Strength Forecasting of Backfilling Materials by BP Neural Network Model Collaborated with Orthogonal Experiment
DONG Yue1, YANG Zhiqiang1, 2, GAO Qian1
1 Key Laboratory of High Efficient Mining and Safety of Metal Mine Ministry of Education, University of Science and Technology Beijing, Beijing 100083; 2 State Key Laboratory of Comprehensive Utilization of Nickel and Cobalt Resources, Jinchuan Group Co. LTD., Jinchang 737100
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.
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2018, Vol. 32 
