Seismic fragility curves fitting revisited: ordinal regression models and their generalization
arxiv(2024)
摘要
This research conducts a thorough reevaluation of seismic fragility curves by
utilizing ordinal regression models, moving away from the commonly used
log-normal distribution function known for its simplicity. It explores the
nuanced differences and interrelations among various ordinal regression
approaches, including Cumulative, Sequential, and Adjacent Category models,
alongside their enhanced versions that incorporate category-specific effects
and variance heterogeneity. The study applies these methodologies to empirical
bridge damage data from the 2008 Wenchuan earthquake, using both frequentist
and Bayesian inference methods, and conducts model diagnostics using surrogate
residuals. The analysis covers eleven models, from basic to those with
heteroscedastic extensions and category-specific effects. Through rigorous
leave-one-out cross-validation, the Sequential model with category-specific
effects emerges as the most effective. The findings underscore a notable
divergence in damage probability predictions between this model and
conventional Cumulative probit models, advocating for a substantial transition
towards more adaptable fragility curve modeling techniques that enhance the
precision of seismic risk assessments. In conclusion, this research not only
readdresses the challenge of fitting seismic fragility curves but also advances
methodological standards and expands the scope of seismic fragility analysis.
It advocates for ongoing innovation and critical reevaluation of conventional
methods to advance the predictive accuracy and applicability of seismic
fragility models within the performance-based earthquake engineering domain.
更多查看译文
AI 理解论文
溯源树
样例
![](https://originalfileserver.aminer.cn/sys/aminer/pubs/mrt_preview.jpeg)
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要