Abstract
The angle of seismic excitation is a significant factor in the seismic response of RC buildings. The procedure required for the calculation of the angle for which the potential seismic damage is maximized (critical angle) contains multiple nonlinear time history analyses, each using different angles of incidence. Moreover, the seismic codes recommend the application of more than one accelerogram for the evaluation of seismic response; thus, the whole procedure becomes time consuming. Herein, a method to reduce the time required for the estimation of the critical angle based on multilayered feedforward perceptron neural networks is proposed. The basic idea is the detection of cases in which the critical angle increases the class of seismic damage compared to the class that arises from the application of the seismic motion along the buildings’ structural axes. To this end, the problem is expressed and solved as a pattern recognition problem. The ratios of seismic parameters’ values along the two horizontal seismic records’ components, as well as appropriately chosen structural parameters, were used as the inputs of the networks. The results of analyses show that the neural networks can reliably detect the cases in which the calculation of the critical angle is essential.
Highlights
Athanatopoulou [2] introduced analytical formulae for the determination of the critical incident angle and the corresponding maximum elastic structural response of buildings subjected to three correlated seismic components; the application of these formulae to multistory structures has proven that the maximum value of a response quantity can be up to 180% larger than the response produced when the seismic accelerograms act along the structural axes
It must be noted that the procedure proposed in the present study gives engineers the ability to optimize the configuration of RC buildings at the stage of design by testing several alternative configurations, and to select the one that leads to seismic response being slightly influenced by the angle of seismic excitation
The main target of the present investigation is the proposal of a procedure based on multilayered perceptron (MLP) networks, which aims to detect in real time the cases for which the critical angle of the seismic excitation significantly alters the expected seismic damage level (SDL) of RC buildings compared to the SDL, which arises when the buildings are analyzed considering that the angle of seismic excitation is equal to zero
Summary
The angle of seismic excitation has been recognized for many years by the civil engineering research community as a significant factor in the seismic response of RC buildings. Arslan [29] investigated the effects of several structural parameters on the seismic performance of regular RC buildings using multilayered perceptron networks trained with data that were created artificially, and proved the effectiveness of ANNs in the reliable prediction of the structures’ seismic response. It must be noted that the procedure proposed in the present study gives engineers the ability to optimize the configuration of RC buildings at the stage of design by testing several alternative configurations (without the requirement for the implementation of numerus time-consuming NTHAs), and to select the one that leads to seismic response being slightly influenced by the angle of seismic excitation. The results of the present research led to the basic conclusion that the MLP networks can reliably predict the level of influence of the critical angle of seismic excitation on the seismic damage to RC buildings, in real time
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