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Accomplished Reliability Level for Seismic Structural Damage Prediction Using Artificial Neural Networks

  • Magdalini TyrtaiouEmail author
  • Antonios Papaleonidas
  • Anaxagoras Elenas
  • Lazaros Iliadis
Conference paper
  • 193 Downloads
Part of the Proceedings of the International Neural Networks Society book series (INNS, volume 2)

Abstract

This research aims to determine the optimal Multi-Layer Feed-Forward Artificial Neural Network (MLFF) capable of accurately estimating the level of seismic damage on buildings, by considering a set of Seismic Intensity Parameters (SIP). Twenty SIP (well established and highly correlated to the structural damage) were utilized. Their corresponding values were calculated for a set of seismic signals. Various combinations of at least five seismic features were performed for the development of the input dataset. A vast number of Artificial Neural Networks (ANNs) were developed and tested. Their output was the level of earthquake Damage on a Reinforced Concrete Frame construction (DRCF) as it is expressed by the Park and Ang overall damage index. The potential contribution of nine distinct Machine Learning functions towards the development of the most robust ANN was also investigated. The results confirm that MLFF networks can provide an accurate estimation of the structural damage caused by an earthquake excitation. Hence, they can be considered as a reliable Computational Intelligence approach for the determination of structures’ seismic vulnerability.

Keywords

Seismic intensity parameters Multiple feedforward perceptron artificial neural network ANN Park and Ang damage index 

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Copyright information

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of Civil Engineering, Institute of Structural Statics and DynamicsDemocritus University of ThraceXanthiGreece

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