Bayesian linear regression (BLR) based demand prediction models are proposed for efficient seismic fragility analysis (SFA) of structures utilizing limited numbers of nonlinear time history analyses results. In doing so, two different BLR models i.e. one based on the classical Bayesian least squares regression and another based on the sparse Bayesian learning using Relevance Vector Machine are explored. The proposed models integrate both the record-to-record variation of seismic motions and uncertainties due to structural model parameters. The magnitude of uncertainty involved in the fragility estimate is represented by providing a confidence bound of the fragility curve. The effectiveness of the proposed BLR models are compared with the commonly used cloud method and the maximum likelihood estimates methods of SFA by considering a nonlinear single-degree-of-freedom system and a five-storey reinforced concrete building frame. It is observed that both the BLR models can estimate fragility with improved accuracy compared to those common analytical SFA approaches considering direct Monte Carlo simulation based fragility results as the benchmark.
机构:
Univ Calif Los Angeles, Dept Civil & Environm Engn, Los Angeles, CA USAAndong Natl Univ, Dept Civil Engn, Andong Si 36729, Gyeongsangbuk D, South Korea
Mangalathu, Sujith
;
Song, Junho
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Seoul Natl Univ, Dept Civil & Environm Engn, Seoul, South KoreaAndong Natl Univ, Dept Civil Engn, Andong Si 36729, Gyeongsangbuk D, South Korea
Song, Junho
;
Desroches, Reginald
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Rice Univ, Dept Civil & Environm Engn, Houston, TX 77005 USAAndong Natl Univ, Dept Civil Engn, Andong Si 36729, Gyeongsangbuk D, South Korea
机构:
Univ Calif Los Angeles, Dept Civil & Environm Engn, Los Angeles, CA USAAndong Natl Univ, Dept Civil Engn, Andong Si 36729, Gyeongsangbuk D, South Korea
Mangalathu, Sujith
;
Song, Junho
论文数: 0引用数: 0
h-index: 0
机构:
Seoul Natl Univ, Dept Civil & Environm Engn, Seoul, South KoreaAndong Natl Univ, Dept Civil Engn, Andong Si 36729, Gyeongsangbuk D, South Korea
Song, Junho
;
Desroches, Reginald
论文数: 0引用数: 0
h-index: 0
机构:
Rice Univ, Dept Civil & Environm Engn, Houston, TX 77005 USAAndong Natl Univ, Dept Civil Engn, Andong Si 36729, Gyeongsangbuk D, South Korea