A probabilistic model is proposed that uses observation data to estimate failure probabilities during excavations.The model integrates a Bayesian network and distanced-based Bayesian model updating.In the network,the ...A probabilistic model is proposed that uses observation data to estimate failure probabilities during excavations.The model integrates a Bayesian network and distanced-based Bayesian model updating.In the network,the movement of a retaining wall is selected as the indicator of failure,and the observed ground surface settlement is used to update the soil parameters.The responses of wall deflection and ground surface settlement are accurately predicted using finite element analysis.An artificial neural network is employed to construct the response surface relationship using the aforementioned input factors.The proposed model effectively estimates the uncertainty of influential factors.A case study of a braced excavation is presented to demonstrate the feasibility of the proposed approach.The update results facilitate accurate estimates according to the target value,from which the corresponding probabilities of failure are obtained.The proposed model enables failure probabilities to be determined with real-time result updating.展开更多
基金This work is supported by the Chinese Scholarship Council.
文摘A probabilistic model is proposed that uses observation data to estimate failure probabilities during excavations.The model integrates a Bayesian network and distanced-based Bayesian model updating.In the network,the movement of a retaining wall is selected as the indicator of failure,and the observed ground surface settlement is used to update the soil parameters.The responses of wall deflection and ground surface settlement are accurately predicted using finite element analysis.An artificial neural network is employed to construct the response surface relationship using the aforementioned input factors.The proposed model effectively estimates the uncertainty of influential factors.A case study of a braced excavation is presented to demonstrate the feasibility of the proposed approach.The update results facilitate accurate estimates according to the target value,from which the corresponding probabilities of failure are obtained.The proposed model enables failure probabilities to be determined with real-time result updating.
