A Data-Driven Indirect Approach for Predicting the Response of Existing Structures Induced by Adjacent Excavation

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.