基于GPR代理模型和GA-APSO混合优化算法的软基水闸底板脱空反演

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摘要软基水闸底板脱空是水闸在长期服役期间受水流侵蚀等环境因素影响所产生的一种危害极大且难以察觉的病害。由于其病害部位于水下，传统方法难以检测，本文提出一种基于高斯过程回归（Gaussian Process Regression，GPR）代理模型和遗传—自适应惯性权重粒子群（Genetic Algorithm-Adaptive Particle Swarm Optimization，GA-APSO）混合优化算法的水闸底板脱空动力学反演方法，用于检测软基水闸底板脱空。首先，构建表征软基水闸底板脱空参数和水闸结构模态参数之间非线性关系的GPR代理模型；其次，基于GPR代理模型与水闸实测模态参数建立脱空反演的最优化数学模型，将反演问题转化为目标函数最优化求解问题；最后，为提高算法寻优计算的精度，提出一种GA-APSO混合优化算法对目标函数进行脱空反演计算，并提出一种更合理判断反演脱空区域面积和实际脱空区域面积相对误差的指标—面积不重合度。为验证所提方法性能，以一室内软基水闸物理模型为例，对2种不同脱空工况开展研究分析，结果表明，反演脱空区域面积和模型实际设置脱空区域面积的相对误差分别为8.47%、10.77%，相对误差值较小，所提方法能有效反演出水闸底板脱空情况，可成为软基水闸底板脱空反演检测的一种新方法。

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	李火坤1
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	刘双平1
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	唐义员1
	方静1

关键词 ：软基水闸, 底板脱空反演, 动力学方法, GPR代理模型, GA-APSO混合优化算法

Abstract：The floor void of sluice on soft foundation is a very harmful and undetectable disease caused by environmental factors such as water erosion during long-term service of sluice gates. Since the damage area is underwater, it is difficult to be detected by traditional methods. In this paper, a dynamic inversion method based on Gaussian Process Regression(GPR) surrogate model and Genetic Algorithm-Adaptive Particle Swarm Optimization(GA-APSO) hybrid optimization algorithm is proposed to detect the voiding of soft foundation sluice floor.. Firstly, a GPR surrogate model is constructed to characterize the nonlinear relationship between the emptying parameters and the modal parameters of the sluice structure; secondly, an optimization mathematical model for emptying parameter inversion is established based on the GPR surrogate model and the measured modal parameters of the sluice, and the inversion problem is transformed into an objective function optimization solution problem; Finally, in order to improve the accuracy of the algorithm, a GA-APSO hybrid optimization algorithm is proposed to perform the inversion calculation of the objective function for the emptying, and a more reasonable index to judge the relative error between the inversion voiding area and the actual voiding area—area non-coincidence is proposed. In order to verify the performance of the proposed method, the physical model of an indoor soft foundation sluice is used as an example, and two different emptying conditions are set for analysis. The results show that the relative errors of the inverse emptying area and the actual set emptying area of the model are 8.47% and 10.77%, respectively, with smaller relative error values. and the proposed method can effectively inverse perform the emptying of the sluice bottom plate, which can be a new method for inversion detection of soft foundation sluice floor.

Key words： soft foundation sluice bottom plate emptying inversion Dynamic method GPR surrogate model GA-APSO hybrid optimization algorithm

收稿日期: 2022-07-14 出版日期: 2023-07-28

引用本文:

李火坤1，柯贤勇1,2，黄伟1，刘双平1,2，唐义员1，方静1. 基于GPR代理模型和GA-APSO混合优化算法的软基水闸底板脱空反演[J]. 振动与冲击, 2023, 42(14): 1-10.
LI Huokun1，KE Xianyong1,2，HUANG Wei1，LIU Shuangping1,2，TANG Yiyuan1，FANG Jing1. Inversion study of soft foundation sluice bottom plate emptying based on a GPR surrogate model and a GA-APSO hybrid optimization algorithm. JOURNAL OF VIBRATION AND SHOCK, 2023, 42(14): 1-10.

链接本文:

http://jvs.sjtu.edu.cn/CN/ 或 http://jvs.sjtu.edu.cn/CN/Y2023/V42/I14/1

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