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• W4385705024 abstract "Slope stability prediction is an important task in geotechnical engineering which can be achieved through physics-based or data-driven approaches. Physics-based approaches rely on geotechnical knowledge from soil mechanics, such as limit equilibrium analysis and shear strength theories, to evaluate the stability condition of slopes, and they are often limited to slope-specific analysis. Data-driven approaches predict slope stability conditions based on learned relationships between influencing factors and slope stability conditions from past observations of slope failures (i.e., case histories); they rely on big data which are difficult to obtain. This study examines three easy-to-implement and effective methods to integrate geotechnical engineering domain knowledge into data-driven models for slope stability prediction: hybrid knowledge-data model, knowledge-based model initiation, and knowledge-guided loss function. These models were benchmarked against pure data-driven models and domain knowledge–based models, including a physics-based solution chart and a physics-based empirical model. A compilation of slope stability case histories from the literature was used as the benchmark database, and five-fold cross-validation was employed to evaluate model performance. The model validation results demonstrated that machine learning models outperformed domain knowledge–based models in terms of several evaluation metrics. The three proposed methods were found to outperform both domain knowledge–based models and pure data-driven models. Additionally, the hybrid knowledge-data models and knowledge-guided loss function were found to reduce discrepancies in the predicted slope stability conditions compared with reported factor-of-safety values, leading to a better alignment with the underlying physics related to slope stability. This study provides an initial assessment of the value of coupling domain knowledge and data-driven methods in geotechnical engineering applications using slope stability prediction as an example." @default.
• W4385705024 created "2023-08-10" @default.
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• W4385705024 date "2023-10-01" @default.
• W4385705024 modified "2023-09-23" @default.
• W4385705024 title "Applying Knowledge-Guided Machine Learning to Slope Stability Prediction" @default.
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• W4385705024 doi "https://doi.org/10.1061/jggefk.gteng-11053" @default.
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