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Application of mean-covariance regression methods for estimation of edp|im distributions for small record sets

Ghods, B ; Sharif University of Technology | 2022

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  1. Type of Document: Article
  2. DOI: 10.1080/13632469.2021.1961938
  3. Publisher: Taylor and Francis Ltd , 2022
  4. Abstract:
  5. The performance of several regression methods is investigated to estimate the distribution of engineering demand parameters conditioned on intensity measures (EDP|IM) for small record sets. In particular, the performance of the multivariate ordinary least squares (OLS), a simultaneous mean-variance regression (MVR) done by a penalized weighted least-square loss function, and a mean-covariance/variance regression based on expectation maximization method (EM) are assessed. The efficiency of the introduced methods is compared with FEMA-P58 methodology. Performance assessment of EM and MVR methods shows that the overall increase in efficiency is about 25–45% for maximum inter-story drift ratios, and 30–50% for maximum absolute floor acceleration. © 2021 Taylor & Francis Group, LLC
  6. Keywords:
  7. Distribution of engineering demand parameters conditioned on intensity measure ; Expectation maximization algorithm ; Performance based earthquake engineering ; Earthquake engineering ; Image segmentation ; Least squares approximations ; Maximum principle ; Regression analysis ; Distribution of engineering demand parameter conditioned on intensity measure ; Engineering demand parameters ; Expectations maximization algorithms ; FEMA-p58 methodology ; Intensity measure ; Mean-covariance regression ; Performance ; Performance-based earthquake engineering ; Regression ; Regression method ; Efficiency
  8. Source: Journal of Earthquake Engineering ; Volume 26, Issue 14 , 2022 , Pages 7276-7296 ; 13632469 (ISSN)
  9. URL: https://www.tandfonline.com/doi/abs/10.1080/13632469.2021.1961938?journalCode=ueqe20