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Transverse synchronization of vertical displacements of all jacking-up points is an important monitoring indicator to replace bearings in assembled multigirder bridges during the jacking phase. Currently, using target paper to identify the 3D coordinates of control points reduces the complexity of monitoring operations and improves the stability of data precision. However, the existing planar target locating methods have low accuracy, inefficiency, and subjectivity, which seriously hinders the construction process of bearing replacement. Accurately obtaining the center coordinates of multiple targets from a point cloud in a short monitoring period remains a challenge. This study proposes a high-precision automated algorithm to extract target center points in low-density point clouds to quickly calculate real target center points. First, we construct a standard point cloud model of the target papers for scanning, including color and geometric features. Then, we extract the measured point cloud of the typical jacking operation phase based on the reflection intensity and size information. Next, we map the intensity values of the measured point cloud into the color channel and register the measured point cloud with its standard point cloud model using the normal vector estimation and colored ICP algorithms. Finally, we extract the center point of the measured targets. Numerical experiments and engineering test results show that the proposed method converges quickly with high precision and good robustness, which saves 91.4% of the time compared with the traditional method. In general, this research can provide effective technical support for 3D laser scanning in monitoring the operation phase of bridge jacking. [ABSTRACT FROM AUTHOR]

Cables are important components of long-span bridge structures, whose operation is significantly affected by cable force changes. Nowadays, cable force testing is performed by physical methods; that is, sensors are installed on the cable structure to monitor its force changes. Obviously, this strategy requires an extensive amount of time to achieve cable force calculation, which makes it impossible to monitor the force of the cable structure in real time. Meanwhile, smartphones have attracted extensive attention in the field of structural health monitoring (SHM) because of their higher cost-effectiveness than accelerometers, which include price and lifespan. Besides, many people own a smartphone, which leads to the possibility of a wider range of applications. Therefore, this paper presents a framework for the rapid estimation of the cable force of long-span bridges based on smartphones-captured video and a template matching algorithm. First, the empirical mode decomposition (EMD) method with wavelet decomposition (WD) method, that is, the EMDWD model, is constructed to extract the vibration signal of the bridge cable by eliminating the effects of smartphone vibration and environmental noise on the measured dynamic displacement, thus effectively improving the accuracy of data processing. In addition, the vibration identification model of bridge cable based on a template matching algorithm is established, and the deformation curve of cable is obtained. Finally, the frequency of bridge suspender is calculated by the Fourier transform method (FFT), and the cable force is estimated based on the smartphone-captured video. [ABSTRACT FROM AUTHOR]

Recently, the demand for high-precision balancing of rotors has increased in the automobile industry, as more rotors are designed to rotate at ever-higher speeds to maximize energy efficiency. The accumulation of measurement uncertainty in the balancing process decreases the accuracy of the unbalanced mass estimation, which is the ultimate goal of balancing. Here, the problem of uncertainty is shown through a Monte Carlo simulation of signals acquired from an actual production line. To reduce the effect of measurement uncertainty in the balancing procedures, a signal-processing technique that increases the dynamic reliability of the signal is proposed. The suggested method is based on density-based spatial clustering of applications with noise (DBSCAN) with the use of the orthogonality-based averaging method. Specifically, by adjusting radius values while clustering samples through the use of the DBSCAN method, the outliers that arise due to uncertainty are successfully removed. In this work, our proposed automatic-adaptive DBSCAN (AA-DBSCAN) method is validated by applying it to a balancing machine used for blower rotors in fuel cell electric vehicles. The results show that the deviation of the extracted influence coefficients is up to 0.0050, whereas the proposed method reduced it to less than 0.0037. In addition, the suggested procedure reduced the deviations of the unbalanced mass phase estimation by 35.2% as compared to the results found by the conventional method. Consequently, through the validation test, the suggested method was found to have the largest vibration decrease of any method considered in the study. [ABSTRACT FROM AUTHOR]

Summary: Estimating modal parameters requires significant user interaction, especially when parametric system identification methods are used and the physical modes are selected in the stabilization diagram. In this paper, a fast density peaks clustering algorithm combined with the covariance‐driven stochastic subspace identification method is used to automatically identify modal parameters. Before the automatic identification process, the spurious modes from the stochastic subspace identification method were eliminated by a two‐stage method, including using the soft and hard verification criteria to remove spurious modes in the first stage and the removal of spurious modes based on the stability of physical modes in the second stage; thus, a better stabilization diagram was obtained for the subsequent automatic identification. Furthermore, fast density peaks clustering algorithm was applied to select the appropriate structure modes from the stabilization diagram. In the entire identification process, no user participation was required. The proposed method was demonstrated on a 4‐degree of freedom (DOF) numerical model and a benchmark frame structure, and the results indicated that the modal parameters can be identified accurately even with the noise effects using the default user‐defined parameters. This method showed higher efficiency and universality than the existing methods. Finally, the applicability and robustness of the proposed method in automated operational mode tracking were verified on a real cable‐stayed bridge. [ABSTRACT FROM AUTHOR]

Summary: The identification of traffic loads, including the axle weight (AW) and the gross vehicle weight (GVW) of vehicles, plays an important role in bridge design refinement, safety evaluation, and maintenance strategies. Bridge weigh in motion (BWIM) is a promising technique to weigh vehicles passing through bridges. Though the state‐of‐the‐art BWIM can accurately identify the GVW, unacceptable weighing errors are reported when identifying the AW of vehicles, particularly for those with closely spaced axles. To address the axle weighing problem, this paper aims to improve the performance of BWIM in weighing individual axle loads apart from the gross vehicle weight. The work first theoretically analyzes the possible sources of errors of existing BWIM algorithms, which are observational errors residing in the BWIM equation, no constraint imposed on individual axle loads, and ill‐conditioned nature. Accordingly, three measures are taken to establish a novel robust BWIM algorithm, which is based on regularized total least squares, as well as imposing constraints on the relationship between axles. To validate the proposed algorithm, a series of weighing experiments are carried out on a high‐fidelity vehicle‐bridge scale model. The corresponding results indicate that the proposed BWIM algorithm significantly outperforms other existing BWIM algorithms in terms of the accuracy and robustness of identifying individual axle weight, while retaining satisfactory identification of the gross vehicle weight as well. [ABSTRACT FROM AUTHOR]

