Multiobjective Optimization Approach for Robust Bridge Damage Identification against Sensor Noise

Abstract

One of the important goals of structural health monitoring is to identify structural damage using measured responses. However, such damage identification is sensitive to noises in the response measurements. Even a small change in the measurement may result in a significantly biased damage assessment. The goal of this paper is to expand the multiobjective optimization approach developed for robust damage identification in order to facilitate its applications to more realistic bridge damage identification problems. Specifically, a benchmark problem on highway bridges, developed under the auspices of International Association for Bridge Maintenance and Safety (IABMAS), is investigated. Various issues regarding sensor noises, multiple measurements, and loading scenarios are addressed to improve the robustness of bridge damage identification. A major finding from this study is that the stochastic process of Pareto optimal solutions obtained in a single run not only captures the actual damage locations successfully but also provides useful information such as damage-detected ratio on the potential candidates for damage to be inspected on site. Moreover, it is shown through the success, failure, and partial detection rates that the robustness of the proposed approach can be improved by using appropriate excitation scenarios and multiple sets of measurement data.