This paper presents novel methodologies that combine analytical mechanics with artificial intelligence to classify the failure mode of reinforced concrete walls under lateral loading. The adequacy of existing approaches is examined, including design equations and established machine learning algorithms, against experimental datasets consisting of 330 specimens. Findings indicate that the machine learning algorithms outperform the empirical design equations in terms of failure mode classification; however, the former lacks physical interpretability. To address this limitation, a new concept is proposed by integrating analytical and computational platforms for identifying attributes that dominate the failure characteristics of the walls. Non-dimensional features are derived and substituted into the machine learning algorithms. The aspect ratio of the walls and the amount of vertical rebars in boundary elements are found to be salient factors when categorizing failure modes, followed by the amount of horizontal rebars in the web. Through the application of decision boundaries stemming from the mechanics-based machine learning protocol, design recommendations are suggested to effectively classify the failure mode of load-bearing walls.
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