Optimal design of soil dynamic compaction using genetic algorithm and fuzzy system

Abstract

Computational intelligent techniques, such as fuzzy and genetic algorithm, have proven to be useful in modeling of complex nonlinear phenomena such as dynamic compaction. Dynamic compaction method is used to improve the mechanical behavior of underlying soil layers especially loose granular materials. The method involves the repeated impart of high-energy impacts to the soil surface using steel or concrete tampers with weights ranging 10–40 ton and with drop heights ranging 10–40 m. A relatively exact estimation of dynamic compaction level is of major concern to geotechnical engineers. This paper develops a fuzzy set base method for the analysis of dynamic compaction phenomenon. In this model, the input variables are tamper weight, height of tamper drop, print spacing, tamper radius, number of impact and soil layer geotechnical properties. The main shortcoming of this technique is uncertainty to locate the best sketch of dynamic compaction to gain maximum effect of this method of soil improvement. Therefore, this paper describes the incorporation of genetic algorithm methodology using fuzzy system for determining the optimum design of dynamic compaction. Subsequently, it will be shown that the genetic algorithm has some abilities in the optimization of dynamic compaction design. Also different manners of this algorithm are compared and then the optimized structure of genetic algorithm will be suggested for dynamic compaction.