<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Bolted Flange Joints |</title><link>https://cnardin.github.io/tags/bolted-flange-joints/</link><atom:link href="https://cnardin.github.io/tags/bolted-flange-joints/index.xml" rel="self" type="application/rss+xml"/><description>Bolted Flange Joints</description><generator>HugoBlox Kit (https://hugoblox.com)</generator><language>en-us</language><lastBuildDate>Sun, 28 Jul 2024 00:00:00 +0000</lastBuildDate><image><url>https://cnardin.github.io/media/icon_hu_2d2b1e39e19355d7.png</url><title>Bolted Flange Joints</title><link>https://cnardin.github.io/tags/bolted-flange-joints/</link></image><item><title>State-dependent seismic fragility functions for bolted flange joints on special-risk industrial substructures</title><link>https://cnardin.github.io/publication/conferences/2024-pvp-bfj/</link><pubDate>Sun, 28 Jul 2024 00:00:00 +0000</pubDate><guid>https://cnardin.github.io/publication/conferences/2024-pvp-bfj/</guid><description>
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&lt;div class="callout-title font-semibold mb-1"&gt;Abstract&lt;/div&gt;
&lt;div class="callout-body"&gt;&lt;p&gt;Seismic vulnerability assessment of industrial plants and process equipment has gained attention lately. However, the complexity of the problem and its modelling, combined with a general scarcity of available data on industrial systems, contributed to limiting or developing risk assessment methods based on extremely simplified models. On these premises, a new methodology that combines limited data from finite element (FE) models with cutting-edge machine learning techniques is developed to generate state-dependent fragility functions for critical components mounted on archetypical industrial substructure modules. Specifically, referring to the classical forward uncertainty quantification scheme, a physics-informed FE model, calibrated on the results of a comprehensive shake table test campaign of a real-scale industrial braced-frame (BF) steel substructure, is considered. Time histories of seismic event sequences generated by a site-based ground motion model compose the input provided to the FE model. The uncertainty is then propagated through Monte Carlo analysis on inexpensive-to-run polynomial chaos expansion (PCE) metamodels set for each starting damage level condition measure. As a result, empirical state-dependent fragilities are evaluated, assuming the lognormal distribution.&lt;/p&gt;&lt;/div&gt;
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