis of all measurement data from all measurement directly. The recorded seismic response data of three structures (one 7-story RC building, one midisolation building, and one isolated bridge), under Taiwan Strong Motion Instrumentation Program, are analyzed during the past 15 years. The results present the variation of the identified fundamental modal frequencies and damping ratios from all the recorded seismic events that these three structures had encountered during their service life. Seismic assessment of the structures from the identified system dynamic characteristics during the period of their service is discussed. Copyright © 2014 John Wiley & Sons, Ltd.

This paper presents an investigation of using long-term static deformation measurements for structural health monitoring (SHM) of Fei-Tsui dam, which is located in a very active seismic zone of Taiwan. A modeling technique is developed to characterize the measured static deformation, which is described as a function of the measured physical parameters, where the water level and temperature are the two most important variables affecting dam deformation. The developed modeling method integrates a linear least-squares technique and a nonlinear least-squares process to identify a model by minimizing the residual error between the identified model and the measured static deformation. The proposed modeling technique can also be used for the prediction of the long-term deformation based on the measured water level and temperature for SHM.

Structural health monitoring (SHM) is considered as an incentive multi-disciplinary technology for conditional assessment of infrastructure system. However, most classical output-only system identification methods based on a stationary assumption fail to achieve reliable results under non-stationary scenarios. Numerous techniques from the disciplines of multivariate statistics and pattern recognition in the field of structural damage detection have been developed, such as stochastic subspace identification or null-space and subspace damage identification. In order to obtain the accurate and quick structural damage assessment of a structure, measurement from all sensing nodes in the structure must be used. Therefore, dimension reduction and data visualization techniques for damage assessment need to be investigated. In this study a structural health monitoring method for damage identification and localization, which incorporated with the principal component analysis (PCA) based data compression and pattern recognition is developed. First, the frequency response function (FRF)-based damage assessment will be investigated. Based on the FRF to construct the Sammon map from all the measurements will be investigated. Sammon’s mapping is a nonlinear dimensional reduction algorithm which seeks to preserve distances as far as possible. Similarity among different data structure can be used to detect damage localization. Besides, the correlation of time-frequency analysis of data (Hilbert amplitude spectrum from WPT) among different measurement and the differences on the reconstructed data using multivariate AR-model are also used to detection damage. Verification of the proposed methods by using shaking table tests of two structures is demonstrated: One is focus on the damage of lower vibration modes and the other one is focus on the damage of high frequency modes.

In this study, the recursive update algorithm of subspace system identification is proposed for estimating dynamic characteristics of a time-varying multi-input multi-output (MIMO) building structure using the pastinput/ output multivariable output-error state space (PO-MOESP) based algorithm. First, the recursive subspace identification with Bona-fide LQ renewing algorithm (RSI-BonaFide-Oblique) incorporated with moving window technique is utilized to identify modal parameters such as natural frequencies, damping ratios and mode shapes at each time instant during the strong earthquake excitation, which assumes the equivalent linear dynamic characteristic in every moving time window with a fixed length. In addition to identifying the instantaneous dynamic characteristics of the structural system using RSI, several damage detection algorithms related to the reconstructed system stiffness matrix using simplified model was derived from the previously identified modal parameters to quantify the damage extent, specify its corresponding damage location, the time of occurrence during the excitation, and the percentage of stiffness reduction. To demonstrate the capability of the proposed recursive subspace identification algorithm and the damage assessment technique, dynamic response data generated from a shaking table test of a two 3-story steel structure experiment is used to verify the algorithms on detecting damage caused by nonlinear behavior occurring on the steel column. Besides, a building seismic response data (collected from Chi-Chi earthquake) was also used to evaluate the damage situation during severe earthquake excitation.

