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Due to the random nature of the road excitation and the inherent uncertainties in bridge-vehicle system, damage identification of bridge structure through continuous monitoring under operating situation become a challenge problem. Methods for system identification and damage detection of a continuous two-span concrete bridge structure in time domain is presented using interaction forces from random moving vehicles as excitation. The signals recorded in different locations of the instrumented bridge are mixed with signals from different internal and external (road roughness) vibration sources. The damage structure is also modelled as the stiffness reduction in one of the beam element. For the purpose of system identification and damage detection three different output-only modal analysis techniques are proposed: The covariance-driven stochastic subspace identification (SSI-COV), the blind source separation algorithms (called Second Order Blind Identification) and the multivariate AR model. The advantages and disadvantages of the three algorithms are discussed. Finally, the null-space damage index, subspace damage indices and mode shape slope change are used to detect and locate the damage. The proposed approaches has been tested in simulation and proved to be effective for structural health monitoring.

Structural health monitoring acquires structural information through numerous sensor measurements. Vibrational measurement data render the dynamic characteristics of structures to be extracted, in particular of the modal properties such as natural frequencies, damping, and mode shapes. The stochastic subspace system identification has been recognized as a power tool which can present a structure in the modal coordinates. To obtain qualitative identified data, this tool needs to spend computational expense on a large set of measurements. In study, a stochastic system identification framework is proposed to improve the efficiency and quality of the conventional stochastic subspace system identification. This framework includes 1) measured signal processing, 2) efficient space projection, 3) system order selection, and 4) modal property derivation. The measured signal processing employs the singular spectrum analysis algorithm to lower the noise components as well as to present a data set in a reduced dimension. The subspace is subsequently derived from the data set presented in a delayed coordinate. With the proposed order selection criteria, the number of structural modes is determined, resulting in the modal properties. This system identification framework is applied to a real-world bridge for exploring the feasibility in real-time applications. The results show that this improved system identification method significantly decreases computational time, while qualitative modal parameters are still attained.

Structural health monitoring of concrete structures under seismic loads has always attracted a lot of attention in the earthquake engineering community. In this paper, two tests of structural health monitoring of concrete columns using piezoceramic-based sensors are presented. The first test was a shake table test of a reinforced concrete (RC) column. A piezoceramic-based device, called a 'smart aggregate', was pre-embedded and adopted for the structural health monitoring of the concrete column under earthquake excitations. The second test of this study was the in situ health monitoring of RC piers of Niu-Dou Bridge in Taiwan, under seismic loading. RC piers instrumented with the post-embedded piezoceramic-based sensors were tested using reversed cyclic loading. During the shake table test and the in situ reversed cyclic loading test, one sensor was used as an actuator to generate propagating waves, and the other sensors were used to detect the waves. By analyzing the wave response, the existence of cracks can be detected and the severity can be estimated. The experimental results demonstrate the sensitivity and the effectiveness of the piezoceramic-based approach in the structural health monitoring of large-scale concrete structures under earthquake loading.

In this paper, a wireless sensing system is designed to realize on-line damage localization and quantification of a structure using a frequency response function change method (FRFCM). Data interrogation algorithms are embedded in the computational core of the wireless sensing units to extract the necessary structural features, i.e. the frequency spectrum segments around eigenfrequencies, automatically from measured structural response for the FRFCM. Instead of the raw time history of the structural response, the extracted compact structural features are transmitted to the host computer. As a result, with less data transmitted from the wireless sensors, the energy consumed by the wireless transmission is reduced. To validate the performance of the proposed wireless sensing system, a six-story steel building with replaceable bracings in each story is instrumented with the wireless sensors for on-line damage detection during shaking table tests. The accuracy of the damage detection results using the wireless sensing system is verified through comparison with the results calculated from data recorded of a traditional wired monitoring system. The results demonstrate that, by taking advantage of collocated computing resources in wireless sensors, the proposed wireless sensing system can locate and quantify damage with acceptable accuracy and moderate energy efficiency.

