A hybrid methodology using finite elements and neural networks for the analysis of adhesive anchors exposed to hurricanes and adverse environments. (1st June 2020) - Aragão Almeida, Sálvio; Guner, Serhan
Highlights: A novel methodology to permit the use of 2D models in the analysis of adhesive anchors. Prediction accuracies comparable to 3D models but at a fraction of the computational cost. The Equivalent Cone Method models 3D concrete breakout failure using 2D models. The artificial neural network rapidly accounts for long-term concrete cracking. Analytical equations account for beam bending and elevated temperatures. Abstract: Hurricanes are responsible for approximately $28bn of damage every year in the United States alone, which may reach $151bn by 2075 due to the intensification of climate change according to certain prediction models. Approximately 35% of this damage is estimated to come from anchorage failures of non-structural components (NSCs). Severe exposure of NSCs to the adverse environments (such as elevated temperatures and long-term concrete cracking) and wind-induced bending effects during hurricanes promote anchorage failures. Three-dimensional (3D) nonlinear finite element (NLFE) analysis methods are currently required for simulating the anchor behavior due to the 3D phenomena involved; however, these models are rather complex and computationally prohibitive for analyzing large systems commonly encountered in practice. This study proposes a 2D analysis methodology that combines the strengths of 3D numerical modeling with the artificial neural network techniques to rapidly simulate the anchorage behavior while accounting for the effects of the adverse environmental exposure, concrete cone failure, and wind-induced bending effects. The methodology, which is validated with experimental data and 3D NLFE analyses, employs three distinct techniques as follows: ( i) a novel modeling approach, 'the Equivalent Cone Method, ' to accurately simulate the concrete cone breakout failure, ( ii) analytical equations developed to account for wind-induced beam bending and elevated temperatures, and ( iii) a multilayered feed-forward artificial neural network, trained and tested with the experimental data from a worldwide database, to rapidly account for long-term concrete cracking experienced by rooftop slabs. By employing these techniques, the proposed methodology permits the use of 2D NLFE models for anchor analysis with accuracies comparable to advanced 3D NLFE models but at a fraction of the computational cost.