Slope failures are the leading cause of significant casualties and financial losses, despite technological advancements. Digital Twin has become an essential tool for real-time monitoring and decision-making, particularly for the complex problems related to slope stability. Data-driven algorithms have proven to be efficient surrogate models for Digital Twins as an alternative to the existing limit equilibrium and finite element analysis. In this study, ensemble learning algorithms are employed to predict the factor of safety (FoS) of the slopes, and Explainable Artificial Intelligence (XAI) methods are used to improve the model interpretability. The findings of the study are aimed at identifying key features important for effective monitoring of slopes in the field, showing that XAI findings align well with established physical principles of slope. A comprehensive field database containing 497 unique slope cases consisting of both manmade and natural slopes with circular failure conditions is utilised in this study for model development and verification. Soil unit weight (γ), soil cohesion (c), internal friction angle (ϕ), slope angle (β), slope height (H), and pore pressure ratio (ru) are taken as primary input parameters. Notably, features H, c, and β are consistently ranked as the most important features across models, highlighting their crucial function in slope stability. A structured approach is used to rank the models, showing that combining ensemble learning and XAI within Digital Twin framework can lead to safer, data-driven decisions for slope stability issues.
扫码关注我们
求助内容:
应助结果提醒方式:

