Nonlinear multiclass support vector machine-based health monitoring system for buildings employing magnetorheological dampers

In this article, a nonlinear multiclass support vector machine-based structural health monitoring system for smart structures is proposed. It is developed through the integration of a nonlinear multiclass support vector machine, discrete wavelet transforms, autoregressive models, and damage-sensitive features. The discrete wavelet transform is first applied to signals obtained from both healthy and damaged smart structures under random excitations, and it generates wavelet-filtered signal. It not only compresses lengthy data but also filters noise from the original data. Based on the wavelet-filtered signals, several wavelet-based autoregressive models are then constructed. Next, damage-sensitive features are extracted from the wavelet-based autoregressive coefficients and then the nonlinear multiclass support vector machine is trained by a variety of damage levels of wavelet-based autoregressive coefficient sets in an optimal method. The trained nonlinear multiclass support vector machine takes new test wavelet-based autoregressive coefficients that are not used in the training process and quantitatively estimates the damage levels. To demonstrate the effectiveness of the proposed nonlinear multiclass support vector machine, a three-story smart building equipped with a magnetorheological damper is studied. As a baseline, naive Bayes classifier-based structural health monitoring system is presented. It is shown from the simulation that the proposed nonlinear multiclass support vector machine-based approach is efficient and precise in quantitatively estimating damage statuses of the smart structures.