Affiliation:
1. Key Laboratory of Urban Security and Disaster Engineering of Ministry of Education, Beijing University of Technology, Beijing 100124, China
2. Agiletech Engineering Consultants Co., Ltd., Beijing 100037, China
Abstract
A data-driven indirect approach for predicting the response of existing structures induced by excavation is hereby proposed based on making full use of monitoring data during excavation, which can predict the deformation history of the research object during excavation. In this article, a machine-learning-based model framework for implementing the proposed approach is constructed and the treatment of key issues in the design and implementation of the proposed method is described in detail including the theoretical framework, the implementation mode of the method, the dimensionality reduction of the model parameters, and the normalization of data for model. On this basis, three models are provided to predict the settlement of buildings induced by adjacent excavation, namely the SVM model, BP model, and BP–SVM model. Relying on an excavation project for a subway in Xuzhou, Jiangsu Province, China, the proposed method is verified, and some conclusions are obtained.
Funder
Beijing Municipal Science and Technology Planning Project
Beijing Postdoctoral Research Foundation
Subject
Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science
Cited by
2 articles.
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