原文传递
Spatiotemporal Deep Learning for Bridge Response Forecasting
| 题名： | Spatiotemporal Deep Learning for Bridge Response Forecasting |
| 正文语种： | eng |
| 作者： | Zhang, Ruiyang;Meng, Libo;Mao, Zhu;Sun, Hao |
| 作者单位： | Southeast Univ Sch Civil Engn Nanjing 211189 Peoples R China|Northeastern Univ Dept Civil & Environm Engn Boston MA 02115 USA;China Merchants Chongqing Commun Technol Res & De Dept Bridge & Struct Engn Chongqing 400067 Peoples R China;Univ Massachusetts Lowell Dept Mech Engn Lowell MA 01854 USA;Northeastern Univ Dept Civil & Environm Engn Boston MA 02115 USA|MIT Dept Civil & Environm Engn Cambridge MA 02139 USA |
| 关键词： | Deep learning;Response forecasting;Spatiotemporal learning;Convolutional long-short term memory;ConvLSTM |
| 摘要： | Accurate prediction/forecasting of the future response of civil infrastructure plays an essential role in health monitoring and safety assessment. However, the complex latent dynamics within the field sensing measurements makes the forecasting task challenging. To this end, this paper leverages the recent advances in deep learning and proposes a spatiotemporal learning framework to forecast structural responses with strong temporal dependencies and spatial correlations. The key concept is to establish a convolutional long-short term memory (ConvLSTM) network to learn spatiotemporal latent features from data and thus establish a surrogate model for structural response forecasting. The proposed approach is applied to predict the strain response for a concrete bridge with over three-year measurements available. A comparative study is also conducted against a traditional temporal-only network to highlight the forecasting performance of the proposed approach. Convincing results demonstrate that the ConvLSTM approach is a promising, reliable, and computationally efficient approach that is capable of accurately forecasting the dynamical response of civil infrastructure in a data-driven manner. (C) 2021 American Society of Civil Engineers. |
| 出版年： | 2021 |
| 期刊名称： | Journal of structural engineering |
| 卷： | 147 |
| 期： | 6 |
相关文献
- Electric vehicle charging demand forecasting using deep learning model
- Spatiotemporal Deep-Learning Networks for Shared-Parking Demand Prediction
- Explainable Stacking-Based Learning Model for Traffic Forecasting
- Expressway Traffic Incident Detection Using a Deep Learning Approach Based on Spatiotemporal Features with Multilevel Fusion
- Automated Bridge Inspection Image Retrieval Based on Deep Similarity Learning and GPS
- Physics-Informed Spatiotemporal Learning Framework for Urban Traffic State Estimation
检索历史
应用推荐
