基于改进特征筛选的随机森林算法对锂渣混凝土强度的预测研究

doi:10.11896/cldb.22050319

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Abstract This work proposed the method of feature variable screening combined with feature variable correlation to optimize the constructed 28-day compressive strength database of lithium-slag concrete, and established random forest model and deep neural network model for testing the database, respectively, and compared the prediction results of the models with three indicators: correlation coefficient (R), root mean square error (RMSE) and mean relative error (MAE). The results show that the improved feature variable screening method can effectively improve the prediction effect of the model when predicting the 28-day compressive strength of lithium-slag concrete, and in addition, the prediction effect of the before-and-after random forest (RF) model with feature variable screening is significantly better than that of the deep neural network (DNN) model.

Key words： random forest (RF) deep neural network (DNN) variable selection concrete with lithium slag compressive strength

Published: 10 May 2024 Online: 2024-05-13

CLC number:	TU528
	TP312

Fund:2021 Industry Technology Foundation Public Service Platform-Key Raw Material Industry Carbon Peak and Carbon Neutrality Public Service Platform Project (2021-H029-1-1).

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	WEI Linggang
	HUANG Liang
	ZENG Linghong

Cite this article:

WEI Linggang,HUANG Liang,ZENG Linghong. Using Random Forest with Improved Variable Selection to Predict the Compressive Strength of Concrete with Lithium Slag[J]. Materials Reports, 2024, 38(9): 22050319-6.

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http://www.mater-rep.com/EN/10.11896/cldb.22050319 OR http://www.mater-rep.com/EN/Y2024/V38/I9/22050319

