ANN-based dredging operation parameters optimization and software development

In this paper, the trailing suction hopper dredger dredging production is used to be an optimization objective. By analyzing the impacts of dredging operation behaviors and system uptime factors, an artificial neural network algorithm for parameter optimization of dredging operations is proposed. Based on the field construction parameters of the trailing suction hopper dredger, the method integrates the genetic algorithm for solving common the dredging job of production optimization problem, and gives the optimized parameters. Adopting the field collecting data, the simulation test is carried out on the VC++ platform. The simulation results show that the method provided in this paper can be effective to carry on the dredging operations modeling and parameter optimization problem. Given the same operating conditions, it is able to meet the production constraints. Meanwhile, based on this method, a dredging operation decision support software program is developed with VC++. The software is based on actual demand, the use of algorithms automatically give the construction parameters of the optimization.