A chaos game Optimization-based model updating technique for structural damage identification under incomplete noisy measurements and temperature variations. (February 2023)

• Record Type: Journal Article
• Authors: Dinh-Cong, D.; Nguyen-Thoi, T.
• Is Part Of: Structures. Volume 48(2023)
• Page Start: 1271
• Page End: 1284
• Publication Date: 2023-02
• Subjects: Damage identification -- Model updating -- Chaos Game Optimization -- Temperature variation -- Incomplete measurement; Structural engineering -- Periodicals; 624.1
• DOI: 10.1016/j.istruc.2023.01.032
• Languages: English
• ISSNs: 2352-0124

Abstract

Abstract: Dealing with structural damage identification (SDI) considering limited noise-contaminated measurements and varying environmental conditions is still a challenge due to the difficulties regarding the localization and quantification of structural damage. If the challenging issue is not considered in SDI process, the SDI techniques may become unreliable and have limitations in their implementation in in-service structures. In this regard, the article proposes an optimization-based model updating technique for SDI taking into consideration both incomplete noisy measurements and temperature variations. First, it is assumed in the finite element model of the monitored structure that the relation between temperature conditions and structural materials is temperature-dependent to synthetically generate damage-induced vibration data under varying environmental temperatures. Then, the inverse SDI problem is recast into a constrained optimization problem and a newly developed algorithm called Chaos Game Optimization (CGO) is adopted for solving this problem with high efficiency. The efficiency of the CGO algorithm is assessed by comparison with other four parameter-less optimization algorithms. Finally, three engineering numerical examples are conducted to demonstrate the performance of the proposed technique. The results of the numerical investigations indicated that for the monitored structures made of metallic material (i.e., steel and aluminum), the proposed technique can accurately localize and quantify single and multi-damage even in incomplete and noisy modal data and environmental temperature changes. Meanwhile, for the monitored structures made of concrete material, the ambient temperature variations significantly affect the damage detectability and localizability of the proposed technique.