Support vector regression for on-line health monitoring of large-scale structures

Abstract

Large-scale, structural health monitoring remains a challenge especially when I/O measurement data are contaminated by high-level noise. A novel approach that uses incremental support vector regression (SVR), a promising statistics technology, is proposed for large-scale, structural health monitoring. Due to the potential properties of this novel SVR, the SVR-based approach makes structural health monitoring accurately and robustly. A sub-structure strategy is utilized to reduce the number of unknown parameters in the health monitoring formula, thereby making large-scale structural health monitoring possible. Lastly, an incremental SVR training algorithm adopted for the SVR-based approach not only markedly reduces computation time, but identifies structural parameters on-line. Numerical examples show that results of this SVR-based approach for large-scale structural health monitoring are accurate and robust, even when observed data are contaminated with different kinds and intensity levels of noise.