Prediction of stability of a slope with weak layers using convolutional neural networks

Mansheng Lin, Limei Zeng, Shuai Teng, Gongfa Chen () and Bo Hu ()
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Mansheng Lin: Guangdong University of Technology
Limei Zeng: Guangdong University of Technology
Shuai Teng: Guangdong University of Technology
Gongfa Chen: Guangdong University of Technology
Bo Hu: Guangdong University of Technology

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2024, vol. 120, issue 13, No 23, 12105 pages

Abstract: Abstract Artificial intelligence (AI)-based methods have been widely applied to slope stability assessment, but due to the scarcity of samples, most AI models are used under certain working conditions, such as the homogeneous or fixed-size slopes. In actual situations, the slope stability is affected by factors such as geometries, weak layers, etc. Therefore, in order to further consider more parameters affecting slope stability in AI models, this study used digital twin (DT) technique to build a database of the slopes with weak layers through practical cases. Meanwhile, in order to improve the prediction performance of the model, a convolutional neural network (CNN) is constructed. In this paper, the process of establishing a database of slopes with weak layers is elaborated in detail. Meanwhile, the performance of the CNN models is investigated thoroughly through several evaluators. Finally, the trained CNN is applied to actual slope cases. The results show that the CNN achieves the highest scores in a range under the receiver operating characteristics (0.99), accuracy (95.4%), and F1 score (95.3%) compared with other machine learning (ML) methods on the testing dataset, and also correctly classifies the actual slope cases, which provides valuable practical and engineering insights for slope stability assessment with weak layers in terms of efficiency and accuracy, especially for practitioners with limited knowledge of slope stability assessment.

Keywords: Slope stability prediction; Weak layers; Convolutional neural networks; Database; Bayesian optimization (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s11069-024-06674-2

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