Abstract:

Aiming to the problem about expensive cost and low efficiency of stochastic global optimization technology for displacement back analysis in geotechnical engineering, a novel cooperative optimization algorithm based on particle swarm optimization (PSO) algorithm and Gaussian process (GP) machine learning for back analysis is proposed. In order to reduce the cost of numerical calculation during displacement back analysis, the new method not only takes advantage of the global optimization performance of particle swarm optimization with quick convergence, but also uses GP model to summarize the historic experience during searching optimum solution and predicting the most perspective zone for guiding the flying of particle swarm. The results of performance analysis for different benchmark functions and application to a tunnel engineering show that the method is feasible to reduce remarkably computational time-consuming for solving problem of displacement back analysis based on numerical calculation.

Key words: displacement back analysis, optimization, particle swarm optimization, Gaussian process machine learning