Combined Model-Free Data-Interpretation Methodologies for Damage Detection during Continuous Monitoring of Structures

Abstract

Despite the recent advances in sensor technologies and data-acquisition systems, interpreting measurement data for structural monitoring remains a challenge. Furthermore, because of the complexity of the structures, materials used, and uncertain environments, behavioral models are difficult to build accurately. This paper presents novel model-free data-interpretation methodologies that combine moving principal component analysis (MPCA) with each of four regression-analysis methods—robust regression analysis (RRA), multiple linear analysis (MLR), support vector regression (SVR), and random forest (RF)—for damage detection during continuous monitoring of structures. The principal goal is to exploit the advantages of both MPCA and regression-analysis methods. The applicability of these combined methods is evaluated and compared with individual applications of MPCA, RRA, MLR, SVR, and RF through four case studies. Result showed that the combined methods outperformed noncombined methods in terms of damage detectability and time to detection.