Research on the Prediction Model of Mine Subsidence Based on Object-Oriented and Probability Integration Method

Gu, Zhongyuan; Zhao, Yanlin; Gao, Rugao; Wu, Lewen

doi:10.1155/2022/8107024

Cited by 5 publications

(3 citation statements)

References 33 publications

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“…The subsidence value at any point within a surface subsidence movement basin induced by mining subsidence can be represented as follows [32,33]:…”

Section: Prediction Principles Of the Probability Integral Methodsmentioning

confidence: 99%

Research on Mining Subsidence Prediction Parameter Inversion Based on Improved Modular Vector Method

Chai,

Xu,

Guan

et al. 2023

Applied Sciences

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In this study conducted in the Shendong mining area, this paper tackles the challenge of estimating mining subsidence parameters in the absence of empirical values. The study employs a tailored pattern recognition method specifically designed for mining subsidence in a specific working face. The goal is to determine a globally approximate optimal solution for these parameters. Subsequently, this study utilizes the approximate optimal solution as an initial exploration value and harnesses the modular vector method to obtain stable, accurate, optimal solutions for the parameters. The results of the study demonstrate the effectiveness of the improved modular vector method. In simulation tests involving the subsidence coefficient, the main influence angle tangent value, the propagation angle of mining influence, and the deviation of the inflection point, the relative errors do not exceed 1.2%, 1.9%, 1.7%, and 7.9%, respectively. Furthermore, when subjected to random errors of less than 20 mm, the relative errors for each parameter remain below 2%. Even in conditions with 200 mm gross error, the relative error for each parameter does not exceed 5.1%, indicating high precision. In an engineering example, the root mean square error of the improved modular vector method’s fitting result is 64.31 mm, constituting a mere 1.79% of the maximum subsidence value. This performance surpasses that of the genetic algorithm (70.47 mm), particle swarm algorithm (72.82 mm), and simulated annealing algorithm (75.45 mm). Notably, the improved modular vector method exhibits superior stability and reduced reliance on the quantity of measured values compared to the three aforementioned algorithms. The inversion analysis of predicted parameters based on the improved modular vector method, coupled with the probability integral method, holds practical significance for enhancing the accuracy of mining subsidence prediction.

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“…The subsidence value at any point within a surface subsidence movement basin induced by mining subsidence can be represented as follows [32,33]:…”

Section: Prediction Principles Of the Probability Integral Methodsmentioning

confidence: 99%

Research on Mining Subsidence Prediction Parameter Inversion Based on Improved Modular Vector Method

Chai,

Xu,

Guan

et al. 2023

Applied Sciences

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“…It has been used for the detection of unrelated features. In 2022, Gu et al [35] established an object-oriented probability integration prediction model framework. They introduced an object-oriented method coupled with the traditional probability integration method and then created the framework.…”

Section: Literature Reviewmentioning

confidence: 99%

K-means based quality prediction of object-oriented software using LR-ACO

2022

IJACR

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A quality prediction mechanism has been developed in this paper. K-means clustering algorithm has been applied for the clustering of object-oriented features. Finally logistic regression (LR) and ant colony optimization (ACO) (LR-ACO) have been used for the classification. The object-oriented parameters have been considered like polymorphism, encapsulation, abstraction, inheritance and other object-oriented features for experimentation. The purpose of these features to categorize the data in different class levels based on memory usage, reusability and multiple forms. Different hyperparameters like dynamic allocation and feature margin have also been considered for the classification thresholds. Different performance measures have been considered for the experimentation and the results shows the approach effectiveness through different exploration.

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“…The model was developed on the basis of the finite element method (FEM). Studies of mining subsidence prediction have achieved many results, and each method has its own advantages and disadvantages [8]. Artificial intelligence, machine learning and other methods are mostly good for extracting and analyzing the absence of relationship between the data labels and features from a large amount of data.…”

Section: Introductionmentioning

confidence: 99%

Method of Predictive Calculation of the Earth’s Surface Subsidence for Protecting Railways Against Underworking

Kozhas¹

2023

GEOMATE

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Underground development of mineral deposits causes deformations of the earth's surface in the form of a displacement trough, which negatively affects buildings and structures undermined. Knowing the parameters of the displacement trough subsidence curve at the design or operation stage of a construction site will prevent the dangerous impact of underground work and reduce the cost of repair and restoration work. A method of predictive calculation of earth's surface subsidence based on the construction of the boundaries of the stress-strain state of rocks zones using curved slip lines is proposed. Curved slip lines are built on the basis of the equation of damped oscillations and take into account the geological and mining conditions in the field under consideration. The predictive method of subsidence of the earth's surface makes it possible to predict and calculate the values of subsidence of the earth's surface at the design stage of underground mining and build a shear trough profile based on them. Based on the predictive method for calculating the parameters of the displacement trough, a method of protecting railways by arranging an embankment at the design stage is proposed. This technique will make it possible to take the needed mining and constructive measures in advance to protect against the harmful effects of underground mining in relation to protected objects without disrupting their operation.

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Research on the Prediction Model of Mine Subsidence Based on Object-Oriented and Probability Integration Method

Cited by 5 publications

References 33 publications

Research on Mining Subsidence Prediction Parameter Inversion Based on Improved Modular Vector Method

Research on Mining Subsidence Prediction Parameter Inversion Based on Improved Modular Vector Method

K-means based quality prediction of object-oriented software using LR-ACO

Method of Predictive Calculation of the Earth’s Surface Subsidence for Protecting Railways Against Underworking

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