Summary: In this paper, different variants of Kalman filter based algorithms extended Kalman filter (EKF), two‐stage EKF, and unscented Kalman filter (UKF) are used to estimate and compare the performance of these algorithms in identifying the state and parameters of nonlinear hysteretic model, simulating unbonded fibre reinforced elastomeric isolator (U‐FREI). A two‐story masonry building supported on U‐FREI is utilized for the present study. The model is excited on a shake table using recorded excitations and measured responses are considered for the identification study. Since the hysteretic behavior of U‐FREI is different from that of conventional steel reinforced elastomeric isolator, U‐FREI is modeled using extended Bouc–Wen model. The considered model comprises of mainly six parameters and the identified parameters as obtained from various considered algorithms as well for different input excitations are observed to be different. In view of this, percentage error index (PEI) has been introduced to compare the performance of these algorithms. PEI is evaluated using the parameter weights obtained from parameter sensitivity analysis in the form of spider diagram. The state and parameters of the U‐FREI are also identified using noise corrupted responses from a numerically simulated model of the same considered two‐story masonry building. The responses are contaminated with varying level of artificially added Gaussian white noise. It is observed that the performances of these algorithms are quite stable and are unaffected due to artificially added noise. Further, studies have also been carried out for proper selection of measurement noise covariance and a significant influence on the performance of these algorithms are observed. [ABSTRACT FROM AUTHOR]

Summary: Knowledge of the camera motion is important in the applications of structural dynamic response measurement using a vision‐based approach because in most of the field measurements, such motion can be non‐trivial and the accuracy of dynamic response measurements is strongly affected by the camera motion. This paper presents a new framework for camera motion estimation and vision‐based displacement measurement, which greatly lowers the barriers to the application of generally positioned cameras from strictly stationary cameras. The contributions in this paper are twofold. First, camera motion as well as reconstructed structural displacement are calculated based on reference planar structures visible in a given scene. In this case, a homography is more effective at describing view changes and planar geometric constraints can be incorporated early in the reconstruction process, thereby improving the quality and effectiveness of the estimates. The second contribution is that the homography of the planar structure is estimated by a newly proposed tracking algorithm that combines RANSAC algorithm and Efficient Second‐order Minimization (ESM) technique, which refines the final estimates to sub‐pixel accuracy and avoids tracking drift and non‐smoothness effectively. Experimental results indicate that the quality of the camera motion estimation and displacement reconstruction can be significantly improved by the judgmatical use of the proposed algorithm for planar structure homography estimation. Furthermore, nonlinear structural system identification is carried out to additionally verify the proposed algorithm using unscented Kalman filter. [ABSTRACT FROM AUTHOR]

Summary: This paper develops a trend line‐based algorithm for signal decomposition in which the adjusted Savitzky–Golay filter is utilized to initiate the decomposition process. In this line, the proposed algorithm determines some special trend lines, mainly composed of the natural frequency of a bridge. An easy‐to‐implement algorithm is then provided to formulate this process and to decompose the given signal into its components in a systematic way. Additionally, a residual signal is generated by the proposed algorithm to store the detected noise and to reconstruct the original signal. To verify the proposed algorithm in the field of bridge health monitoring, a set of numerical and experimental examples are offered in which the proposed algorithm is employed to decompose the signal and provide the constituent components. Moreover, the application of the proposed algorithm in damage localization of the bridge is addressed in the appendix using a simply supported bridge under a moving vehicle. Finally, the bridge example is solved by empirical mode decomposition, as a promising benchmark method, to further illustrate the accuracy of the results and compare them in detail. [ABSTRACT FROM AUTHOR]

Summary: In this paper, a novel distance‐based slicing (DBS) algorithm is proposed for the visualization of inclusion defects within a thick solid structure. The algorithm is based on the propagation distance traveled by the ultrasonic wave signals and was integrated with the generation laser scanning ultrasonic propagation imaging (UPI) system in which the fixed sensor was positioned to detect the induced through‐the‐thickness ultrasonic signals. The advantages provided by the UPI system allow for an in‐situ non‐destructive evaluation for real world applications. An investigation of an isotropic solid specimen of large thickness with multiple internal inclusion defects of different shapes and depth location was performed using the proposed algorithm and system. The results in the form of ultrasonic propagation freeze frames show that the algorithm is much more effective in detecting each individual defect and is particularly useful for a thick‐walled specimen. Additionally, a relationship was determined between the visualized result of each defect and the source position during data acquisition which enabled us to estimate the depth location of the defect within the specimen. [ABSTRACT FROM AUTHOR]

Summary: Rapid health assessment of essential buildings such as hospitals, fire stations, and large residential complexes is crucial after damaging earthquakes. The use of advanced technologies such as wireless sensors, learning algorithms, and signal processing methods became more attractive in such fast applications due to their higher reliabilities and efficiencies compared to the conventional visual inspection methods. This paper presents a robust post‐earthquake damage detection framework for predicting the extent and location of damage occurrence in the braced‐frame structures after an earthquake. To do so, features derived from acceleration response of the structure were used along with a classification learner to determine the health condition of the structure. Decision tree classifiers are used for the purpose of damage classification where the Bayesian optimization algorithm is implemented to optimize the architecture of the mentioned classifier. A one‐story chevron steel‐braced frame, a three‐story X‐braced steel‐frame, and a five‐story three‐dimensional building are considered to validate the proposed method. The total number of 3774 and 1887 nonlinear response history analyses were respectively performed for 2D and 3D numerical models under scaled SAC motions, using the OpenSees simulation platform. Furthermore, in order to simulate the field condition, a maximum level of 10% white Gaussian noise is added to the output signals. Results obtained from the three case studies show that the proposed framework is robust and reliable in predicting the extent of damage level in the braced‐frame structures in a short time after an earthquake. [ABSTRACT FROM AUTHOR]

Image-based bridge displacement measurement still suffers from certain limitations in outdoor implementation. Each of these limitations was addressed in this study. (1) The laser spot is difficult to identify visually during the object distance (OD: mm) measurement using a laser rangefinder, which makes the scale factor (SF: mm/pixel) calibration tricky. To overcome this issue, a stereovision-based full-field OD measurement method using only one camera was suggested. (2) Sunlight reflected by the water surface during the measurement causes light spot interference on the captured images, which is not conducive to target tracking. A network for light spot removal based on a generative adversarial network (GAN) is designed. To obtain a better image restoration effect, the edge prior was novelly designed as the input of a shadow mask-based semantic-aware network (S2Net). (3) A coarse-to-fine matching strategy combined with image sparse representation (SR) was developed to balance the subpixel location precision and efficiency. The effectiveness of the above innovations was verified through algorithm evaluation. Finally, the integrated method was applied to the vibration response monitoring of a concrete bridge impacted by the traffic load. The image-based measurement results show good agreement with those of the long-gauge fiber Bragg grating sensors and lower noise than that of the method before improvement. [ABSTRACT FROM AUTHOR]

