Development of an adaptive relevance vector machine approach for slope stability inference

Abstract

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.

Comparative Analysis of ANN-ICA and ANN-GWO for Crop Yield Prediction

“…The empires tend to widen their territories by controlling more colonies from the other empires. This makes competition among the empires in which the strongest empire dominates and controls the weaker colonies [14].…”

Prediction of safety factors for slope stability: comparison of machine learning techniques

“…The very large values of cohesion, which are outlier cases, are perhaps for rock slopes. The outcome of Zhang et al (2014) study explained that the improvements in models' performance could be achieved with the removal of these cases. Therefore, these outliers (17 slope cases) should be eliminated, and 327 slope cases remained.…”

“…The medians of the c and the γ are around 10 kN/m 2 and 20 kN/m 3 , respectively. As per the study of Liu et al (2014), Zhang et al (2014), andLin et al (2018), it could be acquired that these slopes are mostly soil slopes or heavily fractured rock. The very large values of cohesion, which are outlier cases, are perhaps for rock slopes.…”

Prediction and Analysis of Slope Stability Based on IPSO-SVM Machine Learning Model

“…To ensure the reliability of the database, the sample data should comply with the following standards: all the slope failure deformation conforms to the basic deformation law. In this study, 28 of data on slope from the literature [42,43] are taken as samples. The whole sample data are divided into two parts.…”

“…Kang et al presented an intelligent response surface method to evaluate the system failure probability of soil slopes based on least squares support vector machines (LSSVM) and particle swarm optimization [27]. Zhang et al presented the adaptive relevance vector machine (ARVM) for stability inference of soil slopes [28]. Liu et al presented an approach of a fast robust neural network, named the extreme learning machine (ELM) in slope stability evaluation and prediction [29].…”