Abstract: The weights of all factors in weighted statistical model of dam monitoring were determined with engineering experience, which could result in the lack of the information of some factors. According to monitoring data, the regression coefficients and weights of weighted statistical model can be objectively determined by Particle Swarm Optimization algorithm, but for high dimension optimization, the algorithm has some deficiencies such as slow convergence and local minimums. In view of this, an improved Particle Swarm Optimization algorithm in consideration of the information of average location in particles is proposed. The learning factors are determined based on the information of average location in single particle and particle groups. The analysis results of earth-rock dam example show that the improved Particle Swarm Optimization algorithm enhances the ability of jumping out of the local minimum. The factors of weighted statistical model of safety monitoring for earth-rock dam are consistent in actual situation with this improved algorithm. Especially in the early stages of operation with few monitoring data, dam monitoring model based on improved Particle Swarm Optimization algorithm has better precision. The improved algorithm could be a new method of data analysis in dam monitoring field.

Key words: earth-rock dam, weighted statistical model, improved Particle Swarm Optimization algorithm, optimization computation, weight coefficient