基于DBO-GRNN神经网络的冰水堆积物渗透系数预测  

Prediction of permeability coefficient of ice-water accumulation based on DBO-GRNN neural network

作　　者：彭俊皓 魏玉峰[1] 李常虎 王群 李征征[2] PENG Junhao;WEI Yufeng;LI Changhu;WANG Qun;LI Zhengzheng(State Key Laboratory of Geohazard Prevention and Geoenvironment Protection,Chengdu University of Technology,Chengdu 610059,China;PowerChina Northwest Engineering Corporation Limited,Xi′an 710065,China)

机构地区：[1]成都理工大学地质灾害防治与地质环境保护国家重点实验室,四川成都610059 [2]中国电建集团西北勘测设计研究院有限公司,陕西西安710065

出　　处：《人民长江》2025年第2期167-174,共8页Yangtze River

基　　金：国家自然科学基金项目(42072303)。

摘　　要：冰水堆积物具有粒径范围宽、颗粒组成不均匀的特点,此类颗粒级配特征会较大程度上影响其渗透特性,从而影响水利水电工程的安全运行。以易贡藏布流域夏曲水电站冰水堆积物为研究对象,设计开展20组室内常水头渗透试验,建立了考虑级配面积的渗透系数计算经验公式;在此基础上,以试验数据为样本建立蜣螂算法(DBO)优化的GRNN神经网络,以特征粒径d 10~d 100、级配面积S为输入变量,预测冰水堆积物的渗透系数;并开展4组现场单环渗透试验验证DBO-GRNN模型精度。结果显示:该模型的渗透系数预测值与试验值能较好地吻合,误差在5%以内,而经验公式预测值、传统BP神经网络预测值与试验值的误差最大分别为61.29%和37.50%,表明DBO-GRNN神经网络可以较为准确地获取冰水堆积物的渗透系数。Ice-water accumulations are characterized by a wide range of particle sizes and heterogeneous compositions,which significantly influence their permeability properties,consequently,the safe operation of hydraulic and hydropower projects.This study focuseD on the ice-water accumulations in the Xiaqu Hydropower Station,Yigong-Zangbu Basin.A total of 20 sets of indoor constant-head permeability tests were conducted,and an empirical formula was developed to calculate the permeability coefficient,taking the gradation area into consideration.Based on these results,a Generalized Regression Neural Network(GRNN)model optimized by Dung Beetle Optimization(DBO)was constructed,with characteristic particle sizes(d 10~d 100)and gradation area(S)as input variables to predict the permeability coefficient of the ice-water accumulations.Four sets of field single-ring permeability tests were then carried out to verify the accuracy of the DBO-GRNN model.The results showed that the predicted permeability coefficients from this model were in excellent agreement with the experimental values,with an error margin of less than 5%.In contrast,the errors between the predictions of the empirical formula and the traditional BP neural network model and the test values reached up to 61.29%and 37.50%,respectively.These findings demonstrate that the DBO-GRNN model can accurately estimate the permeability coefficient of ice-water accumulations.

关 键 词：冰水堆积物 渗透系数 颗粒级配 DBO-GRNN神经网络 渗透试验 夏曲水电站 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程] TV223.4[自动化与计算机技术—控制科学与工程]