A hybrid data-driven model for geotechnical reliability analysis. (March 2023) - Liu, Wenli; Li, Ang; Fang, Weili; Love, Peter E.D.; Hartmann, Timo; Luo, Hanbin
Highlights: A hybrid data-driven model is proposed to quantify risk under uncertain parameters.during a tunnel's excavation process. A real case is used to evaluate the effectiveness and feasibility of our proposed approach. A Markov-Chain-based importance sampling is used to analyze settlement reliability. Abstract: Tunnel boring machines are widely used to construct underground rail networks in urban areas. However, ground settlement due to complex geological conditions is an ever-present reality requiring continuous monitoring and management of risks. This paper addresses the following research question: How can we predict tunnel-induced ground settlement with engineering parameters, improve its predictive ability, and quantify its risks under uncertain parameters in complex geological conditions ? To this end, we develop a hybrid data-driven model that considers prior domain knowledge to effectively and accurately quantify risk under uncertain parameters during a tunnel's excavation process. Our model comprises: (1) a deep neural network (DNN) to construct a ground settlement prediction model; (2) the incorporation of physical knowledge into the DNN-based prediction model; and (3) a Markov-chain-based importance sampling to analyze settlement reliability. We use the San-yang Road tunnel project in Wuhan, China, to evaluate the effectiveness and feasibility of our proposed approach. The results demonstrate that our hybrid data-driven model can accurately predict tunnel-induced ground settlement and quantify failure probability for geotechnical reliability under uncertain parameters during a tunnel's excavation process.