Abstract

Analysis of the geological conditions of high-altitude and low-temperature stope slopes and the study of grade division are the basis for the evaluation of slope stability. Based on the engineering background of the eastern slope of the Preparatory iron mine in Hejing County, Xinjiang, we comprehensively analyse and summarize the factors that affect the geological conditions of high-altitude and cold slopes and finally determine nine geological conditions that affect the index parameters. Based on a back-propagation (BP) neural network algorithm, we establish an applicable network model to analyse the geological conditions of slopes in cold areas. The model is applied to the eastern slope to analyse and classify the geological conditions of the high-altitude and low-temperature slopes. The research results show that the skarn rock layer in the eastern slope is in a stable state and not prone to landslides, and its corresponding geological condition is Grade I; meanwhile, the monzonite porphyry rock layer is in a relatively stable state, with a potential for landslides and a corresponding geological condition Grade II. The marble rock layer is in a generally stable state, there is the possibility of landslide accidents, and the corresponding geological condition level is Grade III. The limestone rock layer is in an unstable state and prone to landslide accidents, it has a corresponding geology condition Grade IV. Therefore, the eastern slope can be divided into different geological condition regions: Zone I, Zone II, Zone III, and Zone IV, and the corresponding geological condition levels for these are Grade I, Grade II, Grade III, and Grade IV. These results may provide a basis for the stability evaluation of high altitudes and cold slopes.

Highlights

  • Slope instability is one of the world’s geological disasters [1,2,3,4]

  • Based on the engineering background of the eastern slope of the Preparatory iron mine in Hejing County, Xinjiang, this paper comprehensively analyses and summarizes the factors that affect the geological conditions of high-altitude and cold slopes and determines nine geological conditions that affect the index parameters [5,7,13,14,15,16]

  • According to the training results of the BP neural network model based on the training samples in the previous section, the accuracy of the network is high, so it can be used to prepare for the calculation of the iron ore geological index parameter samples

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Summary

Introduction

Slope instability is one of the world’s geological disasters [1,2,3,4]. Every year, the economic losses of various countries in the world caused by geological disasters due to slope instability reach immeasurable levels [5,6,7]. Open pit mine slope landslides are a potential hazard in harsh environments with high altitudes and cold areas. Based on the engineering background of the eastern slope of the Preparatory iron mine in Hejing County, Xinjiang, this paper comprehensively analyses and summarizes the factors that affect the geological conditions of high-altitude and cold slopes and determines nine geological conditions that affect the index parameters [5,7,13,14,15,16]. Based on a back-propagation (BP) neural network algorithm, a network mode is established that is suitable for the analysis of the geological conditions of slopes in cold areas. This model is applied to the east slope to analyse and classify the geological conditions of high-altitude and low-temperature slopes

BP Neural Network Operation Mechanism
Data Processing
BP Neural
The diversified
Construction of a BP Neural Network Suitable for Preparing Iron Ore Slopes
Sample Training and Result Analysis
Regression
Determination of Parameter Samples of Geological Condition Indicators
Calculation Results and Analysis
Concluding Remarks
Full Text
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