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.
| Original language | English |
|---|---|
| Article number | 117266 |
| Number of pages | 14 |
| Journal | Construction and Building Materials |
| Volume | 232 |
| DOIs | |
| Publication status | Published - 30 Jan 2020 |
Keywords
- Adaptive Network-Based Fuzzy Inference System
- Artificial Neural Network
- Blast furnace slag
- Compressive strength
- Fly ash
- Grey Wolf Optimizer
- High-Performance Concrete
Cite this
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Predicting the compressive strength of normal and High-Performance Concretes using ANN and ANFIS hybridized with Grey Wolf Optimizer. / Golafshani, Emadaldin Mohammadi; Behnood, Ali; Arashpour, Mehrdad.
In: Construction and Building Materials, Vol. 232, 117266, 30.01.2020.Research output: Contribution to journal › Article › Research › peer-review
TY - JOUR
T1 - Predicting the compressive strength of normal and High-Performance Concretes using ANN and ANFIS hybridized with Grey Wolf Optimizer
AU - Golafshani, Emadaldin Mohammadi
AU - Behnood, Ali
AU - Arashpour, Mehrdad
PY - 2020/1/30
Y1 - 2020/1/30
N2 - 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.
AB - 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.
KW - Adaptive Network-Based Fuzzy Inference System
KW - Artificial Neural Network
KW - Blast furnace slag
KW - Compressive strength
KW - Fly ash
KW - Grey Wolf Optimizer
KW - High-Performance Concrete
UR - http://www.scopus.com/inward/record.url?scp=85073393456&partnerID=8YFLogxK
U2 - 10.1016/j.conbuildmat.2019.117266
DO - 10.1016/j.conbuildmat.2019.117266
M3 - Article
VL - 232
JO - Construction and Building Materials
JF - Construction and Building Materials
SN - 0950-0618
M1 - 117266
ER -