首页|基于GRU和MOGWO的软基水闸底板脱空动力学智能反演

基于GRU和MOGWO的软基水闸底板脱空动力学智能反演

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软基水闸在水流侵蚀等因素作用下易发生底板脱空现象,极大地威胁水闸的安全运行.针对软基水闸底板脱空检测问题,提出一种基于门控循环单元(Gate Recurrent Unit,GRU)神经网络代理模型和多 目标灰狼算法(Multi-Objective Grey Wolf Optimizer,MOGWO)的软基水闸底板脱空动力学反演方法.基于GRU神经网络构建表征软基水闸结构模态参数与脱空参数间非线性关系的数学代理模型,基于水闸结构固有频率、归一化振型建立软基水闸脱空参数反演的多目标优化函数,并采用MOGWO优化算法求解多目标优化问题的Pareto最优解.将所提方法应用于室内软基水闸物理模型两种脱空工况的反演计算.GRU神经网络代理模型精度优于多层前馈(Back Propagation,BP)神经网络代理模型及三阶多项式响应面模型,且反演脱空面积和模型实际脱空面积的相对误差分别为6.76％、5.58％,反演效果明显优于单目标反演方法.
Intelligent Dynamic Inversion of Soft Foundation Sluice Floor Void Based on GRU and MOGWO
Soft foundation sluice is vulnerable to floor void due to water erosion and other factors,which greatly threatens sluice safety.To solve the detection of soft foundation sluice floor void,the dynamic detection method was proposed based on"Gate Recurrent Unit(GRU)neural network surrogate model and multi-objective gray wolf optimal algorithm(MOGWO)".The surrogate model was constructed based on GRU neural network to characterize the nonlinear relationship between floor void parameters and modal parameters of soft foundation sluice;The multi-objective optimization function was established for void parameters inversion based on the intrinsic frequency and normalized vibration mode.The MOGWO was used to solve the multi-objective optimization problem of void parameters inversion.The proposed method was applied to the inversion of two void conditions of an indoor physical soft foundation sluice model.The GRU surrogate model accuracy is better than the Back Propagation(BP)surrogate model and the polynomial response surface model.The relative errors between the inverse void area and actual void area of the model are 6.76％and 5.58％for the two void conditions.The results indicate that the proposed method is superior to the single-objective inversion method.

soft foundation sluicefloor void inversiondynamic methodsurrogate modelGRU neural networkmulti-objective grey wolf optimal algorithm(MOGWO)

李火坤、唐义员、黄伟、柳波、王文韬、朱慧琪

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南昌大学工程建设学院,江西南昌 330031

软基水闸 底板脱空反演 动力学方法 代理模型 GRU神经网络 MOGWO优化算法

国家自然科学基金江西省双千计划科技创新高端人才项目江西省水利厅重大科技项目江西省自然科学基金重点项目

52079061jxsq2023201017202325ZDKT0520232ACB204027

2024

10.16058/j.issn.1005-0930.2024.01.006

应用基础与工程科学学报
中国自然资源学会

应用基础与工程科学学报

CSTPCD北大核心
影响因子：0.895
ISSN：1005-0930
年,卷(期)：2024.32(1)
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