Design-Oriented Machine-Learning Models for Predicting the Shear Strength of Prestressed Concrete Beams
Publication: Journal of Bridge Engineering
Volume 28, Issue 4
Abstract
The shear behavior of prestressed concrete (PC) beams is a complex problem because there are many influential parameters involved. Currently, the code-based shear strength of PC beams is primarily based on empirical equations, which tend to be overconservative and are unable to generalize to different cases (e.g., beams with or without stirrups). This paper presents a framework to develop an explainable, data-driven model for the shear design of PC beams over a wide range of parameters. To this end, a comprehensive data set was assembled, consisting of 670 experiments of PC beams with and without stirrups. Different machine-learning (ML) techniques, including Random Forest, AdaBoost, and XGBoost, were evaluated to define a predictive model for the data set. Then, the most accurate model (based on XGBoost) was optimized to achieve high accuracy (with a coefficient of determination of 0.98 for the testing set). Moreover, the Shapley Additive exPlanations technique was used to explain and evaluate the importance of different parameters on the output of the predictive model. The predictive model was shown to be largely more accurate and more generalizable than current design equations and advanced finite-element analysis. In addition, reliability-based shear strength reduction factors were derived for the proposed ML model. These reduction factors allowed the application of the proposed ML model in the code-compliant shear design of PC beams.
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Data Availability Statement
All the data, models, and/or codes that support the findings of this study are available from the corresponding author upon reasonable request. In addition, a direct link to access the developed GUI for the shear strength of PC beams, based on the optimized XGBoost model, can be found in the Supplemental Materials in accordance with the original owner’s data retention policies.
Acknowledgments
The work presented here was financially supported by UTEC through its UTEC Seed Fund Program 2021, Grant No. 871049 (Principal Investigator: Luis A. Bedriñana). A part of the data of PC beams was kindly provided by Prof. K. H. Reineck, University of Stuttgart.
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Information & Authors
Information
Published In
Journal of Bridge Engineering
Volume 28 • Issue 4 • April 2023
Copyright
© 2023 American Society of Civil Engineers.
History
Received: Aug 18, 2022
Accepted: Nov 17, 2022
Published online: Jan 25, 2023
Published in print: Apr 1, 2023
Discussion open until: Jun 25, 2023
ASCE Technical Topics:
- Artificial intelligence and machine learning
- Beams
- Computer models
- Computer programming
- Computing in civil engineering
- Concrete
- Concrete beams
- Engineering fundamentals
- Engineering materials (by type)
- Material mechanics
- Material properties
- Materials engineering
- Materials processing
- Mathematics
- Model accuracy
- Models (by type)
- Parameters (statistics)
- Prestressed concrete
- Prestressing
- Shear strength
- Statistics
- Strength of materials
- Structural engineering
- Structural members
- Structural systems
Authors
Metrics & Citations
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