Damage identification to be used in a SHM system, numerous methods have been studied including methods based on modal properties (natural frequencies and mode shapes) and frequency response function (FRF). However, the methods based on modal properties may have limited capabilities for early detection of local damage due to contaminated measurements and computational error that can lead to false alarm. Besides, local damage is frequently associated with higher modes and nonlinearities and even more difficult to detect. For random, non-stationary, nonlinear, and transient signals, under pattern-level data analysis wavelet transform (WT) has been used as a signal pattern recognition method in structural damage identification. In this study, wavelet decomposition of dynamic data using modified complex Morlet wavelet with variable central frequency (MCMW+VCF) establishes a time-frequency representation for modal parameter estimation and system damage identification. Seismic response data collected from two different steel frames, a twin tower steel structure and a 3-story steel structure, under a series of both white noise and earthquake excitation were used for this discussion. First, the accuracy of MCMW+VCF for the purpose of using in the analysis of nonlinear response of structure is examined through the decomposition of vibration responses into wavelet coefficient distribution as a joint function of time and frequency. Using the wavelet coefficients, phase information and Novelty index, temporal variation of stiffness in the response of structures to strong earthquake excitation can be identified. Besides, through the total wavelet coefficient joint density function at each measurement for the pre-damage and post-damage states, detection and localization of damage can be implemented.

Critical non-structural equipment, including life-saving equipment in hospitals, circuit breakers, computers, high technology instrumentations, etc., is vulnerable to strong earthquakes, and on top of that, the failure of the vibration-sensitive equipment will cause severe economic loss. In order to protect vibration-sensitive equipment or machinery against strong earthquakes, various innovative control algorithms are developed to compensate the internal forces that to be applied. These new or improved control strategies, such as the control algorithms based on optimal control theory and sliding mode control (SMC), are also developed for structures engineering as a key element in smart structure technology. The optimal control theory, one of the most common methodologies in feedback control, finds control forces through achieving a certain optimal criterion by minimizing a cost function. For example, the linear-quadratic regulator (LQR) was the most popular control algorithm over the past three decades, and a number of modifications have been proposed to increase the efficiency of classical LQR algorithm. However, except to the advantage of simplicity and ease of implementation, LQR are susceptible to parameter uncertainty and modeling error due to complex nature of civil structures. Different from LQR control, a robust and easy to be implemented control algorithm, SMC has also been studied. SMC is a nonlinear control methodology that forces the structural system to slide along surfaces or boundaries; hence this control algorithm is naturally robust with respect to parametric uncertainties of a structure. Early attempts at protecting vibration-sensitive equipment were based on the use of existing control algorithms as described above. However, in recent years, researchers have tried to renew the existing control algorithms or developing a new control algorithm to adapt the complex nature of civil structures which include the control of both structures and non-structural components. The aim of this paper is to develop a hybrid control algorithm on the control of both structures and equipments simultaneously to overcome the limitations of classical feedback control through combining the advantage of classic LQR and SMC. To suppress vibrations with the frequency contents of strong earthquakes differing from the natural frequencies of civil structures, the hybrid control algorithms integrated with the wavelet-base vibration control algorithm is developed. The performance of classical, hybrid, and wavelet-based hybrid control algorithms as well as the responses of structure and non-structural components are evaluated and discussed through numerical simulation in this study.

techniques, for structural vibration suppression under earthquakes. Various control strategies have been developed to protect structures from natural hazards and improve the comfort of occupants in buildings. However, there has been little development of adaptive building control with the integration of real-time system identification and control design. Generalized predictive control, which combines the process of real-time system identification and the process of predictive control design, has received widespread acceptance and has been successfully applied to various test-beds. This paper presents a formulation of the predictive control scheme for adaptive vibration control of structures under earthquakes. Comprehensive simulations are performed to demonstrate and validate the proposed adaptive control technique for earthquake-induced vibration of a building.

