Embedding Prior Knowledge into Data-Driven Structural Performance Prediction to Extrapolate from Training Domains
Publication: Journal of Engineering Mechanics
Volume 149, Issue 12
Abstract
Machine learning (ML)–based data-driven approaches have become increasingly prevalent for predicting structural performance. Because a properly trained ML model can learn hidden patterns in databases of experimental samples, ML model performance sometimes exceeds that of mechanics-based predictive models, especially when the latter either conflates multiple phenomena into a single term or does not represent them at all. Nevertheless, there is almost always an inherent gap between the domain of the collected data—used in developing the predictive models—and the desired prediction domain. For instance, structural testing is often carried out on scaled components with approximated boundary conditions. Although mechanics-based models can usually bridge the said gaps, discrepancies are a perpetual challenge to the extrapolation capabilities of data-driven models. To address this issue, a new data-driven approach, a prior-knowledge embedded data-driven approach (PkeDA), is proposed herein, which integrates valuable prior knowledge embedded in empirical formulas into a data-driven model. A particular realization of this approach, based on artificial neural networks, PkeDA-ANN, is developed. To verify its feasibility and compare it with a model trained through a classic data-driven approach (CDA), a case study for predicting the bending capacities of reinforced concrete beams is carried out. The results indicate that when the model is trained via CDA, it exhibits good interpolation capabilities, as expected, but its extrapolation capabilities are observed to be severely limited. Under identical conditions, the proposed PkeDA was observed to have not only excellent interpolation and extrapolation capabilities but also to dramatically surpass the classic empirical formulas that have been developed for predicting the capacity of concrete beams under bending. As such, PkeDA appears to be a viable approach for developing highly accurate data-driven models when prior knowledge is available.
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Data Availability Statement
All data, models, or codes that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
The authors greatly appreciate the financial support from National Key R&D Program of China (No. 2021YFB1600300), National Natural Science Foundation of China (No. 52008027), Natural Science Foundation of Jiangsu Province (No. BK20211564), and Fundamental Research Funds for the Central Universities, CHD (No. 300102213209).
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Information & Authors
Information
Published In
Journal of Engineering Mechanics
Volume 149 • Issue 12 • December 2023
Copyright
© 2023 American Society of Civil Engineers.
History
Received: Nov 12, 2022
Accepted: Jul 31, 2023
Published online: Oct 3, 2023
Published in print: Dec 1, 2023
Discussion open until: Mar 3, 2024
ASCE Technical Topics:
- Artificial intelligence and machine learning
- Beams
- Bending (structural)
- Case studies
- Computer programming
- Computing in civil engineering
- Concrete
- Concrete beams
- Continuum mechanics
- Dynamics (solid mechanics)
- Education
- Engineering fundamentals
- Engineering materials (by type)
- Engineering mechanics
- Feasibility studies
- Materials engineering
- Methodology (by type)
- Model accuracy
- Models (by type)
- Practice and Profession
- Reinforced concrete
- Research methods (by type)
- Solid mechanics
- Structural dynamics
- Structural engineering
- Structural members
- Structural systems
- Training
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