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The recent surge in earthquake engineering is the use of machine learningmethods to develop predictive models for structural behavior. Complex black-boxmodels are typically used for decision making to achieve high accuracy;however, as important as high accuracy, it is essential for engineers tounderstand how the model makes the decision and verify that the model isphysically meaningful. With this motivation, this study proposes a glass-box(interpretable) classification model to predict the seismic failure mode ofconventional reinforced concrete shear (structural) walls. Reportedexperimental damage information of 176 conventional shear walls tested underreverse cyclic loading were designated as class-types, whereas key designproperties (e.g. compressive strength of concrete, axial load ratio, and webreinforcement ratio) of shear walls were used as the basic classificationfeatures. The trade-off between model complexity and model interpretability wasdiscussed using eight Machine Learning (ML) methods. The results showed thatthe Decision Tree method was a more convenient classifier with higherinterpretability with a high classification accuracy than its counterparts.Also, to enhance the practicality of the model, a feature reduction wasconducted to reduce the complexity of the proposed classifier with higherclassification performance, and the most relevant features were identified,namely: compressive strength of concrete, wall aspect ratio, transverseboundary, and web reinforcement ratio. The ability of the final DT model topredict the failure modes was validated with a classification rate of around90%. The proposed model aims to provide engineers interpretable, robust, andrapid prediction in seismic performance assessment.

Glass-box model representation of seismic failure mode prediction for conventional RC shear walls