Data-driven machine-learning-based seismic failure mode identification of reinforced concrete shear walls

2020 ◽  
Vol 208 ◽  
pp. 110331 ◽  
Author(s):  
Sujith Mangalathu ◽  
Hansol Jang ◽  
Seong-Hoon Hwang ◽  
Jong-Su Jeon
Keyword(s):  
Machine Learning ◽  
Reinforced Concrete ◽  
Failure Mode ◽  
Shear Walls ◽  
Data Driven ◽  
Mode Identification ◽  
Failure Mode Identification

Machine learning-based failure mode identification of RCSPSW

2021 ◽  
Author(s):  
Dongqi Jiang ◽  
Shanquan Liu ◽  
Tao Chen ◽  
Gang Bi
Keyword(s):  
Machine Learning ◽  
Failure Mode ◽  
Failure Modes ◽  
Shear Failure ◽  
Tall Buildings ◽  
Shear Walls ◽  
Superior Performance ◽  
Ann Model ◽  
Mode Recognition ◽  
Wide Range

<p>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), <i>K</i>-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.</p>

Multiple failure mode identification of SiC planar MOSFETs in short-circuit operation

2020 ◽  
Vol 114 ◽  
pp. 113804
Author(s):  
Bixuan Wang ◽  
Jingcun Liu ◽  
Wanping Li ◽  
Guogang Zhang ◽  
Yingsan Geng ◽  
...  
Keyword(s):  
Failure Mode ◽  
Short Circuit ◽  
Mode Identification ◽  
Failure Mode Identification
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