1 Dai L X. Nonferrous Metals Engineering, 2007(4), 123 (in Chinese).
戴立新. 有色金属, 2007(4), 123.
2 Yang F. Study on properties and influencing factors of pervious concrete by solid waste materials. Master's Thesis, Southeast University, China, 2020 (in Chinese).
杨锋. 利用固废材料制备透水混凝土及其性能影响因素研究. 硕士学位论文, 东南大学, 2020.
3 Hu X. Prediction of high performance concrete strength based on artificial neural network. Master's Thesis, Hunan University, China, 2014 (in Chinese).
胡鑫. 基于人工神经网络的HPC强度预测. 硕士学位论文, 湖南大学, 2014.
4 Yang C C, Huang R. Cement and Concrete Research, 1996, 26(10), 1567.
5 Kang S, Lloyd Z, Kim T, et al. Cement and Concrete Research, 2020, 137, 106218.
6 Hu C X, Zhang Y S, Peng S. Journal of Chongqing University of Techno-logy(Natural Science), 2023, 37(9), 208(in Chinese).
胡昌秀, 张仰森, 彭爽. 重庆理工大学学报(自然科学), 2023, 37(9), 208.
7 Xiao Z H, Yu H, Wang Y C. Journal of Chongqing University of Techno-logy(Natural Science), 2018, 32(11), 134(in Chinese).
肖枝洪, 于浩, 王一超. 重庆理工大学学报(自然科学), 2018, 32(11), 134.
8 Li X Y, Ma C Y, Zhao X D. Journal of Functional Materials, 2020, 51(1), 1126(in Chinese).
李心源, 马春阳, 赵旭东. 功能材料, 2020, 51(1), 1126.
9 Dao D V, Ly H B, Vu H L T, et al. Materials, 2020, 13(5), 1072.
10 Gupta T, Patel K A, Siddique S, et al. Measurement, 2019, 147, 106870.
11 Huang W, Zhou L, Ge P, et al. Materials Reports, 2021, 35(15),15026 (in Chinese).
黄炜, 周烺, 葛培, 等. 材料导报, 2021, 35(15), 15026.
12 Azimi-Pour M, Eskandari-Naddaf H, Pakzad A. Construction and Buil-ding Materials, 2020, 230, 117021.
13 Abd A M, Abd S M. Case studies in Construction Materials, 2017, 6, 8.
14 Xu J C, Ren Q W, Shen Z Z. Annals of Nuclear Energy, 2015, 85, 296.
15 Han Q, Gui C, Xu J, et al. Construction and Building Materials, 2019, 226, 734.
16 Mohamed O A, Ati M, Najm O F. Key Engineering Materials, 2017, 744, 141.
17 Sun Y, Li G, Zhang J, et al. Advances in Civil Engineering, 2019, 2019,1.
18 Fernández-Delgado M, Cernadas E, Barro S, et al. The Journal of Machine Learning Research, 2014, 15(1), 3133.
19 Scornet E. Journal of Multivariate Analysis, 2016, 146, 72.
20 Zhang J, Ma G, Huang Y, et al.Construction and Building Materials, 2019, 210, 713.
21 Robin Genuer, Poggi Jean-Michel, Tuleau-Malot Christine. Pattern Re-cognition Letters, 2010, 31(14), 2225.
22 Bi L P. Experimental study on influence of lithium slag admixtures on durability of concrete. Master's Thesis, East China Jiaotong University, China, 2011 (in Chinese).
毕丽苹. 锂渣掺和料对混凝土耐久性影响的试验研究. 硕士学位论文, 华东交通大学, 2017.
23 Fan Y, Shi K B. China Rural Water and Hydropower, 2013(3), 119 (in Chinese).
范勇, 侍克斌. 中国农村水利水电, 2013(3), 119.
24 Hu Z Y. Study on concrete mixing lithium slag with other mineral admixtures. Master's Thesis, Chongqing University, China, 2008 (in Chinese).
胡志远. 锂渣复合渣混凝土研究. 硕士学位论文, 重庆大学, 2008.
25 Mao Y Z. Study on pozzolanic activity of lithium slag from lithium mica in Yichun and its effect on the performance of cement and concrete. Master's Thesis, Nanchang University, China, 2017 (in Chinese).
毛意中. 宜春锂云母提锂渣的火山灰活性及其对水泥混凝土性能的影响研究. 硕士学位论文, 南昌大学, 2017.
26 Wang C. Study on the effect of lithium slag on the adsorption of chloride ions in concrete. Master's Thesis, Xinjiang University, China, 2018 (in Chinese).
王晨. 锂渣对混凝土中氯离子吸附性作用的研究. 硕士学位论文, 新疆大学, 2018.
27 Wang G Q. Study on crack resistance of high performance concrete with lithium-slag. Master's Thesis, Xinjiang Agricultural University, China, 2011 (in Chinese).
王国强. 锂渣高性能混凝土收缩与抗裂性能研究. 硕士学位论文, 新疆农业大学, 2011.
28 Wen H. Research on preparation and concrete of lithium slag composite powder. Master's Thesis, Chongqing University, China, 2006 (in Chinese).
温和. 锂盐渣复合粉体制备与混凝土研究. 硕士学位论文, 重庆大学, 2006.
29 Wen Y, Liu G J, Qin Z Y, et al. Concrete, 2011(8), 76 (in Chinese).
温勇, 刘国君, 秦志勇,等. 混凝土, 2011(8), 76.
30 Xu K C, Bi L P, Chen M C. Concrete, 2017(1), 125 (in Chinese).
许开成, 毕丽苹, 陈梦成. 混凝土, 2017(1), 125.
31 Yang H Y. Experimental study on the strength and the cracking resistance at early ages of high-performance concrete added with lithium slag and fly ash. Master's Thesis, Xinjiang Agricultural University, China, 2012 (in Chinese).
杨恒阳. 复掺锂渣、粉煤灰高性能混凝土强度及早期抗裂性能试验研究. 硕士学位论文, 新疆农业大学, 2012.
32 Zhang L F, Chen J X, Li S W, et al. Construction Technology, 2005(8), 59 (in Chinese).
张兰芳, 陈剑雄, 李世伟,等.施工技术, 2005(8), 59.
33 Zhang S D. Forecasting of lithium-slag high performance concrete strength and ring method test on its early- age anti-crack capability. Master's Thesis, Xinjiang Agricultural University, China, 2011 (in Chinese).
张善德. 锂渣高性能混凝土强度预测及圆环法早期抗裂性试验研究. 硕士学位论文, 新疆农业大学, 2011.
34 Zhao Q S. Science and Technology of West China, 2014, 13(7), 36 (in Chinese).
赵强善. 中国西部科技, 2014, 13(7), 36.
35 Zhu Z K. Study on high strength high performance self compacting concrete containing lithium slag composite slag. Master's Thesis, Chongqing University, China, 2007 (in Chinese).
祝战奎. 锂渣复合渣高强高性能自密实混凝土研究. 硕士学位论文, 重庆大学, 2007.
36 Degenhardt F, Seifert S, Szymczak S. Briefings in Bioinformatics, 2019, 20(2), 492.
37 Hapfelmeier A, Ulm K. Computational Statistics & Data Analysis, 2013, 60, 50.
38 Le L M, Ly H B, Pham B T, et al. Materials, 2019, 12(10), 1670.
39 Dao D V, Trinh S H, Ly H B, et al. Applied Sciences, 2019, 9(6), 1113.
40 Breiman L. Machine Learning, 2001, 45(1), 5.
41 Shin H C, Roth H R, Gao M, et al. IEEE Transactions on Medical Imaging, 2016, 35(5), 1285.
42 Zhang J, Zheng Y, Qi D, et al. In: Proceedings of the 24th ACM SIGSPATIAL international conference on advances in geographic information systems. America, 2016, pp.1.
43 Sherstinsky A. Physica D: Nonlinear Phenomena, 2020, 404, 132306.
44 Deng L, Hinton G, Kingsbury B. In: 2013 IEEE international conference on acoustics, speech and signal processing. Canada, 2013, pp.8599.
45 Barron A R. IEEE Transactions on Information Theory, 1993, 39(3), 930.
46 Khorsheed M S, Al-Thubaity A O. Language Resources and Evaluation, 2013, 47(2), 513.
47 Leema N, Nehemiah H K, Kannan A. Applied Soft Computing, 2016, 49, 834.
48 Liu P, Wu X, Cheng H, et al. In: IOP Conference Series: Earth and Environmental Science. China, 2020, pp.012020.
49 Li B L, You N Q, Cao R L, et al. Materials Reports, 2020, 34(10), 10046 (in Chinese).
李保亮, 尤南乔, 曹瑞林,等. 材料导报, 2020, 34(10), 10046.
50 Liu Z, Deng P, Zhang Z.Construction and Building Materials, 2022, 356, 129142.
51 Wang X, Wang H, Wang Q. Materials Reports, 2022, 36(24), 63(in Chinese).
王雪, 王恒, 王强. 材料导报, 2022, 36(24), 63.
52 Wang Y R. Evaluation of pozzolanic activity of lithium slag and study on microstructure characteristics of composite cementitious materials. Ph.D. Thesis, China University of Mining & Technology (Beijing), China, 2018 (in Chinese).
王奕仁. 锂渣的火山灰活性评价及其复合胶凝材料微结构特性研究. 博士学位论文, 中国矿业大学(北京), 2018.
53 He Z, Li L, Du S.Construction and Building Materials, 2017, 147, 296.
54 He Z, Du S, Chen D.Journal of Hazardous Materials, 2018, 353, 35.