Slope reliability evaluation based on multi-objective grey wolf optimization-multi-kernel-based extreme learning machine agent model

Slope reliability assessment is an efficient methodology for landslide risk mitigation. However, it is also a challenging task owing to various uncertainties and randomness of soil properties in geotechnical engineering. As the slope is a complicated system with high nonlinearity, the stability analysis with high precision and reliability fails to be obtained from the traditional response surface method. To counteract this limitation, this paper proposes a technique based on machine learning for slope reliability evaluation, namely a multi-objective grey wolf optimization-multi-kernel-based extreme learning machine model based on strength reduction method. The probability of slope failure is estimated by using the developed model when connected with Monte Carlo Simulation. Model application to probabilistic evaluation of slope was conducted by two typical case studies. The results show that as compared with Monte Carlo Simulation and other traditional techniques, the developed machine learning method, which combines the advantages of strength reduction method, the multi-objection grey wolf optimization and multi-kernel-based extreme learning machine, demonstrates better computational performance and applicability, and achieves more accurate and reliable failure probability. Further, the proposed approach can also be applied to predict probability of slope failure with consideration of different coefficient of variation and correlation of soil properties. Hence, improved failure probability can be obtained from the developed method, which could offer crucial information for decisions with regard to early landslide warning.