A scour monitoring system with a micro camera tracking the bed-level images is proposed in this study. Two image recognition algorithms have been developed to support the bed-level image tracking approach. Through the laboratory experiments of pier scour, this study demonstrates that the proposed system is able to accurately monitor the scour-depth evolution in real time. In addition, five commonly-used temporal scour models are employed to simulate scour-depth evolution and their results are compared with monitoring data. In general, the results indicate that the proposed scour monitoring system has the potential for further applications in the field.

Some civil engineering structures, such as RC frames, exhibit hysteretic behavior when subject to dynamic loads, such as earthquakes. The identification of hysteretic systems with stiffness and strength degradations is a practical but challenging problem encountered in the engineering field. A recently developed technique, referred to as the adaptive quadratic sum-square error with unknown inputs (AQSSE-UI), is capable of identifying time-dependent parameters of hysteretic structures. In this paper, the AQSSE-UI technique is applied to the parametric identification of hysteretic RC frames with stiffness and strength degradations and with the pinching effect. The performance of the AQSSE-UI technique will be demonstrated by the experimental data. A 1/3-scaled 2-story RC frame has been tested experimentally on the shake table at the National Center for Research on Earthquake Engineering (NCREE), Taiwan. This 2-story RC frame was subject to different levels of ground excitations back to back. The str...

Severe pier scour is usually the major cause of bridge failures. It is therefore crucial to develop various scour monitoring techniques and prediction models for real-time warning of bridge safety. In the present study, a multi-lens monitoring system for pier scour under laboratory conditions has been developed. By utilising a plurality of lenses, this system is capable of tracking scour images and obtaining real-time scour-depth variation through a series of image recognition processes. Laboratory experiments under unsteady flow conditions were carried out to validate the proposed monitoring method. Then, three time-dependent scour prediction models were employed for simulation and comparison with the measured data. In order to improve the scour prediction results, with the real-time scour monitoring data, a data assimilation scheme is proposed and applied to the scour model under clear-water scour conditions. The result shows that the accuracy of scour prediction for a lead time of 3 h can be improved significantly.

The objective of this paper is to discuss two different categories on structural damage detection: One is the centralized feature extraction technique by using multi-sensor architecture and the other is the pattern-level data fusion by using the extracted features from individual sensor. The covariance-driven stochastic subspace identification and multivariate singular spectrum analysis are applied for the centralized feature extraction. For pattern-level feature recognition wavelet packet transforms and power spectral density from individual sensing record is used. Through both centralized and pattern-level feature extraction the results can not only detect the occurrence of structural damage but also can locate the damage. Finally, the dynamic finite element model updating is used for damage quantification. Verification of the proposed algorithms by using a research-scale bridge scouring test model to detect the damage occurrence and severity during scouring is presented. Finally, discussion on the computation effectiveness among different method is made.

The local flexibility method (LFM), which can determine local stiffness variations of beam structures using measured modal parameters, is a promising approach for damage detection of structures using vibration signals. The LFM method is founded on virtual forces that cause nonzero stresses within a local part of the structure. In this study, non-local virtual forces which cause concentrated stresses in a local part and nonzero stresses in the other parts of a structure are employed. The theoretical basis of the proposed pseudo local flexibility method (PLFM) which uses the non-local virtual forces is derived. The effects of the number of modes on damage detection results of three damage scenarios of a numerical hyperstatic beam are studied. The results show that much fewer modes are required for the PLFM to estimate the damage locations and extents. Therefore, the feasibility of the PLFM is higher due to limited number of high quality modes can be identified in real world application, especially for ambient vibration measurement.

A portable particle image velocimetry (PIV) measurement system is developed for sensing flow surface velocities. Using the laser-based technique, four laser points are projected on the flow surface and utilized as reference points to simplify the image processing of orthorectification in PIV measurement. Furthermore, by combining a smart phone with laser modules, a portable PIV device has been developed that is capable of performing PIV measurement comprehensively. Two laboratory experiments were carried out to validate the proposed portable PIV measurement system. The experimental results show that this portable PIV system could be easily set up and measure the flow surface velocities immediately.