A neural network surrogate model for the performance assessment of a vertical structure subjected to non-stationary, tornadic wind loads. (15th April 2020) - Le, Viet; Caracoglia, Luca
Highlights: The structural effects of multiple formulations of tornadic wind loads are examined. A framework to assess performance capacity of a vertical structure is proposed. Artificial neural networks are used to approximate structural fragilities. Probability of limit state failure under tornadic loading is evaluated. Modeling uncertainty affecting life-cycle cost assessment is quantified. Abstract: Despite significant advancements in computational technologies and methods, the comprehensive assessment of the performance capacities and risk of structures built in environments prone to severe natural hazards is still a daunting task under standard Monte Carlo-based simulation schemes. This issue is particularly relevant for the consideration of wind actions from loads generated by non-stationary phenomena (e.g. tornadoes) because of extreme complexities in the simulated flow field and the fluid-structure interaction. To mitigate such computational burdens, this study proposes a surrogate modeling approach that utilizes predicted fragilities from artificial neural networks (ANNs) to facilitate the performance-based assessment of a vertical structure subjected to tornadic wind loads. Calibration data for the feedforward ANNs are extracted from numerically generated responses based on a derived wind loading model that capitalizes on the developments of various analytical formulations of a tornado's wind field. Uncertainties in the structural behavior and in the overall modeling procedure are incorporated in the process, culminating in a life-cycle cost assessment that incorporates a practical, economic value to the simulation framework. The novel application of ANNs in this study, therefore, empowers a more robust performance-based framework for the risk evaluation of structures subjected to tornado wind loads.