Geotechnical risk modeling using an explainable transfer learning model incorporating physical guidance.

While Artificial intelligence (AI) has been successfully applied in assessing geotechnical risk, such methods heavily rely on data quality to achieve satisfactory performance, and their results hardly can be interpreted due to their opaque design. With this in mind, this paper aims to address the following research gap: How can we accurately model geotechnical risks using limited data and domain knowledge, and efficiently explain the results of AI model? We develop a physics-guided transfer learning (PGTL) model to enhance the explainability and accuracy of geotechnical risk modeling. With the help of a physical model that simulates the tunnel excavation, a physics-guided dataset with 1000 samples is established and used to train a deep neural network. On these bases, transfer learning is adopted to fuse the features of physics mechanisms and monitoring data, constructing an explainable prediction model of geotechnical risk. To further support risk decision-making, feature relevance techniques are employed to assess the contribution of input parameters to risk. A shield tunnel construction in Wuhan is selected as a case to validate the effectiveness of the proposed method. The PGTL exhibits a more promising accuracy with R 2 of 0.777 in contrast to three popular machine learning approaches, and provides insights into parameters that significantly induce risk, enhancing site managers' understanding of tunnel construction and being conducive to tunnel safety. • A novel explainable artificial intelligence approach for geotechnical risk modeling is proposed. • Incorporate predefined physical guidance to machine learning to enhance data's feature and provide in-model explainability. • Utilize Model-agnostic feature relevance to provide post-hoc explainability without sacrificing predictive ability. • A real tunnel construction in Wuhan, China is taken as a case study for illustration. [ABSTRACT FROM AUTHOR]