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

Abstract

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.

A DOE (Response Surface Methodology) Approach to Predict the Strength Properties of Concrete Incorporated with Jute and Bamboo Fibres and Silica Fumes

“…Response surface methodology is an active tool for providing a suitable empirical model for predicting the optimum performance of an asphaltic mixture to decrease flexible pavement failure [23]. Response surface models developed to evaluate the reliability index of steel towers have the desired accuracy in comparison with that installed inland [24]. Compressive strength is predicted using response surface methodology (RSM) and the artificial neural networks (ANNs) with three variable processes modelling can be used, and both approaches are an effective tool in the prediction of the strength of concrete under compression [25].…”