正交试验协同BP神经网络模型预测充填体强度

doi:10.11896/j.issn.1005-023X.2018.06.033

Abstract
Figure/Table
References
Related Citation (6)

Download:

Full Text ( PDF ) (1409KB)
Export: BibTeX | EndNote (RIS)

Abstract In order to forecast the strength of backfilling materials accurately,based on the 18 mixed samples of orthogonal test, the 4×Y×2 BP neural network model was established, in which cement clinker, desulphurized ash, Glauber’s salt and steel slag were utilized as four input factors and the compressive strength of 7 d and 28 d were used as output factors. Then, the training error curve and the predicted intensity error were analyzed. When the number of neurons in the hidden layer was 9, the prediction strength error of the model is minimized and the average error was 0.7%. The results revealed that the fitting correlation coefficient was 0.999 89 and the maximum relative error of 7 d and 28 d prediction strength were 4.33% and 0.84%, respectively. The method of BP neural network model collaborated with orthogonal experiment to forecast the compressive strength of backfilling materials are more feasibility and accurate. This method has advantages of high uniformity and non-linear optimization of input data, which provides a new idea for accurate prediction of backfilling materials strength.

Key words： BP neural network model orthogonal experiment backfilling materials strength forecasting

Published: 25 March 2018 Online: 2018-03-25

CLC number:

TD853

	Service
	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	DONG Yue
	YANG Zhiqiang
	GAO Qian

Cite this article:

DONG Yue,YANG Zhiqiang,GAO Qian. Strength Forecasting of Backfilling Materials by BP Neural Network Model Collaborated with Orthogonal Experiment[J]. Materials Reports, 2018, 32(6): 1032-1036.

URL:

https://www.mater-rep.com/EN/10.11896/j.issn.1005-023X.2018.06.033 OR https://www.mater-rep.com/EN/Y2018/V32/I6/1032

1 Dong Y, Yang Z Q, Gao Q. Effect of steel slag substitution on the properties of composite cementitious backfill material[J].Bulletin of the Chinese Ceramic Society,2016(9):2967(in Chinese).
董越,杨志强,高谦.钢渣取代量对复合充填胶凝材料性能的影响[J].硅酸盐通报,2016(9):2967.
2 Feng G M. Research on the superhigh-water packing material and filling mining technology and their application[D].Xuzhou:China University of Mining and Technology,2009(in Chinese).
冯光明. 超高水充填材料及其充填开采技术研究与应用[D].徐州:中国矿业大学,2009.
3 Yang J H. Gangue stowing technology of full-mechanized mining face with large mining-height in deep mine[J].Coal Mining Technology,2014(6):77(in Chinese).
杨军辉. 深部大采高综采面矸石充填开采技术[J].煤矿开采,2014(6):77.
4 Liu C, Han B, Sun W, et al. Experimental study of strength of backfillings of cemented rock debris and its application under low temperature condition[J].Chinese Journal of Rock Mechanics and Engineering,2015(1):139(in Chinese).
刘超,韩斌,孙伟,等.高寒地区废石破碎胶结充填体强度特性试验研究与工业应用[J].岩石力学与工程学报,2015(1):139.
5 Wei W, Gao Q. Strength prediction of backfilling body based on modified BP neural network[J].Journal of Harbin Institute of Technology,2013(6):90(in Chinese).
魏微,高谦.改进的BP神经网络模型预测充填体强度[J].哈尔滨工业大学学报,2013(6):90.
6 Xu M F, Gao Y T, Jin A B, et al. Prediction of cemented backfill strength by ultrasonic pulse velocity and BP neural network[J].Chinese Journal of Engineering,2016(8):1059(in Chinese).
徐淼斐,高永涛,金爱兵,等.基于超声波波速及BP神经网络的胶结充填体强度预测[J].工程科学学报,2016(8):1059.
7 Zhang Q L, Li X P, Yang W. Optimization of filling slurry ratio in a mine based on back-propagation neural network[J].Journal of Central South University(Science and Technology),2013(7):2867(in Chinese).
张钦礼,李谢平,杨伟.基于BP网络的某矿山充填料浆配比优化[J].中南大学学报(自然科学版),2013(7):2867.
8 Liu H L, Zhang Q L, Wang X M, et al. Strength prediction of unclassified tailing backfilling based on orthogonal-BP neural network[J].Metal Mine,2016(1):43(in Chinese).
刘恒亮,张钦礼,王新民,等.全尾砂充填体正交-BP神经网络强度预测[J].金属矿山,2016(1):43.
9 Li Peng. Analysis and research on influence factors of cement tailings[D].Kunming:Kunming University of Science and Technology,2011(in Chinese).
李鹏. 尾砂胶结充填体强度影响因素分析与研究[D].昆明:昆明理工大学,2011.
10 Liu Z X, Zhou S L, Guo Y L. GA-BP neural network prediction model for strength of phosphogypsum backfill[J].Mining and Metallurgical Engineering,2011(6):1(in Chinese).
刘志祥,周士霖,郭永乐.磷石膏充填体强度GA-BP神经网络预测模型[J].矿冶工程,2011(6):1.
11 Han B, Wang X L, Xiao W G. Estimation and evaluation of backfill strength in underground stope based on multivariate nonlinear[J].Journal of Mining & Safety Engineering,2012(5):714(in Chinese).
韩斌,王贤来,肖卫国.基于多元非线性回归的井下采场充填体强度预测及评价[J].采矿与安全工程学报,2012(5):714.
12 何玉斌,李新忠. 神经网络控制技术及其应用[M].北京:科学出版社,2000.
13 Zhang S, Wang B, Li X,et al. Research and application of improved gas concentration prediction model based on grey theory and BP neural network in digital mine[J].Procedia CIRP,2016,56:471.
14 Jiang J P. Prediction of concrete strength based on BP neural network[J].Advanced Materials Research,2012, 341-342:58.
15 Yao Hui Qin, Jiang Ye Long. Based on the genetic algorithm to optimize the BP neural network in the degree of concrete creep prediction model[J].Applied Mechanics and Materials,2014, 584-586:1346.
16 Zhang Y H, Sun H, Li X, et al. Study of steam-cured system impact on concrete compressive strength and prediction of strength model[J].Materials Review,2015(s2):554(in Chinese).
张耀煌,孙红,李晓,等.蒸养制度对混凝土抗压强度的影响及其预测研究[J].材料导报,2015(s2):554.
17 Wang Y T, Yang Z Q, Li M H,et al. Preparation of new backfill cementitious materials with unclassified tailings-rod milling sands[J].Chinese Journal of Materials Research,2015(4):291(in Chinese).
王有团,杨志强,李茂辉,等.全尾砂-棒磨砂新型胶凝充填材料的制备[J].材料研究学报,2015(4):291.