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RESEARCH PRODUCT

Krill herd algorithm-based neural network in structural seismic reliability evaluation

Mehdi NikooPanagiotis G. AsterisLiborio CavaleriSaeed NozhatiMohammad Reza Nikoo

subject

Computer scienceGeneral Mathematics02 engineering and technologyBack propagation neural networkkrill herdLinear regression0202 electrical engineering electronic engineering information engineeringMathematics (all)Mechanics of MaterialGeneral Materials Scienceartificial krill herd algorithmCivil and Structural Engineeringregression modelArtificial neural networkMechanical EngineeringFeed forwardseismic reliability assessment of structureKrill herd algorithmRegression analysisArtificial intelligence techniqueKrill herd021001 nanoscience & nanotechnologySettore ICAR/09 - Tecnica Delle CostruzioniMechanics of Materials020201 artificial intelligence & image processingMaterials Science (all)0210 nano-technologyoptimizationRelative displacementAlgorithmartificial neural network

description

ABSTRACTIn 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.

yearjournalcountryeditionlanguage
2018-02-02Mechanics of Advanced Materials and Structures
https://doi.org/10.1080/15376494.2018.1430874