Summary: Modal parameter variation plays an important role in bridge health monitoring. But tracking the long‐term variation of modal parameters is restricted by the modal identifiability. Since some modes cannot be identified due to the limitations of sensor locations or excitation conditions, the identified modes will be missing or misclassification in the tracking process. In this paper, the multistage tracking technique based on subspace correlations is proposed. To avoid missing the identified modes, the reference mode list should be complete to cover all identified modes. Thus, the first stage is to update the reference list adaptively, where the correlations between the observability vectors of identified modes and the subspaces of existing reference modes are taken. The second stage is to cluster each traceable mode and the specified reference mode together without misclassification, where the modal similarity is measured by the similarity of observability vectors (SOV). The proposed algorithm is verified by the vibration data of a numerical model and a highway bridge, respectively. The results show that the proposed method can link with modes in the same order correctly and update the reference mode list adaptively to avoid missing the identified modes. [ABSTRACT FROM AUTHOR]

Summary: The analysis of the dynamic behavior of wind turbines is a complex task. These structures present diverse configurations, dependent on the wind conditions. Consequently, the dynamic behavior is highly dependent on their operating conditions. This paper analyzes the dynamic behavior of a 2.0 MW wind turbine during 1 year of operation. In that sense, post‐processing tools aiming to decompose the measured vibration response of structures into modal responses, originally developed to be applied with the SSI‐DATA algorithm, are implemented and applied. Initially, an original data processing that permits the application of these tools to two additional output‐only modal identification algorithms is introduced. Then, these tools are applied for the first time to data collected in a wind turbine during a period of 1 year. The obtained results evidenced the effectiveness of the proposed post‐processing procedure and the distinct dynamic response of the wind turbine according to its operating conditions. [ABSTRACT FROM AUTHOR]

Summary: Constraint minimum variance unbiased estimator is improvised for simultaneous input and parameter estimation of the Bouc–Wen hysteretic system of a full‐scale bridge pier. The essential structural properties in this case (i.e., bounded input and bounded output) are incorporated in the form of constraint conditions along with degradation and pinching effects. To demonstrate the efficiency of this algorithm, two different types of sigma points generation schemes (viz., unscented transformed points and cubature quadrature points) are presented here. First, the algorithm is validated using simulated response obtained from a single‐bay single‐storey reinforced concrete frame, whose hysteretic parameters are obtained by minimizing the error between the model and experimentally observed load‐deformation characteristics. The rationale behind the use of experimentally observed hysteresis in simulation is to replicate the actual structure so that the realistic material behaviour is maintained. Once the algorithm is validated, its performance is further demonstrated using experimental results of a full‐scale bridge pier, which is the main objective of this study. The numerical results presented in this paper clearly show the robustness of the proposed algorithm using unscented sigma points, which can identify both hysteretic properties and input excitation with sufficient level of accuracy. [ABSTRACT FROM AUTHOR]

Summary: Mode shapes have been playing a vital role in the research and application of bridge structural health monitoring. This paper presents a novel indirect method identifying bridge mode shapes using dynamic responses of a tractor–trailer vehicle model, which consists of one tractor and three instrumented trailers. In an effort to eliminate the road roughness effect, accelerations of adjacent trailers are firstly subtracted. Wavelet analysis is then employed to identify bridge mode shapes from the subtracted accelerations in an iterative manner. Furthermore, wavelet denoising algorithm is adopted to improve the identification accuracy in the presence of measurement noise. Systematic numerical simulations, in which a tractor–trailer model passes over an expressway bridge, are conducted in order to investigate the performance of the proposed method. Sensitivity analysis including vehicle speed, class of road roughness, and noise level are studied in this numerical investigation. Results demonstrate that the proposed method is able to identify bridge modal frequencies and mode shapes with satisfactory resolution, accuracy, and robustness. [ABSTRACT FROM AUTHOR]

Summary: In this article, a novel algorithm is proposed for real‐time system identification using hierarchical interhealing model classes. One major difficulty in the system identification for large structures is to determine the complexity of the structural model and the number of unknown parameters. One can detect finer damages with more unknown stiffness parameters, but this may cause fluctuating or even unidentifiable results. Although Bayesian model class selection allows one to choose among some prescribed model classes, the number of possible model classes for large structures is huge. In this paper, we propose a new method using hierarchical interhealing model classes. The modeling errors of these model classes can be corrected adaptively according to the data and the results from the optimal model class. This includes not only the errors in the parameters but also the deficiencies of the parametric models. Furthermore, the model classes are established in a hierarchical manner so that the proposed strategy requires only a small number of model classes, yet being able to explore a large solution space. Consequently, the proposed algorithm can handle a large number of damage possibilities while it maintains relatively low computational cost. Two examples are presented, and the results show that the proposed algorithm can detect, locate, and quantify damages reliably and efficiently. [ABSTRACT FROM AUTHOR]

Summary: This paper proposes a practical solution to control a problem of a class of continuum mechanics systems in which spatial equations corresponding to vibrational motions are not resolvable through conventional analytical approaches. Control scheme is constructed based on discrete sliding mode technique regarding a rigid model of a system such that control inputs are selected between Lyapunov stability bounds to manipulate the system in tracking reference values. The stability bounds and control input signal proximity to either bounds are updated in every simulation step in accordance to system vibration level using a practical acceleration synchronization method aside from evolution of rigid model states. To this end, the severity of vibrations is analyzed online using a limited number of acceleration sensors. These sensors are installed on critical nodes that are generally characterized with high level of vibrations, and the synchronization process is employed through comparative analysis with other nodes of which exhibit more rigidity within the structure. In order to highlight controller performance, virtual plant is assumed to be constructed from viscoelastic materials (VEMs) featuring time‐varying elasticity and viscosity, and Prony series parameters are involved in modeling VEMs in ANSYS® mechanical APDL student edition. Eventually, controller capability in stabilization of closed‐loop system and tracking reference values are evaluated in finite element analysis transient environment, and simulation results are compared with those of existing methods. [ABSTRACT FROM AUTHOR]

Summary: The tracking performance of an actuation transfer system in a real‐time hybrid simulation (RTHS) frequently faces accuracy and robustness challenges under constraints and complicated environments with uncertainties. This study proposes a novel control approach based on the deep deterministic policy gradient algorithm in reinforcement learning (RL) combined with feedforward (FF) compensation, which emphasizes the implementation of shaking table control and substructure RTHS. The proposed method first describes the control plant within the RL environment. Then, the agent is trained offline to develop optimized control policies for interaction with the environment. A series of validation tests were conducted to assess the performance of the proposed method, starting with the dynamic testing of underwater shaking table control and then a virtual RTHS benchmark problem. For complex systems, such as controlling the underwater shaking table, the proposed algorithm, FF, and adaptive time series (ATS) compensation methods are compared under various water depths and motions. The results show better performance and wider broadband frequency applicability under different shaking table dynamic‐coupling effects. Next, a controller based on the proposed method was designed by extending the virtual RTHS via the configuration of the control plant and substructure division, as provided in the RTHS benchmark problem. The proposed RL controller also improved the tracking accuracy and robustness of conventional FF compensators against unmodeled dynamics and perturbation uncertainties. This controller can be extended to further advanced control strategies as a component of model‐based control methods. [ABSTRACT FROM AUTHOR]

