Structural damage detection based on modal feature extraction and multi-objective optimization method for steel structures

Abstract

Model updating based on intelligent algorithms has achieved great success in structural damage detection (SDD). But the appropriate selection of objective functions remains unclear and becomes an obstacle to applying the methods to real-world steel structures. In this paper, a multi-objective identification method based on modal feature extraction and linear weight sum was proposed, and the best weight values to gain the best solution were also determined. A hybrid particle swarm optimization (HPSO) was selected as a solver to update structural parameters for accurate SDD results. First of all, six single objective functions based on modal feature extraction were considered, and numerical simulations show that the one based on MTMAC indicator exhibits certain superiority over the other. In order to provide a fair comparison among different objective functions, a quantified indicator named damage vector consistency (DVC) is also defined, which describes the consistency between identified result and the assumed one. After that, a multi-objective identification method is formulated by linearly combining an MTMAC-based objective function and another selected single objective function. Different weight values were also investigated to find out the best solution for accurate SDD. Three numerical simulations were conducted, including a simply-supported beam, a two-story steel frame, and a 31-bar plane truss. Their SDD results verify the applicability of the proposed multi-objective optimization method. Some relative discussions are also described in detail.