https://www.scipedia.com/wd/index.php?action=history&feed=atom&title=Murilo_Favilla_1998aMurilo Favilla 1998a - Revision history2026-07-15T08:29:34ZRevision history for this page on the wikiMediaWiki 1.27.0-wmf.10https://www.scipedia.com/wd/index.php?title=Murilo_Favilla_1998a&diff=57575&oldid=prevScipediacontent: Scipediacontent moved page Draft Content 766870405 to Murilo Favilla 1998a2017-06-16T08:53:31Z<p>Scipediacontent moved page <a href="/public/Draft_Content_766870405" class="mw-redirect" title="Draft Content 766870405">Draft Content 766870405</a> to <a href="/public/Murilo_Favilla_1998a" title="Murilo Favilla 1998a">Murilo Favilla 1998a</a></p>
<table class="diff diff-contentalign-left" data-mw="interface">
<tr style='vertical-align: top;' lang='en'>
<td colspan='1' style="background-color: white; color:black; text-align: center;">← Older revision</td>
<td colspan='1' style="background-color: white; color:black; text-align: center;">Revision as of 08:53, 16 June 2017</td>
</tr><tr><td colspan='2' style='text-align: center;' lang='en'><div class="mw-diff-empty">(No difference)</div>
</td></tr></table>Scipediacontenthttps://www.scipedia.com/wd/index.php?title=Murilo_Favilla_1998a&diff=57520&oldid=prevScipediacontent: Created page with "== Abstract == This text presents a study of the use of neural networks for solving the classic structural reliability problem to obtain the probability of failure of collaps..."2017-06-16T08:44:50Z<p>Created page with "== Abstract == This text presents a study of the use of neural networks for solving the classic structural reliability problem to obtain the probability of failure of collaps..."</p>
<p><b>New page</b></p><div>== Abstract ==<br />
<br />
This text presents a study of the use of neural networks for solving the classic structural reliability problem to obtain the probability of failure of collapse of a certain structure. This study is of a particular interest when we analyse structures which prevailing loads are random variables, as it is the case of the Offshore Platforms that are exposed to wind, waves, streams, etc. When a considerable number of random variables occurs, or the limit state functions are too complex, the traditional analytic methods such as FORM and SORM sometimes are not as efficient as we could expect. In these cases, the simulation based methods like the Monte Carlo Technique can become the recomended tool. The examples presented in this text show how neural networks can considerably reduce the number of simulations needed when following the Monte Carlo Technique.<br />
<br />
== Full document ==<br />
<pdf>Media:draft_Content_766870405RR142C.pdf</pdf></div>Scipediacontent