Compressive Strength Prediction of Rice Husk Ash Concrete Using a Hybrid Artificial Neural Network Model
文献类型：期刊论文
| 作者 | Li, Chuanqi; Mei, Xiancheng; Dias, Daniel; Cui, Zhen; Zhou, Jian |
| 刊名 | MATERIALS
 |
| 出版日期 | 2023-04-01 |
| 卷号 | 16期号:8 |
| 关键词 | rice husk ash concrete compressive strength reptile search algorithm with circle mapping artificial neural network |
| 英文摘要 | 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 R-2 (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. |
| 学科主题 | Chemistry ; Materials Science ; Metallurgy & Metallurgical Engineering ; Physics |
| 语种 | 英语 |
| 出版者 | MDPI |
| WOS记录号 | WOS:000977064700001 |
| 源URL | [http://119.78.100.198/handle/2S6PX9GI/35509]  |
| 专题 | 中科院武汉岩土力学所 |
| 作者单位 | 1.Centre National de la Recherche Scientifique (CNRS); 2.CNRS - Institute for Engineering & Systems Sciences (INSIS); 3.UDICE-French Research Universities; Communaute Universite Grenoble Alpes; Institut National Polytechnique de Grenoble; 4.Universite Grenoble Alpes (UGA); 5.Chinese Academy of Sciences; Wuhan Institute of Rock & Soil Mechanics, CAS; 6.Central South University |
| 推荐引用方式 GB/T 7714 | Li, Chuanqi,Mei, Xiancheng,Dias, Daniel,et al. Compressive Strength Prediction of Rice Husk Ash Concrete Using a Hybrid Artificial Neural Network Model[J]. MATERIALS,2023,16(8). |
| APA | Li, Chuanqi,Mei, Xiancheng,Dias, Daniel,Cui, Zhen,&Zhou, Jian.(2023).Compressive Strength Prediction of Rice Husk Ash Concrete Using a Hybrid Artificial Neural Network Model.MATERIALS,16(8). |
| MLA | Li, Chuanqi,et al."Compressive Strength Prediction of Rice Husk Ash Concrete Using a Hybrid Artificial Neural Network Model".MATERIALS 16.8(2023). |
入库方式： OAI收割
来源：武汉岩土力学研究所
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