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Machine learning-based failure mode identification of RCSPSW

 Machine learning-based failure mode identification of RCSPSW
Autor(en): , , ,
Beitrag für IABSE Congress: Resilient technologies for sustainable infrastructure, Christchurch, New Zealand, 3-5 February 2021, veröffentlicht in , S. 1150-1157
DOI: 10.2749/christchurch.2021.1150
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Reinforced concrete – steel plate composite shear walls (RCSPSW) have attracted great interests in the construction of tall buildings. From the perspective of life-cycle maintenance, the failure mo...
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Bibliografische Angaben

Autor(en): (Nanjing University of Science and Technology, Nanjing, Jiangsu Province, China)
(Nanjing University of Science and Technology, Nanjing, Jiangsu Province, China)
(China Academy of Building Research, Beijing, China)
(Yango University, Fuzhou, China)
Medium: Tagungsbeitrag
Sprache(n): Englisch
Tagung: IABSE Congress: Resilient technologies for sustainable infrastructure, Christchurch, New Zealand, 3-5 February 2021
Veröffentlicht in:
Seite(n): 1150-1157 Anzahl der Seiten (im PDF): 8
Seite(n): 1150-1157
Anzahl der Seiten (im PDF): 8
DOI: 10.2749/christchurch.2021.1150
Abstrakt:

Reinforced concrete – steel plate composite shear walls (RCSPSW) have attracted great interests in the construction of tall buildings. From the perspective of life-cycle maintenance, the failure mode recognition is critical in determining the post-earthquake recovery strategies. This paper presents a comprehensive study on a wide range of existing experimental tests and develops a unique library of 17 parameters that affects RCSPSW’s failure modes. A total of 127 specimens are compiled and three types of failure modes are considered: flexure, shear and flexure-shear failure modes. Various machine learning (ML) techniques such as decision trees, random forests (RF),K-nearest neighbours and artificial neural network (ANN) are adopted to identify the failure mode of RCSPSW. RF and ANN algorithm show superior performance as compared to other ML approaches. In Particular, ANN model with one hidden layer and 10 neurons is sufficient for failure mode recognition of RCSPSW.