Application of Grey Deformation Prediction Model Optimized by Double Coefficient for Tailings Dam

Cunji Chu, Gangnian Xu and Junwei Ma

Mathematical Problems in Engineering, 2022, vol. 2022, 1-15

Abstract: As a nonuniform structure, tailings dam undergoes complex and chaotic nonlinear changes, under the joint influence of multiple dynamic or nondynamic factors. These changes make it difficult to predict the deformation of tailings dam accurately with a numerical model. To solve the problem, this paper proposes a grey deformation prediction model optimized by double coefficient (GDPM-DC). Unlike a single grey prediction model, the GDPM-DC does not mutate significantly but adapts well to specific scenarios. Besides, the model can smoothen and stabilize the original data and thus achieve accurate prediction of the deformation of tailings dam. The main results are as follows. The GDPM-DC made more accurate predictions than the traditional grey model (1, 1) (GM (1, 1)), the grey model based on logarithmic transformation (GM-LT), and the grey model optimized by weight coefficient (GM-WC). It significantly improved the overall prediction accuracy of the vertical and transverse deformations of the dam and controlled the relative error of the predicted seepage pressure to 2.79%–3.71%. Moreover, the model could forecast the trend component and random fluctuation component of seepage pressure effectively, fit the increasing trend in stages 1–3 and the decreasing trend in stages 3–9, and realize the quantitative prediction of deformation law for the operating tailings dam. The research results provide a meaningful reference for the instability analysis and safety management of tailings dam.

Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlmpe:6103860

DOI: 10.1155/2022/6103860

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