A review on the artificial neural network approach to analysis and prediction of seismic damage in infrastructure

EasyChair Preprint 2754

Abstract

Machine learning has been the focus of attention in recent decades, and the influence of the artificial neural networks (ANN) is notable as the most extensively used models of machine learning in the assessment of infrastructures. This paper presents the state of the art of analysis and prediction of seismic damage in infrastructure. The survey demonstrates that ANNs are the essential tools for predicting damage detection of seismic performances of RC bridges. It was also shown that efficiency stresses of the reinforcements are one of the important sources of uncertainty in fragility analysis of RC bridges. It is evident from this evaluation that ANNs have been successfully applied to many infrastructure engineering areas like prediction, risk analysis, decision-making, resources optimisation, classification, and selection.

Keyphrases: ANN, Artificial Neural Network, FEM, RC Bridge, Seismic Evaluation, damage detection, deep learning, dynamic analysis, finite element model

@booklet{EasyChair:2754,
  author    = {Manouchehr Shokri and Kian Tavakoli},
  title     = {A review on the artificial neural network approach to analysis and prediction of seismic damage in infrastructure},
  howpublished = {EasyChair Preprint 2754},
  year      = {EasyChair, 2020}}