Extreme learning machine-based surrogate model for analyzing system reliability of soil slopes

Geotechnical engineering problems are characterised by many sources of uncertainty, and reliability analysis is needed to take the uncertainties into account. An intelligent surrogate model based on extreme learning machine is proposed for slope system reliability analysis. The weights and bias which play an important role in the performance of ELM are optimised by a nature inspired artificial bee colony algorithm. The system failure probability of soil slopes is estimated by Monte Carlo simulation via the proposed surrogate model. Experimental results show that the proposed method is feasible, effective and simple to implement system reliability analysis of soil slopes.