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Estimate Deformation Capacity of Non-Ductile RC Shear Walls using Explainable Boosting Machine

Machine learning is becoming increasingly prevalent for tackling challenges in earthquake engineering and providing fairly reliable and accurate predictions. However, it is mostly unclear how decisions are made because machine learning models are generally highly sophisticated, resulting in opaque black-box models. Machine learning models that are naturally interpretable and provide their own decision explanation, rather than using an explanatory, are more accurate in determining what the model actually computes. With this motivation, this study aims to develop a fully explainable machine learning model to predict the deformation capacity of non-ductile reinforced concrete shear walls based on experimental data collected worldwide. The proposed Explainable Boosting Machines (EBM)-based model is an interpretable, robust, naturally explainable glass-box model, yet provides high accuracy comparable to its black-box counterparts. The model enables the user to observe the relationship between the wall properties and the deformation capacity by quantifying the individual contribution of each wall property as well as the correlations among them. The mean coefficient of determination R2 and the mean ratio of predicted to actual value based on the test dataset are 0.92 and 1.05, respectively. The proposed predictive model stands out with its overall consistency with scientific knowledge, practicality, and interpretability without sacrificing high accuracy.

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