改进生成对抗网络及其在结构非线性模型修正中的应用  

Application of the improved generative adversarial network for nonlinear structural model updating

作　　者：王俊 辛宇 王佐才[1,2,3] 戈壁 WANG Jun;XIN Yu;WANG Zuo-cai;GE Bi(College of Civil Engineering,Hefei University of Technology,Hefei 230009,China;Anhui Province Infrastructure Safety Inspection and Monitoring Engineering Laboratory,Hefei 230009,China;Engineering Research Center of Safety-critical Industrial Measurement and Control Technology of the Ministry of Education,Hefei 230009,China)

机构地区：[1]合肥工业大学土木与水利工程学院,安徽合肥230009 [2]安徽省基础设施安全检测与监测工程实验室,安徽合肥230009 [3]安全关键工业测控技术教育部工程研究中心,安徽合肥230009

出　　处：《振动工程学报》2023年第4期934-945,共12页Journal of Vibration Engineering

基　　金：国家自然科学基金优秀青年科学基金资助项目(51922036);安徽省重点研发计划资助项目(1804a0802204);中央高校基本科研业务费专项资金资助项目(JZ2020HGPB0117)。

摘　　要：提出改进生成对抗网络(Generative Adversarial Network,GAN)并在结构非线性模型修正中成功应用。在改进的GAN中,通过引入代理模型的方式,增强网络判别器对非线性结构各节点响应关系特征的学习能力;为避免传统GAN存在的梯度消失问题,使用跳跃连接和密集连接等方式加强网络层之间的信息交流,并且通过引入组合目标函数,构建模型输入响应与输出参数之间的映射关系实现网络训练。在进行结构非线性模型修正时,结构的动力响应作为网络模型的输入,训练好的GAN模型能够根据输入数据的特征,输出非线性模型参数的最优值,从而实现结构非线性模型修正。通过对地震荷载作用下的12层钢筋混凝土框架结构进行数值模拟,验证了方法的可行性,并通过对比基于卷积神经网络的非线性模型修正结果,验证所提方法的优越性;最后进一步结合地震荷载作用下的悬臂铝梁振动台实验,验证了该非线性模型修正方法的可靠性。This paper proposes a nonlinear structural model updating approach based on the improved Generative Adversarial Net⁃work(GAN).In the improved GAN,the ability of the network discriminator to learn the response characteristics of nonlinear structures is enhanced by using the surrogate models.To avoid the problem that the gradient disappearance problem existing in tra⁃ditional GAN,information exchange between network layers is strengthened by means of skip connection and dense connection,and a combined objective function is added to the improved GAN,whose aim is to construct the mapping relationship between structural responses and model parameters of a nonlinear structure to realize the network training.In nonlinear model updating,structural dynamic responses are considered as input of the network.After training,the constructed GAN network can predict the optimal value of nonlinear model parameters by learning the characteristics of input data,and the nonlinear model updating can be performed.To validate the feasibility of the proposed method,a 12-story reinforced concrete frame structure under earthquake exci⁃tations is conducted as numerical simulation,and the accuracy of the proposed method is further verified by comparing the results of nonlinear model modification based on convolutional neural network.Finally,the proposed method is applied to a nonlinear model updating of a cantilever beam shaking table experiment under earthquake excitation,and the same good updating results are achieved.

关 键 词：非线性模型修正 改进生成对抗网络 非线性结构 网络训练 

分 类 号：TU311.3[建筑科学—结构工程] O322[理学—一般力学与力学基础]