A New Predictive Model for Rock Strength Parameters Utilizing GEP Method

Behnia, Danial; Behnia, Mazda; Shahriar, K.; Goshtasbi, Kamran

doi:10.1016/j.proeng.2017.05.222

Cited by 19 publications

(6 citation statements)

References 13 publications

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“…As far as the modulus of elasticity (E t ) is concerned, the descending order of materials with respect to the mean E t value is quartz, marble, quartzite and metasandstone (Table 4); this indicates, as expected, that quartz is the stiffest material and thus more resistant to deformation. Similar UCS values have been reported in other studies for quartz [57], quartzite [58], marble and sandstone [59]. The results indicate that breakage follows a first order law i.e., the breakage rate S i is independent of time and the S i values can be determined from the slope of the straight lines.…”

Section: Uniaxial Compressive Strength and Modulus Of Elasticitysupporting

confidence: 89%

Correlation between Material Properties and Breakage Rate Parameters Determined from Grinding Tests

Petrakis

Komnitsas

2018

Applied Sciences

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Abstract:The present study investigates four materials, namely quartz, marble, quartzite and metasandstone and aims to establish correlations, with the use of simple and multiple regression analysis, between their properties and breakage rate parameters. The material properties considered in this study derived from the application of destructive and non-destructive tests and include P-wave velocity (V p ), Schmidt rebound value (R L ), uniaxial compressive strength (UCS) and tangent modulus of elasticity (E t ), while the breakage rate parameters determined from batch grinding tests, include breakage rate S i , maximum breakage rate S m , α T and α, and optimum particle size x m . The results indicate that the properties of all materials examined show very good correlation and can be used to predict S i or α T . Furthermore, parameter α is well correlated with V p , R L and E t using inverse exponential functions, while S m is strongly correlated with R L and UCS. Overall, it is deduced that multiple regression analysis involving two independent variables is a reliable approach and can be used to identify correlations between properties and breakage rate parameters for quartz, quartzite and metasandstone, which are silica rich materials. The only exception shown is the determination of x m for marble.

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Section: Uniaxial Compressive Strength and Modulus Of Elasticitysupporting

confidence: 89%

Correlation between Material Properties and Breakage Rate Parameters Determined from Grinding Tests

Petrakis

Komnitsas

2018

Applied Sciences

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“…The Gene expression programming algorithm was first introduced by Ferreira in 1999 (Behnia et al, 2017). This algorithm is an enhancement of the genetic algorithm, widely employed across various scientific disciplines, particularly in engineering (Jahed et al, 2017).…”

Section: Gene Expression Programming (Gep)mentioning

confidence: 99%

Land subsidence prediction in coal mining using machine learning models and optimization techniques

Jahanmiri,

Noorian-Bidgoli

2024

Environ Sci Pollut Res

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Land surface subsidence is an environmental hazard resulting from the extraction of underground resources. In underground mining, when mineral materials are extracted deep within the ground, the emptying or caving of the mined spaces leads to vertical displacement of the ground, known as subsidence. This subsidence can extend to the surface as troughs subsidence, as the movement and deformation of the hanging-wall rocks of the mining stope propagate upwards. Accurately predicting subsidence is crucial for estimating damage and protecting surface buildings and structures in mining areas. Therefore, developing a model that considers all relevant parameters for subsidence estimation is essential. In this article, we discuss the prediction of land subsidence caused by the caving of a stop's roof, focusing on coal mining using the longwall method. We consider a total of 11 parameters related to coal mining, including mining thickness and depth (related to the deposit), as well as density, cohesion, internal friction angle, elasticity modulus, bulk modulus, shear modulus, Poisson's ratio, uniaxial compressive strength, and tensile strength (related to the overburden). We utilize information collected from 14 coal mines regarding mining and subsidence to achieve this. We then explore the prediction of subsidence caused by mining using the gene expression programming (GEP) algorithm, optimized through a combination of the artificial bee colony (ABC) and ant lion optimizer (ALO) algorithms. Modeling results demonstrate that combining the GEP algorithm with optimization based on the ABC algorithm yields the best subsidence prediction, achieving a correlation coefficient of 0.96. Furthermore, sensitivity analysis reveals that mining depth and density have the greatest and least effects, respectively, on land surface subsidence resulting from coal mining using the longwall method.

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“…Regression analysis also generated poorerfitting models, when compared with machine learning (ML) approaches, for example, Artificial Neural Network (ANN), Genetic Algorithm (GA), Genetic Programming (GP), Fuzzy logic and Neuro-fuzzy systems, as proved by many researchers [5,6]. ML approaches have become an attractive research topic, with significant progress and reliability in engineering parameters prediction [9][10][11][12]. In this paper, GP has been used to develop relationships between Hydraulic Conductivity and rock mass classifications.…”

Section: Introductionmentioning

confidence: 99%

Estimating the Hydraulic Conductivity of jointed rock mass using Genetic Programming

Doan

Karakus

Nguyen

et al. 2023

IOP Conf. Ser.: Earth Environ. Sci.

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Ground engineering through injection of cementitious grouts or polymer resins is an integral part of fractured rock mass stabilisation by improving its maximum load carrying capacity, stiffness, cohesiveness and reduction in permeability. To optimise product delivery and achieve a high penetrability of product into the rock mass, it is imperative to understand the permeability characteristics or hydraulic conductivity of the rock mass. The hydraulic conductivity of the rock mass also dictates the type of polyurethane (PU) or cement-based products to be injected. For this purpose, a Packer test has to be conducted to measure the hydraulic conductivity of a rock formation which is an expensive and time-consuming process. Alternatively, a number of empirical methods that use rock mass classifications and rock joint properties Rock Quality Designation (RQD), Q-system (Q), Geological Strength Index (GSI), Joint Spacing (JS), Joint Aperture (a) proposed by a number of researchers that are capable of predicting hydraulic conductivity (HC). In this research, a predictive model between rock mass properties and HC is proposed using new approach - genetic programming (GP). For this purpose, a database of rock mass parameters including RQD, Q, GSI, JS, Joint Aperture (a), Second Permeability Index (SPI) and Packer test results available in the literature is established. The database is split into randomly selected training and testing sets. To assess the fitting quality, the sum of the absolute difference is used, while maximum depth on trees is set to control the bloat of the model. The performance is assessed with four statistical criteria and three GP models using different input combinations are proposed. These models have been converted into simple mathematical equations to calculate HC based on collected input data. In summary, two out of three models have successfully predicted HC with high correlation to the actual HC (R2 of testing sets ≈ 0.92). Therefore, this study has shown the feasibility of applying GP models into future prediction of HC for the initial phase of rock grouting design.

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A New Predictive Model for Rock Strength Parameters Utilizing GEP Method

Cited by 19 publications

References 13 publications

Correlation between Material Properties and Breakage Rate Parameters Determined from Grinding Tests

Correlation between Material Properties and Breakage Rate Parameters Determined from Grinding Tests

Land subsidence prediction in coal mining using machine learning models and optimization techniques

Estimating the Hydraulic Conductivity of jointed rock mass using Genetic Programming

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