A Comparison Among Some Non-linear Prediction Tools on Indirect Determination of Uniaxial Compressive Strength and Modulus of Elasticity of Basalt

Yesiloglu-Gultekin, N.; Gökçeoğlu, Candan

doi:10.1007/s10921-021-00841-2

Cited by 16 publications

(3 citation statements)

References 71 publications

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“…During the last decades, a significant number of soft computing models, namely ANN, ANFIS, back-propagation neural network (BPNN), FIS, radian basic function NN (RBFN), gene expression programming (GEP), extreme gradient boosting machine with firefly algorithm (XGBoost-FA), and SVM, have been reported for the prediction of UCS of rocks (Meulenkamp and Grima 1999;Gokceoglu and Zorlu 2004;Yılmaz and Yuksek 2008;Dehghan et al 2010;Monjezi et al 2012;Yagiz et al 2012;Mishra and Basu 2013;Yesiloglu-Gultekin et al 2013;Momeni et al 2015;Mohamad et al 2015;Torabi-Kaveh et al 2015;Teymen and Mengüç 2020;Barham et al 2020;Ceryan and Samui 2020;Mahmoodzadeh et al 2021;Yesiloglu-Gultekin and Gokceoglu 2022;Asteris et al 2021). The proposed models predict the UCS of various rock types and formation methods spanning the very soft to hard rock range.…”

Section: Soft Computing Proposalsmentioning

confidence: 99%

Closed-Form Equation for Estimating Unconfined Compressive Strength of Granite from Three Non-destructive Tests Using Soft Computing Models

et al. 2022

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The use of three artificial neural network (ANN)-based models for the prediction of unconfined compressive strength (UCS) of granite using three non-destructive test indicators, namely pulse velocity, Schmidt hammer rebound number, and effective porosity, has been investigated in this study. For this purpose, a sum of 274 datasets was compiled and used to train and validate three ANN models including ANN constructed using Levenberg–Marquardt algorithm (ANN-LM), a combination of ANN and particle swarm optimization (ANN-PSO), and a combination of ANN and imperialist competitive algorithm (ANN-ICA). The constructed ANN-LM model was proven to be the most accurate based on experimental findings. In the validation phase, the ANN-LM model has achieved the best predictive performance with R = 0.9607 and RMSE = 14.8272. Experimental results show that the developed ANN-LM outperforms a number of existing models available in the literature. Furthermore, a Graphical User Interface (GUI) has been developed which can be readily used to estimate the UCS of granite through the ANN-LM model. The developed GUI is made available as a supplementary material.

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Section: Soft Computing Proposalsmentioning

confidence: 99%

Closed-Form Equation for Estimating Unconfined Compressive Strength of Granite from Three Non-destructive Tests Using Soft Computing Models

et al. 2022

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“…Yu et al [6] performed triaxial compression tests on semi-through jointed mudstone samples at conditions of different confining pressures and inclination angles, based on which, a binary-medium constitutive model for semi-through jointed mudstone samples was proposed, and the model shows the effects of inclination angles and confining pressures on jointed mudstone deformation features and gives good predictions both qualitatively and quantitatively. Yesiloglu, Gultekin and Gokceoglu [7] developed various non-linear prediction models for unconfined compression strength and the initial elastic modulus by employing simple and non-destructive test results, in which a dataset that included 137 cases was analyzed and the non-linear multiple regression (NLMR), adaptive-neuro fuzzy inference system (ANFIS), and artificial neural networks (ANN) were utilized as non-linear prediction algorithms. Yu et al [8] developed a multi-field coupling experimental system for broken coal to study the effects of temperature on the evolution and distribution for the particle size of loose broken coal during the uniaxial confined compression process, which indicates that the particle gradation tended to be reasonably gradual, and, for a given stress, the particle breakage rate presented a slow increasing trend initially while accelerating later under different temperatures.…”

Section: Introductionmentioning

confidence: 99%

Mechanical Properties and Binary-Medium-Based Constitutive Model for Coral-Reef Limestone Samples Subjected to Uniaxial Loading

et al. 2022

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The mechanical and deformation mechanism of coral-reef limestone is vital for the stability and safety of infrastructures built on the ground composed of those materials. Upon loading, strain softening behavior of the coral-reef limestone occurs, usually due to the nonuniform cementation distribution, and the cementation within the samples may break up gradually and reduce the bearing capacity. In order to study their mechanical features, uniaxial compression tests were carried out in this study, and, based on the test results, a binary-medium-based constitutive model was formulated, in which, the sample was idealized, respectively, as the bonded and frictional elements. The bonded elements are composed of strong cementation and may gradually break up to transform into the frictional elements, both of which bear the external loading conjointly. In the new model, the parameters determination method was adopted based on uniaxial compression testing, and comparisons were made between the tested and computed results, which demonstrate that the model can reflect the salient features of coral-reef limestone samples, such as the strain softening and bonding breakage during the loading process. The study can provide a basis for the basic design of coral-reef limestone.

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“…Ultrasonic detection swiftly procures the original rock velocity, subsequently translatable into original rock elastic modulus parameters on-site (Yesiloglu-Gultekin and Gokceoglu 2021). By amalgamating laboratory rock mechanics experimental data, a methodology tailored for investigating the original rock elastic modulus and strength of coal-bearing strata post-grouting transformation is posited, employing artificial intelligence learning parameter conversion methodologies.…”

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confidence: 99%

Conversion, Prediction, and Application of Strength Parameters for Grouted Reconstructed Rock Mass

Zhang,

Liu,

et al. 2024

Preprint

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Ultrasonic detection has emerged as a rapid method for acquiring rock mass sound velocity and converting it into an elastic modulus parameter, a pivotal technique for investigating the in-situ mechanical properties of rock masses. Despite its significance, accurately deducing rock mass strength from elastic modulus remains a formidable challenge and a pressing issue in the realm of protorock parameter research. This study introduces an innovative artificial intelligence-driven methodology for transforming elastic modulus and strength parameters specific to coal measures through rigorous data analysis and experimental validation. By integrating two illustrative engineering cases, we explore the complexities of water inrush and floor heave issues encountered in tunnels traversing fault zones. The novel strength parameter calculation approach is benchmarked against previous studies, highlighting its superior advantages in terms of effectiveness and applicability. In essence, this research offers a comprehensive framework and practical workflow for translating in-situ acoustic parameter-derived elastic modulus into rock mass strength, serving as a valuable resource for future endeavors in mine water control research.

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A Comparison Among Some Non-linear Prediction Tools on Indirect Determination of Uniaxial Compressive Strength and Modulus of Elasticity of Basalt

Cited by 16 publications

References 71 publications

Closed-Form Equation for Estimating Unconfined Compressive Strength of Granite from Three Non-destructive Tests Using Soft Computing Models

Closed-Form Equation for Estimating Unconfined Compressive Strength of Granite from Three Non-destructive Tests Using Soft Computing Models

Mechanical Properties and Binary-Medium-Based Constitutive Model for Coral-Reef Limestone Samples Subjected to Uniaxial Loading

Conversion, Prediction, and Application of Strength Parameters for Grouted Reconstructed Rock Mass

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