基于 GA-PSO 混合优化 BP 的面板堆石坝爆破料压实质量评价.

Compaction quality evaluation is one of the key technologies of dam intelligent construction, which has an important impact on the safety and stability of dam body. However, there is no consensus on its evaluation method at present. In this paper, based on the Artash concrete face rockfill dam in Xinjiang and combined with the results of field test, the BP neural network algorithm based on genetic algorithm and particle swarm optimization (GA-PSO-BP) was proposed. The accuracy and superiority of the model were proved by comparing with BP model, GA-BP model and PSO-BP model. The result shows that the GA-PSO-BP model, proposed by the paper, has faster convergence speed and better performance. And, the BP neural network compaction quality evaluation model, based on GA-PSO hybrid optimization, has relatively higher accuracy, which can be used to evaluate the compaction quality of Xinjiang Artash concrete face rockfill dam under similar working conditions. [ABSTRACT FROM AUTHOR]