Spatiotemporal Deep Learning for Bridge Response Forecasting
Publication: Journal of Structural Engineering
Volume 147, Issue 6
Abstract
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.
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Data Availability Statement
The data, model, and the source codes generated or used during the study will be available in a repository online in accordance with the original owner’s data retention policies (https://github.com/zhry10/ConvLSTM).
Acknowledgments
This work was partially supported by the Fundamental Research Funds for the Central Universities, which is greatly acknowledged.
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Published In
Journal of Structural Engineering
Volume 147 • Issue 6 • June 2021
Copyright
© 2021 American Society of Civil Engineers.
History
Received: Apr 9, 2020
Accepted: Jan 25, 2021
Published online: Mar 31, 2021
Published in print: Jun 1, 2021
Discussion open until: Aug 31, 2021
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