Summary: In this paper, an observer‐based feedback/feedforward model predictive control (MPC) algorithm is developed for addressing the active vibration control (AVC) of lightly damped structures. For this purpose, the feedback control design process is formulated in the framework of disturbance rejection control (DRC) and a repetitive MPC is adapted to guarantee the robust asymptotic stability of the closed‐loop system. To this end, a recursive least squares (RLS) algorithm is engaged for online estimation of the disturbance signal, and the estimated disturbance is feed‐forwarded through the control channels. The mismatched disturbance is considered as a broadband energy bounded unknown signal independent of the control input, and the internal model principle is adjusted to account for its governing dynamics. For the sake of relieving the computational burden of online optimization in MPC scheme, within the broad prediction horizons, a set of orthonormal Laguerre functions is utilized. The controller design is carried out on a reduced‐order model of the experimental system in the nominal frequency range of operation. Accordingly, the system model is constructed following the frequency‐domain subspace system identification method. Comprehensive experimental analyses in both time‐/frequency‐domain are performed to investigate the robustness of the AVC system regarding the unmodeled dynamics, parametric uncertainties, and external noises. Additionally, the spillover effect of the actuation authorities and saturation of the active elements as two common issues of AVC systems are addressed. [ABSTRACT FROM AUTHOR]

Summary: The unscented Kalman filter (UKF) is often used for nonlinear system identification in civil engineering; nevertheless, the application of the UKF to highly nonlinear structures could not provide accurate results. In this paper, an improvement of the UKF algorithm has been adopted. This methodology can consider state constraints, and it can estimate the measurement noise covariance matrix. The results obtained adopting a modified UKF have been compared to the ones obtained using the UKF for parameter estimation of a single degree of freedom nonlinear hysteretic system. The second part of this work shows results of an experimental activity on a base‐isolated prototype structure. Both numerical and experimental results underline that the adopted algorithm produces better state estimation and parameter identification than the UKF, being capable of taking into account parameter boundaries. The adopted algorithm is more robust than the standard UKF in the case of measuring noise variation. [ABSTRACT FROM AUTHOR]

Summary: In this study, the Q‐learning algorithm is integrated into the binary water strider algorithm to adaptively control the search operators and repair strategies. The proposed algorithm (QWSA) is applied to optimal sensor placement in structures with numerous candidate locations. To handle the constraint related to the number of sensors, an efficient strategy based on the triaxial driving‐point residue (DPR3) coefficient is developed, in addition to the random solution repair strategy. The reinforcement learning algorithm with softmax policy determines which strategy should be adopted for the generated solutions. Since the utilized sensors are triaxial, the tridimensional modal assurance criterion (TMAC) is applied as the objective function. To verify the effectiveness of the proposed method, two large‐scale dome‐shaped trusses are investigated, and the results are compared with the basic water strider algorithm and several other well‐known and modern metaheuristics in the literature. The results demonstrate that placements obtained by QWSA have in average lower than half of the cost value obtained by compared methods. Additionally, average DPR3 (DPR3¯) of the QWSA is higher than the rest of the algorithms. The Wilcoxon test indicates that, at a 95% confidence level, the results of QWSA are superior to all algorithms. Additionally, the proposed method outperforms them in terms of convergence speed. [ABSTRACT FROM AUTHOR]

Summary: In recent times, esthetic and functionality requirements have caused many buildings to be asymmetric. An asymmetric building can be defined as one in which there is either geometric, stiffness, or mass eccentricity. Such buildings exhibit complex vibrations as there is coupling between the lateral and torsional components of vibration and are referred to as torsionally coupled buildings. These buildings require 3‐dimensional modeling and analysis. Despite recent research and successful applications of damage detection techniques in civil structures, assessing damage in asymmetric buildings remains a challenging task for structural engineers. There has been considerably less investigation on the methodologies for detecting and locating damage specific to torsionally coupled asymmetric buildings. This paper develops a multicriteria approach using vibration‐based damage indices for detecting and locating damage in asymmetric building structures. These vibration indices are based on the modified versions of the modal flexibility and the modal strain energy methods. The proposed procedure is first validated through experimental testing of a laboratory scale asymmetric building model. Numerically simulated modal data of a larger scale asymmetric building obtained from finite element analysis of the intact and damaged asymmetric building models are then applied into the modified modal flexibility and modal strain energy algorithms for detecting and locating the damage. Results show that the proposed method is capable of detecting both single and multiple damages in the beams and columns of asymmetric building structures. [ABSTRACT FROM AUTHOR]

Summary: In optimization of multiple tuned mass dampers (MTMDs), certain restrictions or preconditions such as uniform distribution of stiffness, mass, or frequency spacing had been applied for simplification, but in turn, solution of individual stiffness and damping parameters are not the true optima. The main purpose of this paper is to obtain the true optima of individual stiffness and damping parameters of MTMD system. In the proposed method, parameters of TMD units are treated as free search optimization variables, and an efficient optimization algorithm, namely, artificial bee colony algorithm has been utilized in obtaining optimum parameters of MTMDs. Performance proposed method with respect to uncontrolled structure is verified through numerical analyses and compared with other reported methods. Comparisons show a superior performance of proposed approach. The basic properties of optimum design, the effectiveness, and robustness with respect to number of dampers are also discussed. [ABSTRACT FROM AUTHOR]

Summary: This paper proposes an identification framework based on a restricted Boltzmann machine (RBM) for crack identification and extraction from images containing cracks and complicated background inside steel box girders of bridges. The original images that include fatigue crack and other background information are obtained by a consumer‐grade camera inside the steel box girder. The original images are cut into a number of elements with small size as the input dataset, and a state representation vector is artificially labeled to every image element used for the crack identification. A deep learning model or network consisting of multiple processing RBM layers to learn the abstract features is constructed to match the input image elements with corresponding state representation vectors. Next, a three‐layer RBM with 500; 500; and 2,000 hidden units is trained as the hidden layers in the deep learning network. A contrastive divergence learning algorithm is employed for training the deep network to update and obtain the optimal parameters (i.e., the biases and weights). The new input image elements labeled as crack are sorted out and assembled to form an output image. A deep network is modeled through the consumer‐grade camera images containing cracks and complicated background information using the proposed approach. The accuracy and ability to identify cracks from new images with different resolutions using the trained deep network are validated. Furthermore, effects of element size on reconstruction error and identification accuracy are investigated. The results show that there exists optimal element size; that is, too small and too large element sizes both increase the reconstruction error and decrease the identification accuracy. [ABSTRACT FROM AUTHOR]

