Compressive Strength Prediction of Rice Husk Ash Concrete Using a Hybrid Artificial Neural Network Model
Autor: | Chuanqi Li, Xiancheng Mei, Daniel Dias, Zhen Cui, Jian Zhou |
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Jazyk: | angličtina |
Rok vydání: | 2023 |
Předmět: |
rice husk ash
concrete compressive strength reptile search algorithm with circle mapping artificial neural network Technology Electrical engineering. Electronics. Nuclear engineering TK1-9971 Engineering (General). Civil engineering (General) TA1-2040 Microscopy QH201-278.5 Descriptive and experimental mechanics QC120-168.85 |
Zdroj: | Materials, Vol 16, Iss 8, p 3135 (2023) |
Druh dokumentu: | article |
ISSN: | 1996-1944 |
DOI: | 10.3390/ma16083135 |
Popis: | The combination of rice husk ash and common concrete both reduces carbon dioxide emission and solves the problem of agricultural waste disposal. However, the measurement of the compressive strength of rice husk ash concrete has become a new challenge. This paper proposes a novel hybrid artificial neural network model, optimized using a reptile search algorithm with circle mapping, to predict the compressive strength of RHA concrete. A total of 192 concrete data with 6 input parameters (age, cement, rice husk ash, super plasticizer, aggregate, and water) were utilized to train proposed model and compare its predictive performance with that of five other models. Four statistical indices were adopted to evaluate the predictive performance of all the developed models. The performance evaluation indicates that the proposed hybrid artificial neural network model achieved the most satisfactory prediction accuracy regarding R2 (0.9709), VAF (97.0911%), RMSE (3.4489), and MAE (2.6451). The proposed model also had better predictive accuracy than that of previously developed models on the same data. The sensitivity results show that age is the most important parameter for predicting the compressive strength of RHA concrete. |
Databáze: | Directory of Open Access Journals |
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