In this study, the authors propose a frequency response function change method (FRFCM) which can be integrated with a wireless sensing system to detect damage of a building structure. The FRFCM was derived based on motion equations under a ground excitation both before and after a structure is damaged. The advantage of FRFCM is that only the frequency response functions of some frequency ranges around natural frequencies of a structure are needed to detect the location and extent of a damage. On the other hand, the wireless sensing units have the calculation ability to transform the measured time series to the frequency spectrum using the fast Fourier transform (FFT) algorithm. Therefore, only a few frequency bands of the frequency spectrum in the wireless sensing units are necessary to be delivered to the wireless server, instead of the whole measured time series. By doing so, the transmit power consumption of a wireless sensing unit is greatly reduced, hence increasing the feasibility of on-line damage detection using wireless sensing system based on structural vibration signals. The proposed idea was validated in a shaking table test of a 6-story steel building structure in a laboratory. In order to detect damage on-line automatically via a wireless sensing system, a FFT algorithm and a automatic peak-peaking algorithm for selecting natural frequencies of a structure were imbedded into the wireless sensing units. The damage extent of each story of the structure was displayed on the screen of the host computer automatically after the transmit of fragments of Fourier spectrum from wireless sensing units was done.

This paper presents a recursive stochastic subspace identification (RSSI) technique for on-line and almost real-time structural damage diagnosis using output-only measurements. Through RSSI the time-varying natural frequencies of a system can be identified. To reduce the computation time in conducting LQ decomposition in RSSI, the Givens rotation as well as the matrix operation appending a new data set are derived. The relationship between the size of the Hankel matrix and the data length in each shifting moving window is examined so as to extract the time-varying features of the system without loss of generality and to establish on-line and almost real-time system identification. The result from the RSSI technique can also be applied to structural damage diagnosis. Off-line data-driven stochastic subspace identification was used first to establish the system matrix from the measurements of an undamaged (reference) case. Then the RSSI technique incorporating a Kalman estimator is used to extract the dynamic characteristics of the system through continuous monitoring data. The predicted residual error is defined as a damage feature and through the outlier statistics provides an indicator of damage. Verification of the proposed identification algorithm by using the bridge scouring test data and white noise response data of a reinforced concrete frame structure is conducted.

Damping systems discussed in this work are optimized so that a three-story steel frame structure and its shape memory alloy (SMA) bracing system minimize response metrics due to a custom-tailored earthquake excitation. Multiple-objective numerical optimization that simultaneously minimizes displacements and accelerations of the structure is carried out with a genetic algorithm (GA) in order to optimize SMA bracing elements within the structure. After design of an optimal SMA damping system is complete, full-scale experimental shake table tests are conducted on a large-scale steel frame that is equipped with the optimal SMA devices. A fuzzy inference system is developed from data collected during the testing to simulate the dynamic material response of the SMA bracing subcomponents. Finally, nonlinear analyses of a three-story braced frame are carried out to evaluate the performance of comparable SMA and commonly used steel braces under dynamic loading conditions and to assess the effectiveness of GA-optimized SMA bracing design as compared to alternative designs of SMA braces. It is shown that peak displacement of a structure can be reduced without causing significant acceleration response amplification through a judicious selection of physical characteristics of the SMA devices. Also, SMA devices provide a recentering mechanism for the structure to return to its original position after a seismic event.

This paper presents the structural control results of shaking table tests for a steel frame structure in order to evaluate the performance of a number of proposed semi-active control algorithms using multiple magnetorheological (MR) dampers. The test structure is a six-story steel frame equipped with MR dampers. Four different cases of damper arrangement in the structure are selected for the control study. In experimental tests, the El Centro earthquake and Kobe earthquake ground motion data are used as excitations. Further, several decentralized sliding mode control algorithms are developed in this paper specifically for applications of MR dampers in building structures. Various control algorithms are used for the semi-active control studies, including the proposed decentralized sliding mode control (DSMC), LQR control, and passive-on and passive-off control. Each control algorithm is formulated specifically for the use of MR dampers installed in building structures. Additionally, each algorithm uses measurements of the device velocity and device drift for the determination of the control action to ensure that the algorithm can be implemented in a physical structure. The performance of each algorithm is evaluated based on the results of shaking table tests, and the advantages of each algorithm are compared and discussed. The reduction of story drifts and floor accelerations throughout the structure is examined.