https://scholars.lib.ntu.edu.tw/handle/123456789/625620
標題: | Performance of artificial neural network and convolutional neural network on slope failure prediction using data from the random finite element method | 作者: | Hsiao C.-H ALBERT CHEN Ge L Yeh F.-H. |
關鍵字: | Artificial neural network; Convolution neural network; Random finite element method; Slope stability analysis | 公開日期: | 2022 | 來源出版物: | Acta Geotechnica | 摘要: | The random finite element method has been widely used to evaluate slope uncertainty and reliability. To determine the probability of failure, the safety factor sampling often involves the Monte Carlo method. This process is a time-consuming task that requires many computational resources. In this study, we propose a pretrained model to directly estimate the safety factor and the trace of the slope slip surface by using machine learning techniques. Each safety factor associated with its random field conditions can be quickly predicted to evaluate the probability of failure. Furthermore, the potential slip surfaces can be examined through the predicted strain contour. Herein, artificial neural networks and convolutional neural networks are both implemented in the factor of safety and slip surface predictions. The results show that the convolutional neural network model is more accurate than the artificial neural network model when the scenario is more complicated (i.e., considering the additional random field of soil properties). Additionally, the convolutional neural network model considers the spatial relationships of input data, which is an appropriate method to address random field problems. This method effectively shortens the time compared with the traditional random finite element method. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature. |
URI: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85128807717&doi=10.1007%2fs11440-022-01520-w&partnerID=40&md5=8b2da8a9fc8c9619ff199ded86e6bc37 https://scholars.lib.ntu.edu.tw/handle/123456789/625620 |
ISSN: | 18611125 | DOI: | 10.1007/s11440-022-01520-w |
顯示於: | 土木工程學系 |
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