Vibration measurements provide useful information about a structural system's dynamic characteristics and are used in many fields of science and engineering. Here, we present an alternative noncontact approach to measure dynamic displacements of structural systems using digital videos. The concept is that intensity measured at a pixel with a fixed (or Eulerian) coordinate in a digital video can be regarded as a virtual visual sensor. The pixels in the vicinity of the boundary of a vibrating structural element contain useful frequency information, which we have been able to demonstrate in earlier studies. Our ultimate goal, however, is to be able to compute dynamic displacements, i.e., actual displacement amplitudes in the time domain. In order to achieve that, we introduce the use of simple black-and-white targets that are mounted on locations of interest on the structure. By using these targets, intensity can be directly related to displacement, turning a video camera into a simple, computationally inexpensive, and accurate displacement sensor with notably low signal-to-noise ratio. We show that subpixel accuracy with levels comparable to computationally expensive block matching algorithms can be achieved using the proposed targets. Our methodology can be used for laboratory experiments, on real structures, and additionally, we see educational opportunities in K-12 classroom. In this paper, we introduce the concept and theory of the proposed methodology, present and discuss a laboratory experiment to evaluate the accuracy of the proposed black-and-white targets, and discuss the results from a field test of an in-service bridge. [ABSTRACT FROM AUTHOR]

Artificial neural networks (ANN) are extensively utilized in structural health monitoring. Nevertheless, the definition of a rigorous method for the optimization of their structure is still an unresolved issue, especially when applied to safety critical systems. In this paper, an approach typically adopted in the design of experiments and based on the analysis of variance (ANOVA) is used to statistically determine the number of hidden neurons in a three-layer ANN structure. Repeated trainings of the same network structure provide multiple observations of the performance index here, based on the root mean square error. Different levels of network structure complexity are statistically compared, based on the number of hidden nodes. ANOVA is used to determine whether there is statistical evidence that the network performance is influenced by the number of hidden nodes. This analysis allows defining the threshold number of hidden nodes above which there is no statistical evidence of a performance benefit by the increase of the ANN structure complexity. The method is applied to the optimization of a set of algorithms for the diagnosis of fatigue damage on a typical aeronautical structure, consisting of a metallic panel with a riveted skin-stringer construction. The ANNs for damage detection, localization, and quantification are trained and validated with finite element simulated strain data and are finally tested on experimental strain signals, acquired in real-time in a fatigue crack growth laboratory test program including a skin crack artificially initiated in a panel bay and two stringers that had failed naturally under fatigue load. [ABSTRACT FROM AUTHOR]

Structural damage detection by non-destructive techniques has been investigated widely by researchers in recent years, and several response-based Structural Health monitoring techniques have been developed. In this paper, a vibration-based damage detection algorithm through model updating utilizing incomplete frequency response function and modal data is presented. First, a modified sensitivity equation is presented to relate the changes of mode shapes to the changes of unknown structural parameters. Then, the developed equations are used to correlate changes of frequency response functions to the changes of structural parameters. Parameter estimation is conducted through least square solution of sensitivity equations. In order to prove the capability of the proposed method for structural damage detection purpose, a numerical example using error contaminated data is presented. Results exhibit robustness of method against measurement and mass modeling errors. [ABSTRACT FROM AUTHOR]

Summary: To determine the degree of fire damage in reinforced concrete (RC) T‐beams, a damage identification method based on an improved support vector machine and firefly algorithm (FA‐SVM) is proposed herein. First, based on the fire test of 10 simply supported beams, a refined finite element model of simply supported T‐beams was established. A modal analysis of the simply supported beams was performed to obtain a sample library of the input and output parameters for the FA‐SVM identification network. Subsequently, the trained FA‐SVM identification network was used to predict the fire exposure duration of the samples. The sectional temperature during the fire exposure duration could be calculated by using the finite element model, and the bending stiffness and bending capacity of the simply supported beams after exposure to the fire were calculated. The test sample results were similar to the experimental results of the simply supported beams exposed to fire for 60, 90, and 120 min, which demonstrated the feasibility and effectiveness of the proposed method. Finally, a three‐step method for fire damage identification suitable for RC continuous beams was developed based on the FA‐SVM identification network. An example calculation analysis of three‐span continuous beams was performed, and the results demonstrated accuracy of the identification results. The identification sample magnitude was significantly reduced using this method, which can be conveniently used in practical engineering applications. [ABSTRACT FROM AUTHOR]

This paper presents output-only observer/Kalman filter identification (O3KID), an effective method for the identification of the dynamic model of a structure and its underlying modal parameters using only output time histories measured on the field. The method is suitable for structural health monitoring based on modal parameters, in particular, for those civil infrastructures whose excitation is random in nature and in the way that it is applied to the structure (e.g., wind and traffic) and therefore is difficult to measure. O3KID is based on a linear-time-invariant state-space model and is derived from an established and successful approach for input-output system identification, known as observer/Kalman filter identification. The paper rigorously proves the applicability of the approach to the output-only case, presents the resulting new algorithms, and demonstrates them via examples on both numerical and experimental data. Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

In the traditional extended Kalman filter approach, unknown structural parameters are included in the extended state vector. Then, the sizes of the extended state vector and the corresponding state equation are quite large, and the state equation is highly nonlinear with respect to the extended state vector. This may cause identification divergent for a large number of unknown parameters. Also, such strategy requires large computational effort and storage capacities, which is not appropriate for intelligent structural damage detection implemented by smart sensors with microprocessors. In this paper, an algorithm based on a two-step Kalman filter approach is proposed to remove the aforementioned drawbacks of the traditional extended Kalman filter. In the first step, recursive estimation of structural state vector is derived by Kalman filter with assumed structural parameters. In the second step, structural parameters and the updated structural state vector are estimated by the Kalman filter and the recursive estimation in the first step. Thus, the number of estimated variables in each step is reduced, which reduces the computational effort and storage requirements. This superiority is important for intelligent structural damage detection implemented by smart sensor in wireless sensor network. The proposed algorithm is first validated by numerical simulations results of structural damage detection of the phase-I 3-D ASCE benchmark building for structural health monitoring, a 30-story shear building with minor damage, and an experimental test of damage detection of a lab multistory frame model. Then, it is applied to structural damage detection of a lab multistory model-employed smart sensors embedded with the proposed algorithm. Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

