A novel model order reduction-based two-stage damage detection paradigm for trusses using time–history acceleration. (February 2023) - Dang, Khanh D.; Nguyen, Nghia H.; Lee, Seunghye; Luong, Van Hai; Le, Tuan A.; Lieu, Qui X.
Abstract: This paper presents a novel model order reduction (MOR)-based two-stage damage detection method for trusses employing time-series acceleration measured by limited sensors. In the first step, an acceleration-based strain energy indicator (ASEI) is newly proposed to evaluate the most doubtfully damaged candidates. Accordingly, the number of design variables defined in an inverse optimization problem of the second phase is reduced significantly. The location and severity of damaged members are then determined by minimizing an objective function with a newly added dynamic penalty parameter to achieve a faster convergence speed and better optimal solutions. Since acceleration signals are incompletely measured at limited sensors, the second-order Neumann series expansion (SNSE) is firstly employed to condense the proportionally damped trusses for inferring the unmeasured time–history information. An adaptive hybrid evolutionary firefly algorithm (AHEFA) is utilized to resolve the above inverse optimization problem. Four numerical examples of 2D and 3D trusses with various damage scenarios including noises are examined to demonstrate the reliability of the proposed methodology. Attained outcomes indicate that the suggested paradigm can reliably diagnose both multidamage sites and extents of trusses with only relatively short time histories and a few measurement sensors. Highlights: A MOR-based two-step damage detection method for trusses by acceleration is proposed. An ASEI is newly suggested to discover doubtfully damaged candidates. A dynamic penalty parameter is used to improve convergence rate and solution quality. Second-order Neumann series expansion is utilized to infer unmeasured acceleration. Damage site and extent are detected with only short time domains and limited sensors.