.png){kind=icon}
.png){kind=icon}
Return
Krill herd algorithm-based neural network in structural seismic reliability evaluation
.png){kind=icon}
PRE
AI
DOI：10.1080/15376494.2018.1430874.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
In this research work, the relative displacement of the stories has been determined by means of a feedforward Artificial Neural Network (ANN) model, which employs one of the novel methods for the optimization of the artificial neural network weights, namely the krill herd algorithm. For the purpose of this work, the area, elasticity, and load parameters were the input parameters and the relative displacement of the stories was the output parameter. To assess the precision of the feedforward (FF) model optimized using the Krill Herd Optimization (FF-KH) algorithm, comparison of results has been performed relative to the results obtained by the linear regression model, the Genetic Algorithm (GA), and the back propagation neural network model. The comparison of results has been carried out in the training and test phases. It has been revealed that the artificial neural network optimized with the krill herd algorithm supersedes the afore-mentioned models in potential, flexibility, and precision.
Keywords:
Artificial intelligence techniques
artificial krill herd algorithm
artificial neural networks
krill herd
optimization
regression models
seismic reliability assessment of structures
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
0
Papers:
4.7K
Citations:
1.4W
View more organizations