Locating critical sliding surface of slopes by estimation of distribution algorithm

Most of the optimization algorithms to solve the slope critical sliding surface have the disadvantages of complex structure, difficult to determine the parameter value, and poor optimization effect. This study introduced the estimation of the distribution algorithm based on the Gaussian distribution model, and combined with the sliding surface calculation and analysis model using the simplified Bishop method, to establish a new critical sliding surface search method with simple biological collaboration and competition ideas; secondly, a local search method for the 3-degrees of freedom was designed to compensate for the poor local search performance of the estimation of distribution algorithm. The standard and improvement methods were applied to the three calculation examples of increasing slope section complexity, respectively. The orthogonal experimental results from the standard method were validated by range analysis and multivariate analysis of variance, and the comparative analysis of the calculation of