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Author: systemreliability

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@@ -19,7 +19,7 @@
   <span class="na">doi</span> <span class="p">=</span> <span class="s">{10.1016/j.ress.2023.109512}</span><span class="p">,</span>
   <span class="na">year</span> <span class="p">=</span> <span class="s">{2023}</span><span class="p">,</span>
   <span class="na">publisher</span> <span class="p">=</span> <span class="s">{Elsevier}</span><span class="p">,</span>
-<span class="p">}</span></code></pre></figure> </div> </div> </div> </li> <li> <div class="row"> <div class="col col-sm-2 abbr"> <abbr class="badge rounded w-100">RESS</abbr> <figure> <picture> <img src="/assets/img/publication_preview/Paper4.gif" class="preview z-depth-1 rounded" width="100%" height="auto" alt="Paper4.gif" data-zoomable loading="eager" onerror="this.onerror=null; $('.responsive-img-srcset').remove();"> </picture> </figure> </div> <div id="lee2025efficient" class="col-sm-8"> <div class="title">Efficient seismic reliability and fragility analysis of lifeline networks using subset simulation</div> <div class="author"> <em>Dongkyu Lee</em>, <a href="https://ce.berkeley.edu/people/faculty/ziqiwang" rel="external nofollow noopener" target="_blank">Ziqi Wang<sup>*</sup></a>, and <a href="https://systemreliability.wordpress.com/junhosong/" rel="external nofollow noopener" target="_blank">Junho Song<sup>*</sup></a> <i class="fa-solid fa-circle-info ml-1" data-toggle="popover" data-placement="top" data-html="true" data-content="*Corresponding Authors"> </i> </div> <div class="periodical"> <em>Reliability Engineering &amp; System Safety</em>, 2025 </div> <div class="periodical"> </div> <div class="links"> <a class="abstract btn btn-sm z-depth-0" role="button">Abs</a> <a href="https://doi.org/10.1016/j.ress.2025.110947" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">DOI</a> <a class="bibtex btn btn-sm z-depth-0" role="button">Bib</a> <a href="https://doi.org/10.1016/j.ress.2025.110947" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">HTML</a> </div> <div class="badges d-inline-flex align-items-center" style="gap:0.35rem;"> <span class="__dimensions_badge_embed__" data-doi="10.1016/j.ress.2025.110947" data-style="small_rectangle" style="margin-bottom: 2.5px;"></span> <a href="https://scholar.google.com/citations?view_op=view_citation&amp;hl=en&amp;user=&amp;citation_for_view=:ufrVoPGSRksC" aria-label="Google Scholar link" role="button" rel="external nofollow noopener" target="_blank"> <img src="https://img.shields.io/badge/scholar-6-4285F4?logo=googlescholar&amp;labelColor=beige" alt="6 Google Scholar citations"> </a> <a href="https://plu.mx/a/?doi=10.1016/j.ress.2025.110947" aria-label="PlumX Metrics Detail Page" class="plumx-plum-print-popup d-inline-block" data-badge="true" data-hide-when-empty="true" data-pass-hidden-categories="true" data-popup="right" data-site="plum" role="button" rel="external nofollow noopener" target="_blank"></a> </div> <div class="abstract hidden"> <p>Various simulation-based and analytical methods have been developed to evaluate the seismic fragilities of individual structures. However, the seismic safety and resilience of a community are substantially affected by network reliability, determined not only by component fragilities but also by network topology and commodity/information flows. However, seismic reliability analyses of networks often encounter significant challenges due to complex network topologies, interdependencies among ground motions, and low failure probabilities. This paper proposes to overcome these challenges by a variance-reduction method for network fragility analysis using subset simulation. The binary network limit-state function in the subset simulation is reformulated into more informative piecewise continuous functions. The proposed limit-state functions quantify the proximity of each sample to a potential network failure domain, thereby enabling the construction of specialized intermediate failure events, which can be utilized in subset simulation and other sequential Monte Carlo approaches. Moreover, by identifying an implicit relationship between intermediate failure events and seismic intensity, we propose a technique to obtain the entire network fragility curve with a single execution of specialized subset simulation. Numerical examples demonstrate that the proposed method can effectively evaluate system-level fragility for large-scale networks.</p> </div> <div class="bibtex hidden"> <figure class="highlight"><pre><code class="language-bibtex" data-lang="bibtex"><span class="nc">@article</span><span class="p">{</span><span class="nl">lee2025efficient</span><span class="p">,</span>
