Slope reliability analysis based on deep learning of digital images of random fields using CNN
JI Jian1,2, JIANG Zhen1, YIN Xin1, WANG Tao1, CUI Hong-zhi1, ZHANG Wei-jie1,2
1. College of Civil and Transportation Engineering, Hohai University, Nanjing 210024, China; 2. Key Laboratory of Ministry of Education for Geomechanics and Embankment Engineering, Hohai University, Nanjing 210024, China
Abstract：Considering the spatial variability of soil strength, a deep learning model for the characteristics of random fields is proposed for reliability analysis of slope stability. The random fields of a soil slope are discretized by the Karhunen-Loeve expansion method, and the discretized results are converted into digital images. Then, a convolutional neural network (CNN) surrogate model is established to approach the implicit relationship between the images and the responses of the performance function. Based on the surrogate model, the probability of failure of the slope is calculated. When training the CNN surrogate model, the Latin-Hypercube sampling technique, Bayesian optimization and 5-fold cross-validation are employed to improve the accuracy. Finally, the effectiveness of the proposed method is demonstrated by two case studies, namely a single-layer saturated clay slope under undrained conditions and a two-layered cohesive soil slope. The results show that in the case of high dimensions and small probability, the proposed CNN deep learning model can approximate the original model accurately, and significantly reduce the computational cost of slope reliability analysis considering the simulation of the random fields.
姬建, 姜振, 殷鑫, 王涛, 崔红志, 张卫杰. 边坡随机场数字图像特征CNN深度学习及可靠度分析[J]. 岩土工程学报, 2022, 44(8): 1463-1473.
JI Jian, JIANG Zhen, YIN Xin, WANG Tao, CUI Hong-zhi, ZHANG Wei-jie. Slope reliability analysis based on deep learning of digital images of random fields using CNN. Chinese J. Geot. Eng., 2022, 44(8): 1463-1473.
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