Predicting the compressive strength of normal and High-Performance Concretes using ANN and ANFIS hybridized with Grey Wolf Optimizer. (30th January 2020)

• Record Type: Journal Article
• Authors: Golafshani, Emadaldin Mohammadi; Behnood, Ali; Arashpour, Mehrdad
• Is Part Of: Construction & building materials. Volume 232(2020)
• Publication Date: 2020-01-30
• Subjects: High-Performance Concrete -- Compressive strength -- Blast furnace slag -- Fly ash -- Artificial Neural Network -- Adaptive Network-Based Fuzzy Inference System -- Grey Wolf Optimizer; Building materials -- Periodicals; 624.18
• DOI: 10.1016/j.conbuildmat.2019.117266
• Languages: English
• ISSNs: 0950-0618

Abstract

Highlights: A reliable model for the prediction of the compressive strength of concretes was proposed. Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) techniques were used. ANN and ANFIS were hybridized with Grey Wolf Optimizer (GWO). Hybridization of both ANN and ANFIS improved the performance of the models. Abstract: Achieving a reliable model for predicting the compressive strength (CS) of concrete can save in time, energy, and cost and also provide information about scheduling for construction and framework removal. In this study, Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) techniques were hybridized by Grey Wolf Optimizer (GWO) to develop the predictive models for predicting the CS of Normal Concrete (NC) and High-Performance Concrete (HPC). The classical optimization algorithms (COAs) served in training of ANN and ANFIS have a high capability in the exploitation phase. In this study, GWO was used in the training phase of ANN and ANFIS to eliminate this weakness. In this regard, a comprehensive dataset containing 2817 distinctive data records was collected to develop six ANN and three ANFIS models. In case of ANN models, three models were developed using three different COAs and the others were constructed using hybridization of these COAs and GWO. With regard to ANFIS models, one model was developed using the original version of ANFIS and two models were hybridized with GWO. The results indicate that the hybridization of the models with GWO improves the training and generalization capability of both ANN and ANFIS models. It is also deduced that ANN models trained with Levenberg-Marquardt algorithm outperformed other ANN-based models as well as all ANFIS-based models.