Multi-site structural damage identification using a multi-label classification scheme of machine learning

Abstract

This study proposes a new method to locate multi-site structural damage using a data-driven multi-label classification (MLC) method. Differing from the multiclass classification (MCC) scheme that sets each damage case as a category, the MLC method denotes each damage case with multiple labels, with each label indicating the occurrence of damage at a certain location. The MLC method considers the physical correlation between damage cases sharing damage locations which is neglected in the MCC method. This paper uses the instance differentiation algorithm to implement MLC. Damage identification results using numerical and experimental data indicate that the MLC can identify multi-site damage with good accuracy, even for damage cases that are not covered by the training dataset. Through comparison, it is demonstrated that the MLC method outperforms the MCC and binary classification methods for multi-site damage identification. Moreover, MLC preserves generality when tested on unobserved multi-site damage cases.