岩土力学2026，Vol.47Issue(1)：323-336,14.DOI:10.16285/j.rsm.2025.0001
基于遗传算法与神经网络的逆向侵蚀管涌通道表征方法
Genetic algorithm-optimized back propagation neural network for the characterization of backward erosion piping channels
摘要
Abstract
The use of levees is one of the most prevalent and effective strategies for flood protection.However,owing to the ageing of levees,inconsistent reinforcement efforts,and complex geological conditions,hazards such as piping frequently arise during flood seasons,which lead to significant and often irreparable damage.This study investigates backward erosion piping(BEP)in the foundations of double-structured levees via a back-propagation(BP)neural network optimized by a genetic algorithm(GA).The primary contributions of this study include:1)the construction of a training dataset through numerical simulations of BEP in heterogeneous aquifers and validation of the dataset against laboratory sandbox piping tests to verify its reliability;2)the extraction of head H and permeability coefficient K data from Groups II,III,and IV in the BEP laboratory tests,augmentation of the dataset,and optimization of the GA–BP model to characterize test results in Group I,where the results demonstrate that the optimized model more accurately characterizes areas where the K≤1.0 cm/s;and 3)the use of the optimized GA-BP model to characterizes the development of a BEP channel.The results indicate that the model accurately captures the general trends.However,minor discrepancies remain in the characterized channel location and size compared with the actual conditions.In conclusion,this study offers an effective tool for characterizing BEP and demonstrates the potential of the GA–BP network model for practical applications in this field.关键词
逆向侵蚀管涌/管涌通道/BP神经网络/遗传算法/渗透系数Key words
backward erosion piping/piping channel/back propagation neural network/genetic algorithm/permeability coefficient分类
建筑与水利引用本文复制引用
梁越,饶育锋,赵卓越,许彬,杨晓霞,夏日风,邓惠丹,RASHID Hafiz Aqib..基于遗传算法与神经网络的逆向侵蚀管涌通道表征方法[J].岩土力学,2026,47(1):323-336,14.基金项目
国家自然科学基金(No.52379097,No.52509138) （No.52379097,No.52509138）
广西科技计划(No.桂科 AA23062023) （No.桂科 AA23062023）
重庆交通大学研究生科研创新基金(No.2025S0028) （No.2025S0028）
重庆市水利科技项目(No.CQSLK-2024005) （No.CQSLK-2024005）
重庆市教委科学技术研究计划(No.KJQN202300744)资助. This work was supported by the Natural Science Foundation of China(52379097,52509138),the Guangxi Science and Technology Program(GuiKe AA23062023),the Graduate Scientific Research and Innovation Foundation of Chongqing Jiaotong University(2025S0028),the Chongqing Water Conservancy Technology Project(CQSLK-2024005)and the Science and Technology Research Program of Chongqing Municipal Education Commission(KJQN202300744). （No.KJQN202300744）
