Interpretable XGBoost-SHAP Machine-Learning Model for Shear Strength Prediction of Squat RC Walls
Publication: Journal of Structural Engineering
Volume 147, Issue 11
Abstract
RC shear walls are commonly used as lateral load-resisting elements in seismic regions, and the estimation of their shear strengths can become simultaneously design-critical and complex when they have so-called squat geometries, i.e., height-to-length ratios less than two. This paper presents a study on the training and interpretation of an advanced machine-learning model that strategically combines two algorithms for the said purpose. To train the model, a comprehensive shear strength database of 434 samples of squat RC walls is utilized. First, the eXtreme Gradient Boosting (XGBoost) algorithm is used to establish a predictive model for estimating the shear strength, wherein 70% and 30% of the data are respectively used for training and validation. This effort resulted in an approximately 97% validation accuracy, which well exceeds current mechanics-based/semiempirical models. Second, the SHapley Additive exPlanations (SHAP) algorithm is used to estimate the relative importance of the factors affecting XGBoost’s shear strength estimates. This step thus enabled physical and quantitative interpretations of the input-output dependencies, which are nominally hidden in conventional machine-learning approaches. Through this setup, several squat wall attributes are identified as being critical in shear strength estimates.
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Data Availability Statement
The data, model, and source codes generated or used during the study will be available in an online repository in accordance with the original owner’s data retention policies (https://github.com/dcfeng-87/Interpretable-ML-shear-squat-wall).
Acknowledgments
The authors greatly appreciate the financial support of the National Natural Science Foundation of China (Grant No. 52078119) that enabled the first author to spend a term as a Visiting Scholar at University of California, Los Angeles.
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Journal of Structural Engineering
Volume 147 • Issue 11 • November 2021
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© 2021 American Society of Civil Engineers.
History
Received: Jul 12, 2020
Accepted: Apr 28, 2021
Published online: Aug 26, 2021
Published in print: Nov 1, 2021
Discussion open until: Jan 26, 2022
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