Prediction of Uniaxial Compressive Strength Using Fully Bayesian Gaussian Process Regression (fB-GPR) with Model Class Selection

Zhao, Tengyuan; Chen, Song; Lu, Shifeng; Xu, Liang

doi:10.1007/s00603-022-02964-y

Cited by 20 publications

(4 citation statements)

References 55 publications

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“…The most common method for measuring rock mass strength is the indoor test on the uniaxial compressive strength UCS 17 . The UCS is one of the key parameters of rock mechanics 18 and one of the most important geomechanical parameters for preliminary and final designs of civil engineering, geotechnical engineering, mining engineering, and underground engineering, i.e., dams, rock excavation, tunnels, slope stability, and infrastructure 19 , 20 , and is used for the evaluation of rock hardness 21 and grade classification. Since inaccurate UCS results would cause project budget overruns or even the collapse of related structures 22 , precise UCS calculation of the rock mass is of great significance.…”

Section: Introductionmentioning

confidence: 99%

“…The researchers have developed many empirical equations for predicting the UCS of rocks. Soft computing-based UCS prediction primarily, i.e., Bayesian analysis 20 , ANN 31 , 32 , fuzzy system 33 , 34 , regression trees 35 , 36 , genetic algorithm 23 , 37 , imperialist competitive algorithm 38 , and particle swarm optimization technique 39 . So, Soft computing techniques predict UCS more accurately than traditional statistical methods.…”

Section: Introductionmentioning

confidence: 99%

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Comparative study on convolutional neural network and regression analysis to evaluate uniaxial compressive strength of Sandy Dolomite

Wang,

Liu,

Liu

et al. 2024

Sci Rep

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Sandy Dolomite is a kind of widely distributed rock. The uniaxial compressive strength (UCS) of Sandy Dolomite is an important metric in the application in civil engineering, geotechnical engineering, and underground engineering. Direct measurement of UCS is costly, time-consuming, and even infeasible in some cases. To address this problem, we establish an indirect measuring method based on the convolutional neural network (CNN) and regression analysis (RA). The new method is straightforward and effective for UCS prediction, and has significant practical implications. To evaluate the performance of the new method, 158 dolomite samples of different sandification grades are collected for testing their UCS along and near the Yuxi section of the Central Yunnan Water Diversion (CYWD) Project in Yunnan Province, Southwest of China. Two regression equations with high correlation coefficients are established according to the RA results, to predict the UCS of Sandy Dolomites. Moreover, the minimum thickness of Sandy Dolomite was determined by the Schmidt hammer rebound test. Results show that CNN outperforms RA in terms of prediction the precision of Sandy Dolomite UCS. In addition, CNN can effectively deal with uncertainty in test results, making it one of the most effective tools for predicting the UCS of Sandy Dolomite.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Comparative study on convolutional neural network and regression analysis to evaluate uniaxial compressive strength of Sandy Dolomite

Wang,

Liu,

Liu

et al. 2024

Sci Rep

View full text Add to dashboard Cite

show abstract

“…The most common method for measuring rock mass strength is the indoor test on the uniaxial compressive strength UCS (Jeffery et al, 2021). The UCS is one of the key parameters of rock mechanics (Matin et al, 2018) and one of the most important geomechanical parameters for preliminary and final designs of civil engineering, geotechnical engineering, mining engineering, and underground engineering, i.e., dams, rock excavation, tunnels, slope stability, and infrastructure (Murlidhar et al, 2019;Zhao et al, 2022), and is used for the evaluation of rock hardness (Kong et al, 2021) and grade classification. Since inaccurate UCS results would cause project budget overruns or even the collapse of related structures (Ng et al, 2015), precise UCS calculation of the rock mass is of great significance.…”