SUMMARY This paper proposes the use of vertical accelerations of a moving train to detect local track irregularities produced by weaker sections of the infrastructure. By analysing the vehicle accelerations using the wavelet transform, it is possible to clearly identify the location of damaged sections. A wavelet-based indicator is proposed to facilitate an algorithm for recognition of deteriorated segments. The proposed indicator is validated by means of a 2-DOF vehicle model excited by randomly generated track irregularities together with the presence of various local track defects. A Monte Carlo analysis is performed to evaluate the performance of the proposed indicator for a variety of model parameters, including vehicle mechanical properties, shape of isolated track irregularities and levels of damage. Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

SUMMARY Sensor placement is an integral component in the design of an effective and reliable structural health monitoring system for large-scale civil engineering structures. In this paper, a novel hybrid optimization algorithm, that is, immune monkey algorithm (IMA), by combining the monkey algorithm with the immune algorithm, is proposed to solve the optimal sensor placement problem. In the proposed algorithm, the dual-structure coding method is designed and adopted for the representation of design variables and a chaos-based approach is presented to initialize the monkey population. In addition, some improved strategies such as the advanced climb process, immune selection, clonal proliferation, and immune renewal are defined to maintain population diversity and inhibit degeneracy during evolution. The effectiveness and reliability of the proposed IMA is demonstrated by designing the sensor arrays of the Canton Tower, the second highest TV tower in the world. Numerical investigations presented clearly suggest that the proposed hybrid IMA outperforms the original monkey algorithm implementations both in terms of generating optimal solutions as well as faster convergence. The proposed algorithm can easily be extended to other multiple global optimization problems. Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

SUMMARY Computational capability of wireless sensor network (WSN) significantly facilitates application of dense sensory arrays, which is increasingly important in health monitoring of large-scale structural systems. As the wireless sensor technology improves, more complicated tasks can be assigned to sensing units, and the communication between sensing nodes and their base station can be minimized, utilizing in-network processing. This strategy should be used to address WSN challenges, such as limited communication bandwidth and prohibitive power consumption, associated with wireless communication and battery power. An iterative modal identification algorithm is proposed in this paper, which uses the on-board processors for estimation of system parameters through iteration cycles. The iterative algorithm was originally developed such that each individual sensor, having an initial estimate of the system parameters, its local measurement, and the excitation signal, updates the estimated model and passes it through the network until convergence. This study further improves the algorithm to eliminate its limitations in need for availability of excitation load and initial estimate of the system parameters. As a result, the algorithm is applicable for modal identification of structural systems under ambient loading without need for prior information about the system parameters. The development of the algorithm is presented in this paper and validated through implementation on a numerically simulated example and a laboratory experiment. Furthermore, its performance is evaluated using data from an ambient vibration test of the Golden Gate Bridge using a WSN. Results of these implementations verify the functionality of the algorithm in monitoring of real-life structural systems. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

In this paper, a mode shape curvature-based method for detection and localization of damage in plate-like structures is described. The proposed algorithm requires only the mode shape curvature data of the damaged structure. The damage index is defined as the absolute difference between the measured curvature of the damaged structure and the smoothed polynomial representing the curvature of the healthy structure. To examine the advantages and limitations of the proposed method, several sets of numerical simulations are carried out. Simulated test cases considering different levels of damage severity, measurement noise and sensor sparsity are studied to evaluate the robustness of the method under the noisy experimental data and limited sensor data. Applicability and effectiveness of the proposed damage detection method are further demonstrated experimentally on an aluminium plate containing mill-cut damage. The modal frequencies and the corresponding mode shapes are obtained via finite element models for numerical simulations and by using a scanning laser vibrometer for the experimental study. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

As a low cost and maintenance energy-absorbing device, tuned liquid damper (TLD) is being widely used to suppress structural vibration. In this paper, the real-time hybrid simulation (RTHS) is employed to investigate the performance of TLD for controlling seismic responses of actual multi-story structures, where the TLD is experimentally modeled as physical substructure and the multi-story structure is numerically simulated as numerical substructure. Taking advantage of RTHS technique, a methodology for achieving full-scale TLD experiments is developed through suitable similarity design; a new interaction force measurement method and a new dual explicit algorithm are embedded into RTHS system to enhance simulation capability. First, the seismic response of a two-story structure installed with TLD is analyzed by applying RTHS. Correspondingly, the pure shaking table test is performed to assess the accuracy of the RTHS. Then, the effectiveness of TLD for structures with various numbers of floors and different structural properties is tested through RTHS by varying the simulation model of the numerical substructure. Finally, the effects of mass ratio and structural damping ratio on a given TLD-structure system are investigated. Copyright © 2015 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Summary: Considering the adjustments for sensor selections attributed by different finite element (FE) grids, a hybrid optimization algorithm including FE grids updating for optimal sensor placement is proposed based on effective independence method and sensor distribution index, in order to improve the algorithm efficient and reduce the redundancy information simultaneously. This study takes into account of the relationship of FE grids, redundancy information, and optimal sensor placement performance by detailed statements. Furthermore, in order to reduce the redundancy information caused by too fine FE grids and too near measurements, the proposed sensor distribution index can synthetically deal with both cases of the nearer nodes and overall sensor distribution ranges. Moreover, by means of normalization and weighted factor, the constituted fitness function, which consists of Fisher information and sensor distribution index, can be more competitive by eliminating the gap of orders caused by the different fitness functions. Therefore, the economical FE grids are suggested, and the corresponding optimum sensor locations are simultaneously obtained by the hybrid optimization algorithm. Finally, the proposed method is verified by a deployable antenna module of space solar power satellite and a wing of reusable launch vehicle, respectively. [ABSTRACT FROM AUTHOR]

Summary: The single‐component adaptive Metropolis (SCAM) algorithm, as one of the Markov chain Monte Carlo (MCMC) sampling methods, is very effective at solving high‐dimensional posterior joint probability distributions. However, studies show that the SCAM algorithm is prone to generating duplicate samples, thus lowering the sampling efficiency and causing large calculation errors. To solve this problem, a modified SCAM algorithm is proposed in the present study. In the modified algorithm, the expression of the variance of the proposal distribution is redefined so that the Markov chain formed by the sample sequence is relatively stable. With the obtained structural response, the posterior joint probability distribution of the physical parameters of the structure is achieved using Bayesian theory, which is then combined with the modified SCAM algorithm to solve the posterior marginal probability distribution and the optimal estimates. Hence, the structural damage is identified. The validity, reliability, and practicability of the modified SCAM algorithm are verified through theoretical analysis, a numerical simulation of the structural example, and a shaking table test. The results show that the modified SCAM algorithm not only enhances the accuracy of the calculation results but also increases the sampling efficiency. This method can be used as a reference for physical parameter identification and damage assessment of the engineering structures. [ABSTRACT FROM AUTHOR]

