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1

Energy School, Xian University of Science and Technology, Xian 710054, China

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Key Laboratory of Western Mines and Hazards Prevention, Ministry of Education of China, Xian 710054, China

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Department of Civil Engineering, Inha University, Incheon 402-751, Korea

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School of Civil & Resource Engineering, University of Western Australia, Perth 6009, Australia

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Department of Civil and Environmental Engineering, Korea Advanced Institute of Science and Technology KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon 305-701, Korea





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Author to whom correspondence should be addressed.



Academic Editor: Christof Schneider

Abstract Workability is a key performance criterion for mining cemented tailing backfill, which should be defined in terms of rheological parameters such as yield stress and plastic viscosity. Cemented tailing backfill is basically composed of mill tailings, Portland cement, or blended cement with supplementary cement material fly ash and blast furnace slag and water, among others, and it is important to characterize relationships between paste components and rheological properties to optimize the workability of cemented tailing backfill. This study proposes a combined model for predicting rheological parameters of cemented tailing backfill based on a principal component analysis PCA and a back-propagation BP neural network. By analyzing experimental data on mix proportions and rheological parameters of cemented tailing backfill to determine the nonlinear relationships between rheological parameters i.e., yield stress and viscosity and mix proportions i.e., solid concentrations, the tailing-cement ratio, the specific weight, and the slump, the study constructs a prediction model. The advantages of the combined model were as follows: First, through the PCA, original multiple variables were represented by two principal components PCs, thereby leading to a 50% decrease in input parameters in the BP neural network model, which covered 98.634% of the original data. Second, in comparison to conventional BP neural network models, the proposed model featured a simpler network architecture, a faster training speed, and more satisfactory prediction performance. According to the test results, any error between estimated and expected output values from the combined prediction model based on the PCA and the BP neural network was within 5%, reflecting a remarkable improvement over results for BP neural network models with no PCA. View Full-Text

Keywords: cemented tailing backfill; yield stress; viscosity; principle component analysis PCA; back-propagation BP neural network cemented tailing backfill; yield stress; viscosity; principle component analysis PCA; back-propagation BP neural network





Autor: Liu Lang 1,2, KI-IL Song 3,* , Dezheng Lao 4 and Tae-Hyuk Kwon 5

Fuente: http://mdpi.com/



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