Mohsen Mousavi
ABSTRACT
The methods to perform long-term condition monitoring of structures can be generally divided into two main categories, i.e. output-only and input-output methods. The former seeks to detect any anomaly in the structural modal data caused by damage without knowing the cause. Input-output normalisation methods have been used for removing Environmental and Operational Variations (EOV) effects, some examples of which include multiple linear regression [1], artificial neural networks [4] and support vector regression [2].
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- Rolands Kromanis and Prakash Kripakaran. 2013. Support vector regression for anomaly detection from measurement histories. Advanced Engineering Informatics 27, 4 (2013), 486--495.Google ScholarDigital Library
- Haichen Shi, Keith Worden, and Elizabeth J Cross. 2018. A regime-switching cointegration approach for removing environmental and operational variations in structural health monitoring. Mechanical Systems and Signal Processing 103 (2018), 381--397.Google ScholarCross Ref
- HF Zhou, YQ Ni, and JM Ko. 2010. Constructing input to neural networks for modeling temperature-caused modal variability: mean temperatures, effective temperatures, and principal components of temperatures. Engineering Structures 32, 6 (2010), 1747--1759.Google ScholarCross Ref
Index Terms
- Structural damage identification under non-linear EOV effects using genetic programming
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