[1]郭茂祖,张欣欣,赵玲玲,等.面向结构地震响应预测的Phy-LInformers方法[J].智能系统学报,2024,19(4):1027-1041.[doi:10.11992/tis.202404002]
GUO Maozu,ZHANG Xinxin,ZHAO Lingling,et al.Phy-LInformers approach toward structural seismic response prediction[J].CAAI Transactions on Intelligent Systems,2024,19(4):1027-1041.[doi:10.11992/tis.202404002]
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GUO Maozu,ZHANG Xinxin,ZHAO Lingling,et al.Phy-LInformers approach toward structural seismic response prediction[J].CAAI Transactions on Intelligent Systems,2024,19(4):1027-1041.[doi:10.11992/tis.202404002]
面向结构地震响应预测的Phy-LInformers方法
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
19
期数:
2024年第4期
页码:
1027-1041
栏目:
人工智能院长论坛
出版日期:
2024-07-05
- Title:
- Phy-LInformers approach toward structural seismic response prediction
- Keywords:
- seismic response prediction; physical knowledge; physics-informed deep learning; time series forecasting; few shot learning; Informer; long short-term memory; Phy-LInformers
- 分类号:
- TP18
- 摘要:
- 为了准确评估建筑结构在地震作用下的动力特性和延性性能并促进韧性城乡的建设，本文提出了一种名为Phy-LInformers的深度学习框架，该框架综合运用了长短期记忆网络（long short-term memory，LSTM）、Transformer类模型Informer以及物理先验知识，以实现对建筑结构非线性地震响应的精确预测。该框架的核心思想是结合Informer的编码（Encoder）和解码（Decoder）结构，在Decoder部分引入了LSTM以预测建筑物先前的历史状态信息。同时，通过将现有的物理知识（例如预测变量之间的状态依赖关系和运动控制方程等）编码到损失函数中，对Phy-LInformers进行指导并约束其学习空间，同时提高有限训练数据下深度学习模型的预测性能。随后，通过2个模拟数据算例验证所提框架的性能。结果表明，所提出的Phy-LInformers是一种鲁棒性良好、预测性能优秀的非线性地震响应预测方法，即使在训练样本非常少（例如仅有10条）的情况下依然能准确预测结构在地震作用下的动力响应。这一特性使得Phy-LInformers在工程实践中具有可行性，并且在建筑结构抗震性能评价领域展现出良好的应用前景。
- Abstract:
- In order to accurately assess the dynamic and ductile properties of building structures under seismic action and to promote the construction of resilient cities and towns, in this study, we introduce a novel deep learning framework denoted as Phy-LInformers, which integrates long short-term memory (LSTM), Transformer-based model Informer, and prior physical knowledge to achieve precise prediction of nonlinear seismic responses in building structures. The central concept of Phy-LInformers lies in fusing the Encoder and Decoder architectures of Informer, while integrating LSTM within the Decoder component to forecast preceding historical states of the building. Meanwhile, the learning space of the Phy-LInformers’ training process is instructed and constrained by encoding existing physical knowledge (e.g., state dependencies between predictor variables and motion control equations, etc.) into the loss function. And at the same time, the prediction performance of the deep learning model is improved with limited training data. Subsequently, the satisfactory performance of the proposed framework is successfully demonstrated through two illustrative examples. The results demonstrate that the proposed Phy-LInformers is a nonlinear seismic response prediction method with better robustness and superior prediction performance, which can still accurately predict the dynamic response of a structure under seismic excitation even with very few training samples (e.g., only 10 samples). This feature makes Phy-LInformers feasible for engineering practice and shows promising application prospects in the field of seismic performance evaluation of building structures.
- 参考文献/References:
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备注/Memo
收稿日期:2024-04-07。
基金项目:国家自然科学基金项目（62271036）；北京市自然科学基金项目（4232021）.
作者简介:郭茂祖，教授，博士生导师，主要研究方向为机器学习、智能建造、智慧城市、生物信息学。主持和参与国家自然科学基金面上项目、北京市属高校高水平创新团队建设计划项目和北京市教委科技计划重点项目等。曾获教育部高等学校科学研究优秀成果自然科学二等奖、省科技进步二等奖等。发表学术论文200余篇。E-mail：guomaozu@bucea.edu.cn。;张欣欣，硕士研究生，主要研究方向为深度学习、地震响应预测。E-mail：1972000436@qq.com。;赵玲玲，副教授，博士，主要研究方向为机器学习方法与应用，主持和参与多项国家自然科学基金项目。发表学术论文50余篇。E-mail：zhaoll@hit.edu.cn
通讯作者:赵玲玲. E-mail：zhaoll@hit.edu.cn
更新日期/Last Update:
1900-01-01