Predicting the compressive strength of normal and High-Performance Concretes using ANN and ANFIS hybridized with Grey Wolf Optimizer

By Emadaldin Mohammadi Golafshani, Ali Behnood, M. Arashpour. 2020

441 citations recorded by the source corpus.

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.

Subfields: Civil and Structural Engineering, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/f2923f1c66af5d562adae50993099d3f26072588

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Civil and Structural Engineering

1. Trung Nguyen-Thoi
2. Masoud Ahmadi
3. A. Shariati
4. Mahdi Shariati
5. Hung Nguyen-Xuan

In Electrical and Electronic Engineering

1. Trung Nguyen-Thoi
2. Hung Nguyen-Xuan
3. Guirong Liu
4. Timon Rabczuk
5. Stéphane P.A. Bordas
6. Guiyong Zhang
7. N. Nguyen‐Thanh

Citations in the corpus

• A novel hybrid extreme learning machine–grey wolf optimizer (ELM-GWO) model to predict compressive strength of concrete with partial replacements for cement (2020, methodology): “[26] combined an ANN and an ANFIS model with grey wolf optimizer (GWO) and showed that hybridization of the models with GWO improves the training and generalization capability of both ANN and ANFIS models.”