Use of Artificial Neural Networks and Response Surface Methodology for Evaluating the Reliability Index of Steel Wind Towers

The estimation of structural reliability is a process that requires a large number of computational hours when statistical data are not available since it is necessary to perform a large amount of analysis or numerical simulations to estimate parameters related to the reliability. A methodology is p...

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Bibliographic Details
Published in:Advances in civil engineering 2022, Vol.2022 (1)
Main Authors: Inzunza-Aragón, Indira, Ruiz, Sonia E., Cruz-Reyes, Laura
Format: Article
Language:English
Subjects:
Algorithms
Artificial neural networks
Civil engineering
Design
Failure
Methods
Network reliability
Neural networks
Optimization
Parameter estimation
Reliability analysis
Reliability aspects
Reliability engineering
Response surface methodology
Simulation
Steel structures
Structural reliability
Turbines
Wind power
Wind turbines
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Summary:The estimation of structural reliability is a process that requires a large number of computational hours when statistical data are not available since it is necessary to perform a large amount of analysis or numerical simulations to estimate parameters related to the reliability. A methodology is proposed for estimating the structural reliability index, as well as the demand and structural capacity factors inherent to the structure, given the fundamental vibration period and the height of the structure, by using artificial neural networks (ANN) and, alternatively, the response surface method (RSM). Both approaches are applied to steel wind turbine towers. For the cases studied, ANN allow evaluating the reliability index and both the demand and structural capacity factors with greater accuracy than when using RSM.
ISSN:1687-8086
1687-8094
DOI:10.1155/2022/4219524