A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Study on Resilience Evaluation of Waterlogging in Deep Foundation Pit Projects Based on the Random Forest
To quantitatively evaluate the waterlogging resilience of deep foundation pit projects, an evaluation model based on the random forest was constructed. Based on the structural and management characteristics, the evaluation index system was established from resistance, resilience, and adaptability. By using the strong nonlinear modeling ability of random forest, the relationship between the index of sample set and the corresponding resilience grade was mined to construct the resilience evaluation model. In addition, Nanchang Boyuanxianghu Project was selected for empirical research. The results showed that the resilience of the waterlogging of this project was medium, which was consistent with the engineering practice. Resilience was the key factor affecting the resilience of this project. Compared with the support vector machines and the back propagation neural network, the evaluation accuracy and data mining ability of the proposed method were stronger.
Study on Resilience Evaluation of Waterlogging in Deep Foundation Pit Projects Based on the Random Forest
To quantitatively evaluate the waterlogging resilience of deep foundation pit projects, an evaluation model based on the random forest was constructed. Based on the structural and management characteristics, the evaluation index system was established from resistance, resilience, and adaptability. By using the strong nonlinear modeling ability of random forest, the relationship between the index of sample set and the corresponding resilience grade was mined to construct the resilience evaluation model. In addition, Nanchang Boyuanxianghu Project was selected for empirical research. The results showed that the resilience of the waterlogging of this project was medium, which was consistent with the engineering practice. Resilience was the key factor affecting the resilience of this project. Compared with the support vector machines and the back propagation neural network, the evaluation accuracy and data mining ability of the proposed method were stronger.
Study on Resilience Evaluation of Waterlogging in Deep Foundation Pit Projects Based on the Random Forest
Liu, Yufei (author) / Li, Hongbing (author) / Wang, Junwu (author)
International Conference on Construction and Real Estate Management 2022 ; 2022 ; Virtual Conference
ICCREM 2022 ; 315-320
2022-12-15
Conference paper
Electronic Resource
English
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