Efficient training of two ANNs using four meta-heuristic algorithms for predicting the FRP strength. (June 2023)

• Record Type: Journal Article
• Authors: Kaveh, Ali; Khavaninzadeh, Neda
• Is Part Of: Structures. Volume 52(2023)
• Page Start: 256
• Page End: 272
• Publication Date: 2023-06
• Subjects: Artificial neural network -- Metaheuristic algorithm -- Feed forward backpropagation -- Radial basis -- Predicting FRP strength; Structural engineering -- Periodicals; 624.1
• DOI: 10.1016/j.istruc.2023.03.178
• Languages: English
• ISSNs: 2352-0124

Abstract

Abstract: In recent years, artificial neural network (ANN) is one of the popular and effective machine learning models that can be used to accurately predict fiber reinforced polymer (FRP) strength. However, the ANN structure and parameters are usually are chosen by experience. In this study, the aim is to use the combination of meta-heuristic algorithm with two different types of artificial neural network structure to optimize the parameters of the feed forward backpropagation and radial basis function networks. In this paper, Particle swarm optimization (PSO), Genetic algorithm (GA), Colliding bodies optimization (CBO), Enhanced colliding bodies optimization (ECBO) algorithms are used to combine with ANNs. A total of 223 test data on Carbon FRP (CFRP) collected from the available literature were used to generate training and test data sets. Various validation criteria such as mean square error, root mean square error and correlation coefficient (R) are used to validate the models. These models consider the effects of concrete compressive strength, concrete sample diameter, concrete sample length, fiber elastic modulus, fiber thickness, fiber strength on the ultimate strength of FRP-concrete. It is shown that ECBO algorithm provide better results and higher accuracy. Also, the comparison of the results of using two neural networks, feed forward backpropagation (FFB) and radial basis function (RBF), indicates that the lower error percentage and more accurate results are related to the FFB neural networks combined with ECBO.