Development of a hybrid artificial intelligence model to predict the uniaxial compressive strength of a new aseismic layer made of rubber-sand concrete
文献类型：期刊论文
| 作者 | Mei, Xiancheng; Li, Chuanqi; Sheng, Qian; Cui, Zhen; Zhou, Jian; Dias, Daniel |
| 刊名 | MECHANICS OF ADVANCED MATERIALS AND STRUCTURES
 |
| 出版日期 | 2023-06-03 |
| 卷号 | 30期号:11页码:2185 |
| 关键词 | Rubber-sand concrete uniaxial compressive strength back propagation neural network swarm intelligence optimization algorithm |
| ISSN号 | 1537-6494 |
| 英文摘要 | This study, proposes the use of a novel rubber-sand concrete (RSC) material, which comprises rubber particles, sand, and cement, as an aseismic material in practical engineering construction. The uniaxial compressive strength (UCS) of damping materials is an important factor that directly affects the seismic activity in underground structures. To predict the UCS of RSC, artificial intelligence model back propagation neural network (BPNN), which is optimized through four swarm intelligence optimization (SIO) algorithms: particle swarm optimization algorithm (PSO), fruit fly optimization algorithm (FOA), lion swarm optimization algorithm (LSO), and sparrow search algorithm (SSA), is used. The dataset for the prediction models was obtained from uniaxial compression tests in the RSC laboratory. The performances of the four hybrid intelligence models were evaluated using six performance indicators: the root mean square error (RMSE), correlation coefficient (R), determination coefficient (R-2), mean absolute error (MAE), mean square error (MSE), and sum of square error (SSE).The prediction capability of these models was graded based on these indicators using a ranking system. The results show that the prediction ability of the LSO-BPNN hybrid model is better than that of the three other hybrid models, with RMSE of (1.0635, 1.2352), R of (0.9887, 0.9713), R-2 of (0.9776, 0.9165), MAE of (0.7257, 0.8243), MSE of (1.1352, 1.5256), SSE of (64.7074, 36.6151), and ranking score of (24, 24) in the training and testing phases, respectively. Therefore, the LSO-BPNN hybrid model is an efficient and accurate method for predicting the UCS of RSCs. Sensitivity analysis showed that rubber and sand were the most important elements that affected UCS prediction, followed by cement, with the lowest relative importance being RPZ. This study provides guidance for the extension and application of RSC materials to underground seismic engineering. |
| 学科主题 | Materials Science ; Mechanics |
| 语种 | 英语 |
| WOS记录号 | WOS:000772313900001 |
| 出版者 | TAYLOR & FRANCIS INC |
| 源URL | [http://119.78.100.198/handle/2S6PX9GI/35555]  |
| 专题 | 中科院武汉岩土力学所 |
| 作者单位 | 1.Chinese Academy of Sciences; Wuhan Institute of Rock & Soil Mechanics, CAS; 2.Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; 3.Centre National de la Recherche Scientifique (CNRS); 4.CNRS - Institute for Engineering & Systems Sciences (INSIS); 5.UDICE-French Research Universities; Communaute Universite Grenoble Alpes; Institut National Polytechnique de Grenoble; 6.Universite Grenoble Alpes (UGA); 7.Central South University; 8.Hefei University of Technology |
| 推荐引用方式 GB/T 7714 | Mei, Xiancheng,Li, Chuanqi,Sheng, Qian,et al. Development of a hybrid artificial intelligence model to predict the uniaxial compressive strength of a new aseismic layer made of rubber-sand concrete[J]. MECHANICS OF ADVANCED MATERIALS AND STRUCTURES,2023,30(11):2185. |
| APA | Mei, Xiancheng,Li, Chuanqi,Sheng, Qian,Cui, Zhen,Zhou, Jian,&Dias, Daniel.(2023).Development of a hybrid artificial intelligence model to predict the uniaxial compressive strength of a new aseismic layer made of rubber-sand concrete.MECHANICS OF ADVANCED MATERIALS AND STRUCTURES,30(11),2185. |
| MLA | Mei, Xiancheng,et al."Development of a hybrid artificial intelligence model to predict the uniaxial compressive strength of a new aseismic layer made of rubber-sand concrete".MECHANICS OF ADVANCED MATERIALS AND STRUCTURES 30.11(2023):2185. |
入库方式： OAI收割
来源：武汉岩土力学研究所
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