A novel Bayesian network approach for predicting soil-structure interactions induced by deep excavations
S.C. Jong, Dominic Ek Leong Ong
Abstract
The demand for deep excavations to construct underground infrastructure has been growing in metropolitan cities due to rapid urban development and limited land space. In this original research, a machine learning technique based on Bayesian Network is proposed to evaluate and predict soil-structure interactions caused by braced excavations. A novel framework based on Bayesian Network incorporating the Bayesian updating process was proposed to study the ground and wall responses induced by deep excavations. Three established case studies collected from the literature were used to implement and showcase the novel concept. Besides, original two-dimensional finite element models were developed to simulate the first two case studies involving deep excavations in clay in order to generate sufficient ground and wall response data for the training and updating phases of the proposed Bayesian model. The third deep excavation case study carried out in a totally different geology (predominantly sand) was then used to assess and verify the robustness of the Bayesian framework in predicting the soil-structure responses. It was eventually established that the developed novel Bayesian Network model managed to successfully predict the wall deflections and bending moments as well as the ground surface settlements of the third case study with good accuracy through using only two case studies as priors via the versatile sequential updating processes, albeit having totally different geological settings.