mentioning

confidence: 99%

“…The researchers have developed many empirical equations for predicting the UCS of rocks. Soft computing-based UCS prediction primarily, i.e., Bayesian analysis (Zhao et al, 2022), ANN (Kahraman et al, 2006;Le et al, 2022), fuzzy system (Mishra and Basu, 2013;Parsajoo et al, 2022), regression trees (Ghasemi et al, 2018;Liang et al, 2016), genetic algorithm (Alemdag et al, 2016;Baykasoglu et al, 2008), imperialist competitive algorithm (Jahed , and particle swarm optimization technique (Momeni et al, 2015). So, Soft computing techniques predict UCS more accurately than traditional statistical methods.…”

mentioning

confidence: 99%

Comparative Study on Convolutional Neural Network and Regression Analysis to Evaluate Uniaxial Compressive Strength of Sandy Dolomite

Xu,

Wang,

Liu

et al. 2024

Preprint

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Sandy dolomite is a kind of widely distributed rock. The uniaxial compressive strength (UCS) of sandy dolomite is an important metric in the application in civil engineering, geotechnical engineering, and underground engineering. Direct measurement of UCS is costly, time-consuming, and even infeasible in some cases. To address this problem, we establish an indirect measuring method based on the convolutional neural network (CNN) and regression analysis (RA). The new method is straightforward and effective for UCS prediction, and has significant practical implications. To evaluate the performance of the new method, 158 dolomite samples of different sandification grades are collected for testing their UCS along and near the Yuxi section of the Central Yunnan Water Diversion (CYWD) Project in Yunnan Province, Southwest of China. Two regression equations with high correlation coefficients are established according to the RA results, to predict the UCS of sandy dolomites. Moreover, the minimum thickness of sandy dolomite was determined by the Schmidt hammer rebound test. Results show that CNN outperforms RA in terms of prediction the precision of sandy dolomite UCS. In addition, CNN can effectively deal with uncertainty in test results, making it one of the most effective tools for predicting the UCS of sandy dolomite.

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Probabilistic classification of surrounding rock mass of tunnels based on fully Bayesian Gaussian process classification (fB‐GPC) with consideration of influencing factors selection

Song,

Zhao,

2024

Geological Journal

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Rock mass classification is of great significance in guiding the design and construction of tunnels, which is often determined through single‐factor or multi‐factor classification methods based on some readily available rock indices, for example, dynamic elastic modulus, degree of tectonization, and so forth. Although these methods work reasonably well in some cases, they tend to lead to inaccurate classification results due to the pre‐selected main factor or function forms, especially for tunnels with complex geological conditions. Alternatively, the classification may be achieved with the aid of some machine learning (ML) methods, which are data‐driven, hence bypassing pre‐determination of the mathematical function forms of the main factor(s). Note although these ML methods perform well in the classification of surrounding rock mass, they generally employ all measurements of readily available rock indices as input variables, which might lead to excessive model complexity and a potential decrease in generalization performance. In this paper, a fully Bayesian Gaussian process classification (fB‐GPC) approach is proposed for probabilistic classification of surrounding rock mass, which is data‐driven and can select the optimal combination of main factors that affect rock mass classification. A real‐life example is used to illustrate and validate the proposed approach. Results show that the proposed fB‐GPC method performs very well in determination of the most important factor for classification of surrounding rock mass, with an area under the receiver operating characteristic curve (AUC) of around 0.98. A sensitivity study is also performed to systematically examine the accuracy and robustness of the proposed approach in this study.

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Prediction of Uniaxial Compressive Strength Using Fully Bayesian Gaussian Process Regression (fB-GPR) with Model Class Selection

Cited by 20 publications

References 55 publications

Comparative study on convolutional neural network and regression analysis to evaluate uniaxial compressive strength of Sandy Dolomite

Comparative study on convolutional neural network and regression analysis to evaluate uniaxial compressive strength of Sandy Dolomite

Comparative Study on Convolutional Neural Network and Regression Analysis to Evaluate Uniaxial Compressive Strength of Sandy Dolomite

Probabilistic classification of surrounding rock mass of tunnels based on fully Bayesian Gaussian process classification (fB‐GPC) with consideration of influencing factors selection

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