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Analytical Prediction of Steel Grid-Shell Stability and Dynamic Behaviors Using Neural Networks - Part 1

Abstract : Artificial Intelligence is a cutting-edge technology expanding very quickly into every industry. It has made its way into structural engineering and it has shown its benefits in predicting structural performance as well as saving modelling and experimenting time. This paper is the first one (out of three) of a broader research where artificial intelligence was applied to the stability and dynamic analyzes of steel grid-shells. In that study, three Artificial Neural Networks (ANN) with 8 inputs were independently designed for the prediction of a single target variable, namely: (i) the critical buckling factor for uniform loading (i.e. over the entire roof), (ii) the critical buckling factor for uniform loading over half of the roof, and (iii) the fundamental frequency of the structure. This paper addresses target variable (i). The ANN simulations were based on 1098-point datasets obtained via thorough finite element analyzes. The proposed ANN for the prediction of the critical buckling factor in steel grid-shells under uniform loading yields mean and maximum errors of 1.1% and 16.3%, respectively, for all 1098 data points. Only in 10.6% of those examples (points), the prediction error exceeds 3%.
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https://hal.archives-ouvertes.fr/hal-02903288
Contributor : Miguel Abambres Connect in order to contact the contributor
Submitted on : Monday, July 20, 2020 - 8:15:58 PM
Last modification on : Wednesday, December 15, 2021 - 10:29:26 AM

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Distributed under a Creative Commons Attribution 4.0 International License

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  • HAL Id : hal-02903288, version 1

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Miguel Abambres, Adrián Cabello. Analytical Prediction of Steel Grid-Shell Stability and Dynamic Behaviors Using Neural Networks - Part 1. 2020. ⟨hal-02903288⟩

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