Feature extraction by time-series analysis and decision making through distance-based methods are powerful and efficient statistical pattern recognition techniques for data-driven structural health monitoring. The motivation of this article is to propose an innovative residual-based feature extraction approach based on AutoRegressive modeling and a novel statistical distance method named as Partition-based Kullback–Leibler Divergence for damage detection and localization by using randomly high-dimensional damage-sensitive features under environmental and operational variability. The key novel element of the proposed feature extraction approach is to establish a two-stage offline and online learning algorithms for extracting the residuals of AutoRegressive model as the main damage-sensitive features. This technique brings the great benefit of reducing the computational time and storage space for feature extraction in long-term monitoring conditions. The major contribution of Partition-based Kullback–Leibler Divergence method is to exploit a partitioning strategy for dividing random features into individual partitions and utilize numerical information of partitioning in distance calculation rather than directly applying random samples. Dealing with the major challenging issue of using the high-dimensional features in decision making and applicability to both correlated and uncorrelated random datasets are the main advantages of Partition-based Kullback–Leibler Divergence method. The accuracy and reliability of the proposed approaches are experimentally validated by two well-known benchmark structures. The stationarity and linearity of measured vibration responses for using in AutoRegressive modeling are evaluated by two hypothesis tests. Comparative studies are also conducted to demonstrate the superiority of the proposed methods over some exciting state-of-the-art techniques. Results show that the methods presented here succeed in detecting and locating damage and make time-saving and efficient tools for feature extraction and damage diagnosis.
A semi-active control method for a seismically excited nonlinear benchmark building equipped with a magnetorheological (MR) damper is presented and evaluated. A linear quadratic Gaussian (LQG) controller is designed to estimate the optimal control force. The required voltage for the MR damper to produce the control force estimated by LQG controller is calculated by a neural network predictive control algorithm (NNPC). The LQG controller and the NNPC are linked to control the structure. The coupled LQG and NNPC system are then used to train a semi-active neuro-controller designated as SANC, which produces the necessary control voltage that actuates the MR damper. The effectiveness of the NNPC and SANC is illustrated and verified using simulated response of a 3-story full-scale, nonlinear, seismically excited, benchmark building excited by several historical earthquake records. The semi-active system using the NNPC algorithm is compared with the performances of passive as well as active and clipped optimal control (COC) systems, which are based on the same nominal controller as is used in the NNPC algorithm. The results demonstrate that the SANC algorithm is quite effective in seismic response reduction for wide range of motions from moderate to severe seismic events, compared with the passive systems and performs better than active and COC systems. of the reactive velocity and the issued voltage as described in Equations (2)-(5). The damper velocity is the same as the relative velocity of the floors the damper is connected to. This neural network model is denoted as NNMR ( Figure 5) and is trained ( Figure 6) using input-output data generated analytically using the simulated MR model based on Equations (2)-(5). The NNMR calculates the damper forces based on the current and few previous histories of measured velocity, voltage signals, and damper forces.Training the NNMR requires the compilation of input-output data. To completely identify the underlying MR system model, the data must contain information about the entire operating range of the system. Here, in this study, the velocity and voltage are generated randomly using band limited white Gaussian noise. The generated forces are results of the MR model described in Equations (2)-(5). The sampling rate of the training data was 200 Hz for 30 s period, which resulted in 6000 patterns for training, testing, and validation (Figure 7). Next step is to select the network architecture. To do so, it is required to determine the numbers of inputs, outputs, hidden layers, and nodes in the hidden layers which is usually done by trial and error. The most suitable input data in our case were found to be the current and the four previous histories for Figure 8. Neural network controller (SANC).
SUMMARYControl algorithm is one of the most important aspects in successful control of buildings against earthquake. In recent years, because of their capabilities, soft computing methods, stemmed from human brain abilities, have become of particular interest to researchers. In this paper, a wavelet neural network-based semi-active control model is proposed in order to provide accurately computed input voltage to the magneto rheological dampers to generate the optimum control force of structures. This model is optimized by a localized genetic algorithm and then applied to a nine-story benchmark structure subjected to 1.5× El Centro earthquake. The results show an average of 43% reduction of maximum drift in the controlled structure versus the uncontrolled one. The capability of the controller is also validated by applying other far-field and near-field earthquakes. The capability and efficiency of the proposed model are demonstrated in terms of drift, acceleration and base shear reduction. The proposed wavelet neural network is also compared with a tangent hyperbolic-based feed forward neural network, linear quadratic Gaussian, clipped optimal controller, and genetic algorithm-based fuzzy inference systems to show the superiority of the proposed controller.
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