Abstract
This paper presents an artificial neural network (ANN)-based response surface method that can be used to predict the failure probability of c-φ slopes with spatially variable soil. In this method, the Latin hypercube sampling technique is adopted to generate input datasets for establishing an ANN model; the random finite element method is then utilized to calculate the corresponding output datasets considering the spatial variability of soil properties; and finally, an ANN model is trained to construct the response surface of failure probability and obtain an approximate function that incorporates the relevant variables. The results of the illustrated example indicate that the proposed method provides credible and accurate estimations of failure probability. As a result, the obtained approximate function can be used as an alternative to the specific analysis process in c-φ slope reliability analyses.
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This work was financially supported by the National Natural Science Foundation of China (Grant No. 51278217).
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Shu, Sx., Gong, Wh. An artificial neural network-based response surface method for reliability analyses of c-φ slopes with spatially variable soil. China Ocean Eng 30, 113–122 (2016). https://doi.org/10.1007/s13344-016-0006-x
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DOI: https://doi.org/10.1007/s13344-016-0006-x