Under ambient excitation of white noise, structural modal parameters can be identified from cross-power spectrum calculated from structural outputs, without known input. The minimization of difference between theoretical value and test value of cross-power spectrum — the former is an equation including modal parameters to be identified, and the latter is computed based on the output-only data of structure — is adopted as an objective function for optimization. The optimal objective function value can be obtained through optimal modal parameter search. Quantum-behaved particle swarm optimization (QPSO) — swarm intelligence optimization algorithm based on particle swarm optimization (PSO) — is used for optimization to identify the modal parameters of structure under the ambient excitation. Then, the modal parameter identification method based on QPSO presented herein had been demonstrated by a numerical simulation of three-span continuous beam and experimental results of a three story structure. The computed results indicated that QPSO can be effectively used in the structural modal parameter identification under ambient excitation.
Abstract A new semiactive independently variable damper, SAIVD, is developed and shown to be effective in achieving response reductions in smart base isolated buildings in near fault earthquakes. The semiactive device consists of four linear visco‐elastic elements, commonly known as Kelvin–Voigt elements, arranged in a rhombus configuration. The magnitude of force in the semiactive device can be adjusted smoothly in real‐time by varying the angle of the visco‐elastic elements of the device or the aspect ratio of the rhombus configuration. Such a device is essentially linear, simple to construct, and does not present the difficulties commonly associated with modelling and analysing nonlinear devices (e.g. friction devices). The smooth semiactive force variation eliminates the disadvantages associated with rapid switching devices. Experimental results are presented to verify the proposed analytical model of the device. A H ∞ control algorithm is implemented in order to reduce the response of base isolated buildings with variable damping semiactive control systems in near fault earthquakes. The central idea of the control algorithm is to design a H ∞ controller for the structural system that serves as an aid in the determination of the optimum control force in the semiactive device. The relative performance of the SAIVD device is compared to a variable friction device, recently developed by the authors in a separate study, and several key aspects of performance are discussed regarding the use of the two devices for reducing the responses of smart base isolated buildings in near fault earthquakes. Copyright © 2006 John Wiley & Sons, Ltd.
SUMMARY Output‐only modal identification is needed when only structural responses are available. As a powerful unsupervised learning algorithm, blind source separation (BSS) technique is able to recover the hidden sources and the unknown mixing process using only the observed mixtures. This paper proposes a new time‐domain output‐only modal identification method based on a novel BSS learning algorithm, complexity pursuit (CP). The proposed concept—independent ‘physical systems’ living on the modal coordinates—connects the targeted constituent sources (and their mixing process) targeted by the CP learning rule and the modal responses (and the mode matrix), which can then be directly extracted by the CP algorithm from the measured free or ambient system responses. Numerical simulation results show that the CP method realizes accurate and robust modal identification even in the closely spaced mode and the highly damped mode cases subject to non‐stationary ambient excitation and provides excellent approximation to the non‐diagonalizable highly damped (complex) modes. Experimental and real‐world seismic‐excited structure examples are also presented to demonstrate its capability of blindly extracting modal information from system responses. The proposed CP is shown to yield clear physical interpretation in modal identification; it is computational efficient, user‐friendly, and automatic, requiring little expertise interactions for implementations. Copyright © 2013 John Wiley & Sons, Ltd.
Digital cameras are cost-effective vision sensors and able to directly provide two-dimensional information of structural condition in monitoring and assessment applications. For example, digital cameras are essential components of unmanned aerial vehicles (UAVs) and robotic agents for mobile sensing and inspection of pipelines, buildings, transportation infrastructure, etc, especially in post-natural disaster and man-made extreme events assessment. Additionally, while surveillance cameras have been widely used for transportation systems (e.g., traffic monitoring), if appropriately mounted on the large-scale structures such as the bridges, they can continuously monitor the structural condition under operational loads and hazards, complementing the regular visual inspection and assessment conducted by experts. In these or other applications, efficiently and reliably transferring the structural images or videos, which are as such large-scale, are important and challenging, especially in wireless platform that is either required (e.g., UAVs and robotic agents) or more suitable (e.g., camera monitoring networks) with only limited power and communication resources. This paper studies the computational algorithms for efficient and reliable transmission of the structural monitoring images; in particular, the compressed sensing (CS) technique is explored for robust data transmission and recovery. The sparse representation or data structure of the structural images is exploited, leading to the CS based central strategy: on some sparse domain, randomly encode large-scale image data into few relevant coefficients, which are then transferred (robust to random data loss) and recovered (in base station) for subsequent structural health diagnosis. Image data of bench scale pipe structure, concrete structure and full scale stay cable are employed for validation of the CS based method. Its performance is also compared with traditional transform coding and low-dimensional encoding (sampling), and their advantages and drawbacks are discussed. Copyright © 2016 John Wiley & Sons, Ltd.
In this paper, a new real-time damage identification method has been presented for bridge structural health monitoring (SHM) considering temperature variation. The method utilizes model-based damage identification that involves three major steps: (1) efficient basis functions—extracted from finite-element (FE) models prior to real-time identification; (2) partial least-squares regression (PLSR) analyses; and (3) the fusion of different types of structural responses into damage indicator. By treating local damages as equivalent vertical loads and then cross-referencing global (inclinations) and local (strain) data, the hidden damage information in bridge structures can be detected and localized in a timely fashion, even in the presence of unknown temperature variation as well as vehicle loads. Inclinations alone cannot reflect local damages, but by fusing inclinations and strains (that represent local damage) into the proposed damage indicator, local damages can be identified. Numerical simulations on a medium-span continuous bridge demonstrate that the proposed method is insensitive to measurement noise and some common modeling errors, revealing the potential of real-time damage identification in bridge SHM applications.
A novel real-time actuator failure detection algorithm is developed in this paper. Actuator fails when the input to the structure is different from the commanded one. Previous research has shown that one error function can be formulated for each actuator through interaction matrix method. For output without noise, non-zero values in the actuator functions indicate the instant failure of the actuator regardless the working status of other actuators. In this paper, it is further demonstrated that the actuator's error function coefficients will be directly calculated from the healthy input of the examined actuator and all outputs. Hence, the need for structural information is no longer needed. This approach is termed as direct method. Experimental results from a NASA eight bay truss show the successful application of the direct method for isolating and identifying the real-time actuator failure. Further, it is shown that the developed method can be used for real-time sensor failure detection.
