LAUSR

ABSTRACT Decades-long experimental databases become accessible in global earthquake engineering community. Yet, complex interactions of a multitude of variables pose formidable challenges to data-driven research. We embarked upon developing an advanced statistical learning and prediction framework with the generalized additive model (GAM). We showed promising performance of GAM with applications to existing RC shear wall databases. Without any prejudice, GAM can predict structural responses accurately using raw databases, and also can identify salient attributes. This study addresses computational implementation and parallel processing, and all codes are made publicly available to promote data-driven research of earthquake engineering community.