A Frequency-Based Ground Motion Clustering Approach for Data-Driven Surrogate Modeling of Bridges
Publication: Journal of Engineering Mechanics
Volume 149, Issue 9
Abstract
Machine learning–based methods, especially deep learning methods, have achieved great success in seismic response modeling due to their exceptional performance in capturing nonlinear features. However, imbalanced features of a limited training data set can significantly decrease the prediction accuracy of machine learning models. Therefore, this study proposes a novel frequency-based clustering approach for ground motion selection to generate a balanced training data set to improve the data-driven surrogate modeling of bridges. The hierarchical clustering method was developed to suppress the redundant information on the basis of a wavelet analysis of ground motion records. The proposed method was validated by a benchmark finite-element model of a girder bridge, in which long short-term memory (LSTM) neural network was used to predict the seismic responses given ground motion excitations. Specifically, the prediction performances of LSTM surrogate models trained using different data sets have been compared, while the influence of time-frequency characteristics of ground motions has been discussed in detail. The results indicated that the proposed method can provide a balanced training data set with a uniform distribution of time-frequency characteristics and effectively improve the prediction accuracy of deep learning–based surrogate models.
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Data Availability Statement
The data, model, and source codes generated or used during the study are available in a repository online in accordance with the original owner’s data retention policies (https://github.com/zhry10/GMClustering).
Acknowledgments
This work is financially supported by the National Natural Science Foundation of China (Grant Nos. 52127813 and 51838004), the National Key Research and Development Program of China (2020YFC1511900), the National Natural Science Foundation of China (Grant No. 52208466), the Transportation Science and Technology Project of Jiangsu Province (2021Y15), the Fundamental Research Funds for the Central Universities, and Postgraduate Research & Practice Innovation Program of Jiangsu Province (KYCX22_0214).
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Published In
Journal of Engineering Mechanics
Volume 149 • Issue 9 • September 2023
Copyright
© 2023 American Society of Civil Engineers.
History
Received: Jun 16, 2022
Accepted: May 16, 2023
Published online: Jul 12, 2023
Published in print: Sep 1, 2023
Discussion open until: Dec 12, 2023
ASCE Technical Topics:
- Artificial intelligence and machine learning
- Computer programming
- Computing in civil engineering
- Earthquake engineering
- Education
- Engineering fundamentals
- Finite element method
- Geotechnical engineering
- Geotechnical investigation
- Ground motion
- Methodology (by type)
- Model accuracy
- Models (by type)
- Neural networks
- Numerical methods
- Practice and Profession
- Seismic effects
- Seismic tests
- Tests (by type)
- Training
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