期刊
JOURNAL OF STRUCTURAL ENGINEERING
卷 146, 期 11, 页码 -出版社
ASCE-AMER SOC CIVIL ENGINEERS
DOI: 10.1061/(ASCE)ST.1943-541X.0002793
关键词
Seismic risk assessment; Continuous wavelet transform; Image analysis; Convolutional neural network deep learning; Building frame; Bridge
资金
- National Research Foundation of Korea (NRF) - Korea government (MSIT) [2019R1C1C1007780]
- National Research Foundation of Korea [2019R1C1C1007780] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
Rapid and accurate evaluation of the damage state of structures after a seismic event is critical for postevent emergency response and recovery. The existing rapid damage evaluation methodology is typically based on fragility curves incorporated into earthquake alerting platforms. However, the extent of damage predicted solely based on the fragility curves can vary significantly depending on ground motion characteristics. This paper presents a methodology for damage assessment of structures while accounting for temporal and spectral nonstationarity of ground motions using continuous wavelet transform and image-analysis techniques. The methodology involves the establishment of a prediction model for wavelet transform of ground motions and damage state of a structure using convolutional neural networks. The methodology is demonstrated in this paper through two case studies: (1) a low-rise nonductile concrete building frame in California and (2) a four-span concrete box-girder bridge in California. The proposed methodology identified damage states with an accuracy greater than 75% in both cases. DOI: 10.1061/(ASCE)ST.1943-541X.0002793. (c) 2020 American Society of Civil Engineers.
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