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Structural Reliability Methods Applied in Analysis of Steel Elements Subjected to Fire
The accuracy and efficiency of an approach using surrogate models are investigated, in comparison with traditional structural reliability methods, in the analysis of two steel structures subjected to natural fire. For this purpose, an algorithm is used, coupled to a finite-element computational package, which includes the first-order reliability method (FORM), the approximate method most used in this type of problem, and Monte Carlo simulation (MCS), which is usually taken as a reference. On the other hand, the literature indicates surrogate models as a promising alternative to maintain, at acceptable levels, the computational cost in similar analyses. Therefore, two approaches using surrogate models are also applied to the algorithm, more specifically adaptive and nonadaptive artificial neural networks. The analyses led to differences of up to 21.83% between the failure probabilities obtained via FORM and via MCS. Furthermore, while the nonadaptive approach fails to achieve sufficient accuracy, the adaptive approach is confirmed to be a viable alternative, with differences, on average, close to 1.00% and computational times less than 1.60% of the time required by MCS.
Structural Reliability Methods Applied in Analysis of Steel Elements Subjected to Fire
The accuracy and efficiency of an approach using surrogate models are investigated, in comparison with traditional structural reliability methods, in the analysis of two steel structures subjected to natural fire. For this purpose, an algorithm is used, coupled to a finite-element computational package, which includes the first-order reliability method (FORM), the approximate method most used in this type of problem, and Monte Carlo simulation (MCS), which is usually taken as a reference. On the other hand, the literature indicates surrogate models as a promising alternative to maintain, at acceptable levels, the computational cost in similar analyses. Therefore, two approaches using surrogate models are also applied to the algorithm, more specifically adaptive and nonadaptive artificial neural networks. The analyses led to differences of up to 21.83% between the failure probabilities obtained via FORM and via MCS. Furthermore, while the nonadaptive approach fails to achieve sufficient accuracy, the adaptive approach is confirmed to be a viable alternative, with differences, on average, close to 1.00% and computational times less than 1.60% of the time required by MCS.
Structural Reliability Methods Applied in Analysis of Steel Elements Subjected to Fire
Ricardo, Alverlando Silva (author) / de Santana Gomes, Wellison José (author)
2021-09-27
Article (Journal)
Electronic Resource
Unknown
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