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In order to evaluate urban earthquake resilience, reliable structural modeling is needed. However, detailed modeling of a large number of structures and carrying out time history analyses for sets of ground motions are not practical at an urban scale. Reduced-order surrogate models can expedite numerical simulations while maintaining necessary engineering accuracy. Neural networks have been shown to be a powerful tool for developing surrogate models, which often outperform classical surrogate models in terms of scalability of complex models. Training a reliable deep learning model, however, requires an immense amount of data that contain a rich input-output relationship, which typically cannot be satisfied in practical applications. In this paper, we propose model-informed symbolic neural networks (MiSNN) that can discover the underlying closed-form formulations (differential equations) for a reduced-order surrogate model. The MiSNN will be trained on datasets obtained from dynamic analyses of detailed reinforced concrete special moment frames designed for San Francisco, California, subject to a series of selected ground motions. Training the MiSNN is equivalent to finding the solution to a sparse optimization problem, which is solved by the Adam optimizer. The earthquake ground acceleration and story displacement, velocity, and acceleration time histories will be used to train 1) an integrated SNN, which takes displacement and velocity states and outputs the absolute acceleration response of the structure; and 2) a distributed SNN, which distills the underlying equation of motion for each story. The results show that the MiSNN can reduce computational cost while maintaining high prediction accuracy of building responses.
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PI-LSTM: Physics-informed long short-term memory network for structural response modeling
Deep learning models have achieved remarkable accuracy for structural response modeling. However, these models heavily depend on having a sufficient amount of training data, which can be challenging and time-consuming to collect. Moreover, data-driven models sometimes struggle to adhere to physics constraints. Therefore, in this study, a physics-informed long short-term memory (PI-LSTM) network was applied to structural response modeling by incorporating physics constraints into deep learning. The physics constraints were modified to accommodate the characteristics of both linear and nonlinear cases. The PI-LSTM network, inspired by and compared with existing physics-informed deep learning models (PhyCNN and PhyLSTM), was validated using the numerical simulation results of the single-degree-of-freedom (SDOF) system and the experimental results of the six-story building. Additionally, the PI-LSTM network underwent thorough investigation and validation across the four cases of the SDOF system and numerical simulation results of the six-story building with the comparison of the regular LSTM. The results indicate that the PI-LSTM network outperformed the regular LSTM models in terms of accuracy. Furthermore, the PI-LSTM network exhibited a more concentrated and higher accuracy range when analyzing the results of both the SDOF system and the six-story building. These findings demonstrate that the PI-LSTM network presents a reliable and efficient approach for structural response modeling.
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- Award ID(s):
- 2308924
- PAR ID:
- 10548515
- Editor(s):
- NA
- Publisher / Repository:
- Elsevier
- Date Published:
- Journal Name:
- Engineering Structures
- Edition / Version:
- 1
- Volume:
- 292
- Issue:
- C
- ISSN:
- 0141-0296
- Page Range / eLocation ID:
- 116500
- Subject(s) / Keyword(s):
- Deep learning Physics-informed long short-term memory network Structural response modeling Physics constraint
- Format(s):
- Medium: X Size: 5 Other: pdf
- Size(s):
- 5
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1016/j.engstruct.2023.116500
