<ul><li>Accurate prediction of structural failure modes under seismic excitations is crucial for assessing seismic risk and resilience.</li><li>A study proposes a framework to construct balanced datasets that include distinct failure modes.</li><li>The framework consists of three steps, including identifying critical ground motion features, estimating probability densities of failure domains, and generating samples transformed into ground motion time histories.</li><li>Numerical investigations using different structural models show that the framework effectively addresses dataset imbalance and improves machine learning performance in seismic failure mode prediction.</li></ul>

Constructing balanced datasets for predicting failure modes in structural systems under seismic hazards

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