Since early 1980s new families of computational methods, termed as soft computing (SC)
methods, have been proposed. SC methods are based on heuristic approaches rather than …

A computing environment for the seismic performance assessment of reinforced concrete
frames has been developed in Matlab in combination with OpenSees. It includes several …

Geotechnical earthquake engineering may generally be considered as an “imprecise”
scientific area due to the unavoidable uncertainties and the simplifications adopted during …

The present paper deals with the investigation of the ability of Artificial Neural Networks
(ANN) to reliably predict the r/c buildings' seismic damage state. In this investigation, the …

Contemporary methods for estimating the extent of seismic-induced damage to structures
include the use of nonlinear finite element method (FEM) and seismic vulnerability curves …

The aim of the present paper is to investigate the number and the combination of 14 seismic
parameters through which an optimum prediction for the damage state of r/c buildings can …

The present study aimed to optimize the artificial neural network (ANN) with one of the well-
established optimization algorithms called particle swarm optimization (PSO) for the problem …

The incorporation of machine learning (ML) algorithms in earthquake engineering can
improve existing methodologies and enable new frameworks to solve complex problems. In …

Accurate nonlinear dynamic analysis of reinforced concrete structures is necessary for
estimating the behavior of concrete structures during an earthquake. A realistic modeling …

Building seismic assessment is at the forefront of modern scientific research. Several
researchers have proposed methods for estimating the damage response of buildings …