+<span class="p">}</span></code></pre></figure> </div> </div> </div> </li> <li> <div class="row"> <div class="col col-sm-2 abbr"> <abbr class="badge rounded w-100">RESS</abbr> <figure> <picture> <img src="/assets/img/publication_preview/Paper4.gif" class="preview z-depth-1 rounded" width="100%" height="auto" alt="Paper4.gif" data-zoomable loading="eager" onerror="this.onerror=null; $('.responsive-img-srcset').remove();"> </picture> </figure> </div> <div id="lee2025efficient" class="col-sm-8"> <div class="title">Efficient seismic reliability and fragility analysis of lifeline networks using subset simulation</div> <div class="author"> <em>Dongkyu Lee</em>, <a href="https://ce.berkeley.edu/people/faculty/ziqiwang" rel="external nofollow noopener" target="_blank">Ziqi Wang<sup>*</sup></a>, and <a href="https://systemreliability.wordpress.com/junhosong/" rel="external nofollow noopener" target="_blank">Junho Song<sup>*</sup></a> <i class="fa-solid fa-circle-info ml-1" data-toggle="popover" data-placement="top" data-html="true" data-content="*Corresponding Authors"> </i> </div> <div class="periodical"> <em>Reliability Engineering &amp; System Safety</em>, 2025 </div> <div class="periodical"> </div> <div class="links"> <a class="abstract btn btn-sm z-depth-0" role="button">Abs</a> <a href="https://doi.org/10.1016/j.ress.2025.110947" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">DOI</a> <a href="http://arxiv.org/abs/2310.10232" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">arXiv</a> <a class="bibtex btn btn-sm z-depth-0" role="button">Bib</a> <a href="https://doi.org/10.1016/j.ress.2025.110947" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">HTML</a> </div> <div class="badges d-inline-flex align-items-center" style="gap:0.35rem;"> <span class="__dimensions_badge_embed__" data-doi="10.1016/j.ress.2025.110947" data-style="small_rectangle" style="margin-bottom: 2.5px;"></span> <a href="https://scholar.google.com/citations?view_op=view_citation&amp;hl=en&amp;user=&amp;citation_for_view=:ufrVoPGSRksC" aria-label="Google Scholar link" role="button" rel="external nofollow noopener" target="_blank"> <img src="https://img.shields.io/badge/scholar-6-4285F4?logo=googlescholar&amp;labelColor=beige" alt="6 Google Scholar citations"> </a> <a href="https://plu.mx/a/?doi=10.1016/j.ress.2025.110947" aria-label="PlumX Metrics Detail Page" class="plumx-plum-print-popup d-inline-block" data-badge="true" data-hide-when-empty="true" data-pass-hidden-categories="true" data-popup="right" data-site="plum" role="button" rel="external nofollow noopener" target="_blank"></a> </div> <div class="abstract hidden"> <p>Various simulation-based and analytical methods have been developed to evaluate the seismic fragilities of individual structures. However, the seismic safety and resilience of a community are substantially affected by network reliability, determined not only by component fragilities but also by network topology and commodity/information flows. However, seismic reliability analyses of networks often encounter significant challenges due to complex network topologies, interdependencies among ground motions, and low failure probabilities. This paper proposes to overcome these challenges by a variance-reduction method for network fragility analysis using subset simulation. The binary network limit-state function in the subset simulation is reformulated into more informative piecewise continuous functions. The proposed limit-state functions quantify the proximity of each sample to a potential network failure domain, thereby enabling the construction of specialized intermediate failure events, which can be utilized in subset simulation and other sequential Monte Carlo approaches. Moreover, by identifying an implicit relationship between intermediate failure events and seismic intensity, we propose a technique to obtain the entire network fragility curve with a single execution of specialized subset simulation. Numerical examples demonstrate that the proposed method can effectively evaluate system-level fragility for large-scale networks.</p> </div> <div class="bibtex hidden"> <figure class="highlight"><pre><code class="language-bibtex" data-lang="bibtex"><span class="nc">@article</span><span class="p">{</span><span class="nl">lee2025efficient</span><span class="p">,</span>
   <span class="na">author</span> <span class="p">=</span> <span class="s">{Lee, Dongkyu and Wang, Ziqi and Song, Junho}</span><span class="p">,</span>
   <span class="na">title</span> <span class="p">=</span> <span class="s">{Efficient seismic reliability and fragility analysis of lifeline networks using subset simulation}</span><span class="p">,</span>
   <span class="na">journal</span> <span class="p">=</span> <span class="s">{Reliability Engineering \&amp; System Safety}</span><span class="p">,</span>