An Advanced Statistical Approach to Data-Driven Earthquake Engineering. Issue 8 (2nd August 2020)

• Record Type: Journal Article
• Authors: Song, Ikkyun; Cho, In Ho; Wong, Raymond K. W.
• Is Part Of: Journal of earthquake engineering. Volume 24:Issue 8(2020)
• Page Start: 1245
• Page End: 1269
• Publication Date: 2020-08-02
• Subjects: Statistical Learning and Prediction -- Data-driven Earthquake Engineering -- Generalized Additive Model -- Reinforced Concrete Shear Wall; Earthquake engineering -- Periodicals; 624.1762
• DOI: 10.1080/13632469.2018.1461713
• Languages: English
• ISSNs: 1363-2469

Abstract

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.