Compressive strength prediction of sprayed concrete lining in tunnel engineering using hybrid machine learning techniques
文献类型：期刊论文
| 作者 | Yin, Xin2,4; Gao, Feng2,4; Wu, Jian3; Huang, Xing1; Pan, Yucong2,4; Liu, Quansheng2,4 |
| 刊名 | UNDERGROUND SPACE
 |
| 出版日期 | 2022-10-01 |
| 卷号 | 7期号:5页码:928 |
| 关键词 | Intelligent construction Hybrid machine learning Sprayed concrete lining Compressive strength prediction Tunnel engineering |
| ISSN号 | 2096-2754 |
| 英文摘要 | Sprayed concrete lining is a commonly employed support measure in tunnel engineering, which plays an important role in construc-tion safety. Compressive strength is a key performance indicator of sprayed concrete lining, and the traditional measuring method is time-consuming and laborious. This paper proposes various hybrid machine learning algorithms to accomplish the advanced prediction of compressive strength of sprayed concrete lining based on the mixture design. Two hundred and five sets of experimental data were collected from a water conveyance tunnel in northwestern China for model construction, and each set of data was made up of six basic input variables (i.e., water, cement, mineral powder, superplasticizer, coarse aggregate, and fine aggregate) and one output variable (i.e., compressive strength). In order to eliminate the correlation between input variables, a new composite indicator (i.e., the water-binder ratio) was introduced to achieve dimensionality reduction. After that, four hybrid models in total were built, namely BPNN-QPSO, SVR-QPSO, ELM-QPSO, and RF-QPSO, where the hyper-parameters of BPNN, SVR, ELM, and RF were auto-tuned by QPSO. Engi-neering application results indicated that RF-QPSO achieved the lowest mean absolute percentage error (MAPE) of 3.47% and root mean square error (RMSE) of 1.30 and the highest determination coefficient (R2) of 0.93 in the four hybrid models. Moreover, RF-QPSO had the shortest running time of 0.15 s, followed by SVR-QPSO (0.18 s), ELM-QPSO (1.19 s), and BPNN-QPSO (1.58 s). Com-pared with BPNN-QPSO, SVR-QPSO, and ELM-QPSO, RF-QPSO performed the most superior performance in terms of both predic-tion accuracy and running speed. Finally, the importance of input variables on the model performance was quantitatively evaluated, further enhancing the interpretability of RF-QPSO. |
| 学科主题 | Engineering |
| 语种 | 英语 |
| WOS记录号 | WOS:000848547200005 |
| 出版者 | KEAI PUBLISHING LTD |
| 源URL | [http://119.78.100.198/handle/2S6PX9GI/35212]  |
| 专题 | 中科院武汉岩土力学所 |
| 作者单位 | 1.State Key Laboratory of Geomechanics and Geotechnical Engineering, Institute of Rock and Soil Mechanics, Chinese Academy of Sciences, Wuhan 430071, China 2.State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan 430072, China 3.PowerChina Huadong Engineering Corporation Limited, Hangzhou 311122, China 4.The Key Laboratory of Safety for Geotechnical and Structural Engineering of Hubei Province, School of Civil Engineering, Wuhan University, Wuhan 430072, China |
| 推荐引用方式 GB/T 7714 | Yin, Xin,Gao, Feng,Wu, Jian,et al. Compressive strength prediction of sprayed concrete lining in tunnel engineering using hybrid machine learning techniques[J]. UNDERGROUND SPACE,2022,7(5):928. |
| APA | Yin, Xin,Gao, Feng,Wu, Jian,Huang, Xing,Pan, Yucong,&Liu, Quansheng.(2022).Compressive strength prediction of sprayed concrete lining in tunnel engineering using hybrid machine learning techniques.UNDERGROUND SPACE,7(5),928. |
| MLA | Yin, Xin,et al."Compressive strength prediction of sprayed concrete lining in tunnel engineering using hybrid machine learning techniques".UNDERGROUND SPACE 7.5(2022):928. |
入库方式： OAI收割
来源：武汉岩土力学研究所
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