Development of Recursive Subspace Identification for Real-Time Structural Health Monitoring under Seismic Loading
Auteur(s): |
Shieh-Kung Huang
Fu-Chung Chi |
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Médium: | article de revue |
Langue(s): | anglais |
Publié dans: | Structural Control and Health Monitoring, février 2023, v. 2023 |
Page(s): | 1-19 |
DOI: | 10.1155/2023/1117042 |
Abstrait: |
Structural health monitoring (SHM) can continuously and nondestructively evaluate the state and performance of structures using the structural responses to external loads or environmental conditions. Moreover, online or real-time SHM of civil structures provides significant advantages over periodic or manual inspection methods, especially under disaster loadings, where the consequences of failure can be severe. To achieve it, performing system identification and damage detection recursively, said recursive subspace identification (RSI), is a promising solution, and SHM based on the algorithms can evaluate damage or deterioration of civil structures, give insight into the health and performance of a structural system, and provide valuable information for decision-making on maintenance and repair. However, the time-consuming decompositions frustrate these algorithms. As a compromise, additional processing is required to implement online and real-time applications. This study demonstrates a modified algorithm that takes advantage of the projection approximation subspace tracking (PAST) algorithm and the repeated system matrices in the extended observability matrix. The modification can reduce numerical decompositions and improve important timeliness for online or real-time SHM of civil structures. Both the numerical simulation and experimental investigation have been used to verify the proposed method, and the results show its capability to determine the changes in the dynamic characteristics of a structure in either the laboratory experiment or in the field application. In the last place, the discussion and some conclusions are also drawn in this paper. |
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sur cette fiche - Reference-ID
10749384 - Publié(e) le:
14.01.2024 - Modifié(e) le:
14.01.2024