水力发电2026，Vol.52Issue(1)：45-51,7.
基于DNN-NSGA-Ⅱ的高填方加筋边坡参数优化研究
Parameter Optimization of High-fill Reinforced Slope Based on DNN-NSGA-Ⅱ
摘要
Abstract
Taking a typical high-fill reinforced slope in Fujian Province as a case study,an intelligent optimization framework that integrates a deep neural network(DNN)with the Non-dominated Sorting Genetic Algorithm Ⅱ(NSGA-Ⅱ)is proposed for the multi-objective optimization of slope reinforcement design.Firstly,the finite element simulations are conducted to generate training samples for a DNN surrogate that maps key design parameters to stability response metrics.Then,the surrogate is embedded in the NSGA-Ⅱ search to obtain Pareto-optimal solutions with objectives of minimizing horizontal displacement and reinforcement quantity while maximizing the factor of safety.The analysis of the Pareto front and extraction of representative designs confirm the effectiveness of proposed method in balancing safety and economy,providing theoretical support and engineering guidance for optimizing high-fill reinforced slopes.关键词
高填方边坡/加筋设计/多目标优化/深度神经网络/非支配排序遗传算法Key words
high-fill slope/reinforcement design/multi-objective optimization/deep neural network/Non-dominated Sorting Genetic Algorithm Ⅱ分类
交通工程引用本文复制引用
ZHA Wenhua,TAN Xuejian,XU Tao,XU Yuanxin,LAI Siling,JI Chao..基于DNN-NSGA-Ⅱ的高填方加筋边坡参数优化研究[J].水力发电,2026,52(1):45-51,7.基金项目
江西省"双千计划"支持项目(DHSQT22021002) （DHSQT22021002）
东华理工大学研究生创新专项基金项目(DHYC-202419) （DHYC-202419）