Summary: Many existing structures suffer from damage due to age or accumulated damage from hazards. It is important to accurately assess the present conditions of these aging, deteriorating, and damaged structures. In recent years, laser scanners have been increasingly used for capturing the in situ conditions of structures. They are used for collecting dense and high‐resolution point clouds of scenes for structural engineering applications. However, automatically extracting meaningful information from the point clouds remains a challenge, and the current state‐of‐the‐art requires significant user interaction. In this work, a process for automatically extracting information from laser datasets such as the location, orientation, and size of objects in a scanned region, and location of damaged regions on a structure is established. First, widely accepted point cloud processing steps are used to divide the collected laser scanner data into meaningful point clusters. A new graph‐based object detection algorithm is then used to generate skeletons of the extracted point clusters in order to detect structural members by using a model library consisting of common structural shapes. The obtained member information is then used for developing a new graph‐based damage detection method, which compares the fitted object model with the as‐is point cloud of the investigated object for locating defects, detecting alignment issues and points of discontinuity, computing changes in the cross‐section through area calculation, and determining the total volume change on the investigated member. The effectiveness of the developed graph‐based object and damage detection algorithms are tested and validated on test specimens and test‐bed bridges. [ABSTRACT FROM AUTHOR]

SUMMARY In this paper, a new decentralized structural active control algorithm using the instantaneous optimal control scheme is proposed. On the basis of finite element models, large-size structures are divided into smaller-size substructures. Each substructure is controlled by its own local controller; however, interconnection effect between adjacent substructures is taken into account by treating the interconnections as 'additional unknown disturbances' at substructural interfaces to the substructure. By sequential Kalman estimator for the substructural state vector and the recursive estimation of the 'additional unknown disturbances', instantaneous optimal control forces of each local controller are estimated without substructure interface measurements. Then, the proposed decentralized control algorithm is applied to the American Society of Civil Engineers 20-story benchmark control building under earthquake excitation and compared with the control criteria by the conventional centralized control approach. It is shown that the proposed decentralized control provides satisfactory control performances when compared with the conventional centralized control, but it increases the reliability of the control performance in case of the failure of local controllers. Robustness of the proposed decentralized control is also studied. Thus, the proposed decentralized control algorithm is suitable for structural active control of large-size structures. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

SUMMARY This paper presents a new algorithm based on square time-frequency distribution to detect damages in concrete piers of railroad bridges. In addition, two new damage indices are proposed in this research. The algorithm and damage indices are described and corroborated using finite element model of Qotour railroad bridge. To create the finite element model according to the current condition of the Qotour railroad bridge, extensive in situ measurement results and laboratory tests were used. Some seismic damages were considered in the concrete piers of the model. The results indicate that the algorithm and damage indices can identify all damages from severe to small accurately. The proposed algorithm and damage indices are shown to be not sensitive to noise and even in case of noise-polluted response signal are working properly. The proposed algorithm only uses the response signals without the need for recording the excitation forces. In addition, according to the algorithm, there is no need to create an analytical model of the bridge for damage detection. This feature in modeling of bridge structures and especially complex bridge, which is associated with problems, is very useful. Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Summary: The aim of this study is to develop a Hamiltonian Monte Carlo‐based algorithm for finite element model updating in the Bayesian framework. The proposed algorithm uses adaptive prior‐based approach, which helps to generate the intermediate pdfs. Numerical analysis is carried out with different coefficient of variations of the prior for model updating. Guidelines for their proper selection procedure are also prescribed in this work. The efficiency of the proposed method is demonstrated using synthetic experiments and actual test data for updating the finite element model of a steel truss bridge. Finally, performance of this algorithm is compared with the standard Markov chain Monte Carlo algorithm to demonstrate its advantages. [ABSTRACT FROM AUTHOR]

ABSTRACT In this paper, a new decentralized identification method based on the principles of blind source separation (BSS) that can perform modal identification in the presence of narrowband external excitations is presented. This algorithm is based on the principle of sparse BSS involving wavelet transforms to undertake identification for underdetermined static mixtures. Most BSS methods in the context of modal identification assume that the excitation is white and does not contain narrow band excitation frequencies. However, this assumption is not satisfied in many situations when the excitation is a superposition of narrowband harmonic(s) and broadband disturbance, for example, rotating machinery fixed to flexible structures, human-induced vibration in pedestrian bridges, or dynamic interaction with surrounding structures. Under such conditions, traditional BSS methods (such as second-order blind identification) yield sources (modes) without any indication as to whether the identified source(s) is a system or an excitation harmonic. In the proposed method, underdetermined BSS is employed, involving transformation of measurements to the time-frequency domain, resulting in a sparse representation. The statistical characteristics of the sources are estimated using residual bootstrap technique, which are then used to delineate the sources corresponding to external disturbances versus intrinsic modes of the system, in a decentralized fashion. The decentralized architecture is aimed at reducing the cost of instrumentation in both traditional wired and recent wireless applications. The performance of the algorithm is demonstrated using both a numerical study and a bench-scale laboratory experiment. The primary advantage of the proposed method is its ability to identify the modal harmonics of a flexible structure under narrowband excitations, in a decentralized fashion, where traditional BSS algorithms fail to achieve the same goals. Copyright © 2013 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Summary: The equivalent force control (EFC) algorithm is a hybrid seismic testing method based on both an implicit integration algorithm and force feedback control. As it performs the computation of the numerical substructure with a fixed sampling number and some evaluations are not necessary, the EFC method is believed to be time‐consuming for seismic testing of nonlinear systems with complicated numerical substructure model. In order to tackle this problem, the EFC method with varying sampling number (vEFC) has been conceived. The analysis of the vEFC method has shown that 2 traditional pseudodynamic testing (PDT) variants on the basis of implicit time integration schemes and numerical iteration, that is, the IPDT1 method and the IPDT2 method, can be recovered from the vEFC method. Moreover, the advantages of the vEFC method, such as fast response rate and compensation for control errors and possible slippage, are demonstrated. [ABSTRACT FROM AUTHOR]

