Development of an adaptive relevance vector machine approach for slope stability inference
Uncertainty is commonly encountered in such problems as the stability inference of slopes in earth science and geotechnical engineering. This uncertainty can be approached by the artificial intelligence techniques and experts systems. This paper presents the adaptive relevance vector machine (ARVM) for stability inference of soil slopes. Based on failure mechanisms and due to data availability, the stability inference here is realized according to the three categories of slope parameters: (1) geomaterial parameters, (2) slope geometry parameters, and (3) the pore pressure coefficient R u . A database of dozens of slope cases is collected to reasonably execute the stability inference. Then, the ARVM is introduced to approach the problem. Some cases in the database are used to train the ARVM model so that an optimized ARVM model can be obtained. Some other cases are then used to test the inference ability of the optimized model. Four models obtained by different numbers of cases are compared to show possible effects of dataset size. Also, the sensitivity of the ARVM parameters is investigated. The results show that the width hyper-parameter has apparent effects on the performance of the ARVMs, and the kernel type as well as the dataset size can result in different optimal hyper-parameter values. Meanwhile, the ARVM is compared to other techniques such as the generalized regression neural network and the support vector machines (SVM) for the stability inference of the slope cases by the inference accuracy function. The results suggest that the ARVMs have satisfactory generalization ability and perform better than the simple SVM and the applied neural networks.
slope stability geotechnical engineering machine learning soft computing parameter sensitivity