Dynamic optimization of compaction process for rockfill materials

The automatic adjustment of compaction parameters during a compaction process for increased efficiency is an important part in intelligent compaction (IC). However, there is a lack of effective methods for optimizing compaction parameters. This study proposes a compaction process-dynamic optimization method (CPDOM) based on a genetic algorithm (GA). The purpose of the CPDOM is to determine the optimal compaction plan based on the current compaction state to complete compaction within the shortest time. Therefore, field compaction tests with rockfill materials were conducted with various roller speeds and frequencies. Further, a relative density incremental function (RDIF) was established via a nonlinear multiple regression method. Based on the RDIF and the concept of a multistage decision process, CPDOM optimizes the compaction process globally via a GA to minimize the remaining compaction time. The main advantage of CPDOM is that the compaction parameters are optimized considering the overall optimal solution. Moreover, contrast tests were conducted in the Qianping reservoir. According to the results, the compaction efficiency improved by 13.1% through CPDOM optimization. Thus, CPDOM can be employed for engineering applications.