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

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Proceedings of the 21st EANN (Engineering Applications of Neural Networks) 2020 Conference (EANN 2020)

Part of the book series: Proceedings of the International Neural Networks Society ((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.

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Authors and Affiliations

  1. Department of Civil Engineering, Institute of Structural Statics and Dynamics, Democritus University of Thrace, 67100, Xanthi, Greece

    Magdalini Tyrtaiou, Antonios Papaleonidas, Anaxagoras Elenas & Lazaros Iliadis

Authors
  1. Magdalini Tyrtaiou
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  2. Antonios Papaleonidas
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  3. Anaxagoras Elenas
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  4. Lazaros Iliadis
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Correspondence to Magdalini Tyrtaiou .

Editor information

Editors and Affiliations

  1. School of Engineering, Department of Civil Engineering, Democritus University of Thrace, Xanthi, Greece

    Lazaros Iliadis

  2. Lancaster University, Lancaster, UK

    Plamen Parvanov Angelov

  3. School of Computing and Digital Technologies, Teesside University, Middlesbrough, UK

    Chrisina Jayne

  4. University of the West of England, Bristol, UK

    Elias Pimenidis

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Tyrtaiou, M., Papaleonidas, A., Elenas, A., Iliadis, L. (2020). Accomplished Reliability Level for Seismic Structural Damage Prediction Using Artificial Neural Networks. In: Iliadis, L., Angelov, P., Jayne, C., Pimenidis, E. (eds) Proceedings of the 21st EANN (Engineering Applications of Neural Networks) 2020 Conference. EANN 2020. Proceedings of the International Neural Networks Society, vol 2. Springer, Cham. https://doi.org/10.1007/978-3-030-48791-1_6

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  • DOI: https://doi.org/10.1007/978-3-030-48791-1_6

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  • Online ISBN: 978-3-030-48791-1

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