Estimate Deformation Capacity of Non-Ductile RC Shear Walls using Explainable Boosting Machine. (arXiv:2301.04652v1 [cs.LG])

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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