<p>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.</p>
<p>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%. </p>