It is widely acknowledged that the tuned mass damper (TMD) is one of the most effective and simplest passive control devices, but its limited control performance is still a troubling problem. In order to surmount the shortage of TMD, the tuned tandem mass dampers (referred herein to as TTMD) have been proposed for mitigating the undesirable oscillation of structures under the ground acceleration. Based on the formulation of the mode-generalized system in the specific vibration mode being controlled, the analytical expression is then derived for the dynamic magnification factor of the structure furnished with a TTMD. The optimum criterion can thereby be defined as minimization of the minimum values of the maximum dynamic magnification factor with a set of optimization variables embedded so as to give full play to the control device potential. The optimization implementation of TTMD is carried out by the MATLAB-based coding and debugging. For the purpose of a mutual authentication to the optimization results, three metaheuristic algorithms, namely, genetic algorithm, particle swarm optimization, and simulated annealing, are concurrently taken into consideration. Results demonstrate that the proposed TTMD endows with the superior stroke performance with respect to TMD. [ABSTRACT FROM AUTHOR]

An image deformation method is herein proposed to monitor the crack propagation in structures. The proposed approach is based on a computational algorithm that uses displacements measured by photogrammetry or image correlation to generate a virtual image of the surface, from an initial input to any given stage of analysis. This virtual image is then compared with the real image of the specimen to identify any discontinuities that appeared or evolved during the monitored period. The procedure was experimentally validated in the characterisation of crack propagation in concrete specimens. When compared with other image processing techniques, for instance, based on edge detectors, the image deformation approach showed insensitiveness to any discontinuity previously existing on the surface, such as cracks, stains, voids, or shadows, and did not require any specific surface treatments or lighting conditions. With this approach, the global crack maps could be extracted from the surface of the structure and extremely small changes occurring within a given time interval could be characterised, such as the movement associated with the opening of cracks. It is highlighted that the proposed procedure is general and therefore applicable to detect and characterise surface discontinuities in different materials and test set-ups. [ABSTRACT FROM AUTHOR]

Visual inspection procedures remain the primary method of infrastructure assessment throughout the USA, but their shortcomings are numerous. In addition to their widely acknowledged variability and subjectivity, the large scale of civil infrastructure systems presents expensive access and time requirements that constrain the frequency of visual inspections and result in poor temporal resolution, which hampers effective decision-making. To overcome this challenge, the research reported herein aimed to assess the ability of computer algorithms together with imagery collected by unmanned aerial vehicles (UAV) to extract accurate and quantitative information to help inform infrastructure management decisions. Techniques such as homography and lens distortion correction are used in this article in a post-processing framework that allows the use of color images obtained by UAVs for actual damage quantification measurements. The experiments described in this article utilize a UAV with a mounted camera and provide measurements from a representative infrastructure mockup with several simulated damage scenarios. Deformation measurements, change detection (related to structural features and the size of deterioration), and crack pattern identification were all analyzed. The results indicated that the developed post-processing algorithms were able to extract quantitative information from UAV captured imagery. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Summary: The paper presents a method for the estimation of true spectral characteristics and for the analysis of numerical models for guided waves. The proposed procedure makes use of a four parametric logistic distribution, called logistic function, and nonlinear least square fit algorithm. The method requires numerical results obtained for five various discretizations only in order to properly estimate true spectral characteristics. Thereby, the method can be used for any arbitrarily sets of data. A general framework for the proposed analysis is given, along with application examples. Although the proposed method is verified and application examples are given based on the local interaction simulation approach for elastic waves propagation, the proposed methodology can easily be adopted for other numerical methods, for example, finite elements. [ABSTRACT FROM AUTHOR]

Summary: Tuning the seismic control systems in order to achieve optimal performance is a challenging area due to the system and disturbance uncertainties. Although, model uncertainties, process time delay, and actuator dynamics can be considered as typical uncertainties, the main source of uncertainty in a seismic control system comes from the aleatory nature of earthquake disturbances. In this case, tuning of the control system based on a given seismic record may not necessarily result in optimal performance for other earthquakes. In this paper, a methodological approach is proposed for online control of active structural control systems considering seismic uncertainties. For this purpose, the concept of reinforcement learning is utilized for online tuning of an active mass drive system. The controller comprises a gain‐scheduling fuzzy proportional derivative controller whose gains are tuned via an online reinforcement learning algorithm. Moreover, in order to tackle the time delays, a dynamic state predictor is utilized in conjunction with the proposed controller. To evaluate the performance of proposed controller, according to an assumed site hazard, thousand ground motion records are generated and clustered based on their spectral features using a fuzzy clustering approach. Finally, the controller is implemented in a laboratory‐scale structure, and its performance is examined in the presence of the cluster centers and some real seismic records simulated on a shake table. The test results reveal successful performance of the proposed controller in tackling a wide range of seismic disturbances in the presence of time delay. [ABSTRACT FROM AUTHOR]

SUMMARY Traditional passive isolation systems have been shown to provide satisfactory seismic mitigation performance under typical far-field earthquake conditions. However, because of the untunable designated isolation period, the performance of these systems is still strongly affected by the low-frequency resonance phenomenon observed during near-fault earthquakes, which are usually dominated by a long-duration impulse. The use of semi-active isolation systems with variable stiffness, which are usually equipped with a customized control algorithm to determine the isolation stiffness or period in real time, could be a promising solution to overcome this problem. To realize this technology, this study develops a smart isolation system that combines the leverage-type stiffness-controllable isolation system (LSCIS) with a simple fuzzy logic control (FLC). Because the proposed FLC merely requires feedback from the velocity and displacement responses of the isolation base, it is quite easily implemented. Theoretical analysis shows that the extreme displacement of the isolation level caused by near-fault seismic waves can be mitigated by the proposed system with tolerable acceleration response variations compared with that of the traditional passive isolation system. The movement of the pivot point of the LSCIS can also be adjusted to address the inherent hardware constraint that limits its practical application. Experimental verification conducted on a shake table further demonstrates the feasibility of integrating the LSCIS system with FLC after proper consideration of the friction effect generated by the leverage mechanism and the inevitable time delay effect. Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

SUMMARY Structural active control is one of the most efficient systems available to protect building structures from destructive effects of earthquakes. Active control efficiency is highly dependent on control algorithm. One of the most important and challenging components of structural control is the development of an accurate analytical approach having less computational expenses. Block pulse functions have been studied and applied frequently in recent years as a basic set of functions for signal characterizations in systems science and control. The proposed method evolves minimizing computational expenses of analytical approaches by a novel method to suppress the responses of the structure based on block pulse functions. To prove the validity and feasibility of the proposed control method, numerical simulations of earthquake-excited 10-story shear buildings with active tendons are performed, and comparative results are presented. The uncontrolled and controlled responses of structural system are obtained by using the proposed method and compared with linear quadratic regulator method. The results reveal that the proposed method can be beneficial in reducing seismic responses of structures with high accuracy and less computational expenses. Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]