Using Indirect Testing Methods to Quickly Acquire the Rock Strength and Rock Mass Classification in Tunnel Engineering

Xue, Yiguo; Kong, Fanmeng; Li, Shucai; Zhang, Lewen; Zhou, Binghua; Li, Guangkun; Gong, Huimin

doi:10.1061/(asce)gm.1943-5622.0001633

Cited by 25 publications

(7 citation statements)

References 45 publications

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“…Rock samples demonstrate the presence of cracks and irregular fragmentation in varying sizes and shapes when subjected to uniaxial or triaxial loading and unloading during mechanical tests 12,13 . The failure characteristics exhibited by rock samples are intricately influenced by various factors, including the rock type as well as the specific loading and unloading stress paths applied.…”

Section: A Quantitative Methods For the Determination Of Rock Fragmen...mentioning

confidence: 99%

A quantitative method for the determination of rock fragmentation based on crack density and crack saturation

Xiao

Deng

et al. 2023

Sci Rep

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Rock mechanics tests are essential for advancing theoretical and practical knowledge in the field. The rock failure mechanism can be studied by analyzing the failure characteristics of rock samples through mechanical tests. However, despite their usefulness, quantitative rock classification systems still possess certain limitations that need to be addressed. The main objective of this paper was to develop a comprehensive quantitative rock classification system based on rock failure characteristics. The rock classification indices, including crack density and crack saturation, were systematically introduced based on rigorous statistical analyses conducted on a diverse set of 200 rock samples. In particular, the crack saturation index serves as a crucial metric that primarily captures and quantifies the extent of actual crack propagation within the rock samples. Moreover, it is important to note that the two evaluation indices, crack density and crack saturation, work in harmony and complement each other, enhancing the overall understanding of rock fragmentation and failure characteristics. By taking into account both crack density and crack saturation, the proposed method effectively categorizes rock fragmentation into five distinct classes, namely “relatively intact”, “slightly fragmented”, “fragmented”, “very fragmented” and “extremely fragmented.” The validation process confirmed the efficacy of the proposed classification method in accurately capturing the crack-propagation characteristics of rocks. This outcome is highly significant as it significantly advances ones understanding of rock failure mechanisms and provides valuable insights into the overall characteristics of rocks.

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Section: A Quantitative Methods For the Determination Of Rock Fragmen...mentioning

confidence: 99%

A quantitative method for the determination of rock fragmentation based on crack density and crack saturation

Xiao

Deng

et al. 2023

Sci Rep

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show abstract

“…It should be noted that, under both tensile and point-load testing conditions, the intact rock fails under compression induced tension (Russell andMuir Wood 2009, Masoumi et al 2018). Moreover, the importance of point load testing in estimating the UCS as well as in geo-mechanical classi cation of rock masses have been widely discussed by several researchers in the past (Bieniawski 1975;Yin et al 2017;Xue et al 2020). In this study, a total of 21 samples were tested under point load testing condition and 16 samples were tested under Brazilian testing condition.…”

Section: Introductionmentioning

confidence: 96%

“…Moreover, tensile stress zones frequently occur in the vicinity of rock masses and tensile failure has been recognized as the primary failure mode in several rock excavation projects such as in the construction of tunnels and mines (Xue et al 2020). Therefore, rock failure modes along the tensional regime are routinely considered as the key factor to characterize the damaging properties of rocks (Li et al 2013;Liu et al 2014).…”

Section: Introductionmentioning

confidence: 99%

An experimental approach to evaluate the role of rock failure modes in mechanical characterization of metabasalts

Bhowmick¹,

Ram²,

Mondal³

2021

Preprint

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A detailed understanding on rock failure modes testifies the competence of support designs for engineering work. Studying such failure modes using physical models of rock samples under laboratory conditions can be time efficient, informative and economically viable. In this study, the mechanical behavior of massive metabasalts were characterized with reference to rock failure modes through index tests such as point load and Brazilian testing conditions. Rock failure modes under point load test are categorized as i) single plane failure mode, ii) triple junction failure mode, iii) twisted failure mode and iv) single plane (inclined). It is observed that twisted and triple junction failure modes are observed for the samples with higher point load strength values (> 6 MPa). A total of five types of rock failure modes were observed in metabasalts under Brazilian testing conditions such as i) central multiple, ii) central, iii) non-central, iv) central multiple + layer activation and v) conjugate failure modes. Central multiple failure mode is found to be the most common failure mode in metabasalts due to its massive nature. It is also observed that all failure modes under Brazilian test are transpired in the range of 8 to 12 MPa. Among all other perceived rock failure modes, single plane (inclined) and conjugate failure modes under point load and Brazilian tests respectively are introduced in this study. It is evident that rock failure modes, which lead to control the strength of the rocks, should be considered as an important aspect for mechanical characterization of rock materials.

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“…For example, the classical single-index grading methods, such as the Protodyakonov coefficient f grading method, the tensile strength R t gradin g method, the compressive strength R c grading method 4 , Deer's RQD grading method 5 , and the elastic wave velocity Vp method 6 . In addition, there are grading methods with juxtaposition of indicators such as Chinese engineering rock grading standard (BQ method) 7 . With the development of systems engineering, the influence of multiple factors is considered in the assessment of rock quality.…”

Section: Introductionmentioning

confidence: 99%

Rock mass classification prediction model using heuristic algorithms and support vector machines: a case study of Chambishi copper mine

Zhou

et al. 2022

Sci Rep

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The rock mass is one of the key parameters in engineering design. Accurate rock mass classification is also essential to ensure operational safety. Over the past decades, various models have been proposed to evaluate and predict rock mass. Among these models, artificial intelligence (AI) based models are becoming more popular due to their outstanding prediction results and generalization ability for multiinfluential factors. In order to develop an easy-to-use rock mass classification model, support vector machine (SVM) techniques are adopted as the basic prediction tools, and three types of optimization algorithms, i.e., particle swarm optimization (PSO), genetic algorithm (GA) and grey wolf optimization (GWO), are implemented to improve the prediction classification and optimize the hyper-parameters. A database was assembled, consisting of 80 sets of real engineering data, involving four influencing factors. The three combined models are compared in accuracy, precision, recall, F1 value and computational time. The results reveal that among three models, the GWO-SVC-based model shows the best classification performance by training. The accuracy of training and testing sets of GWO-SVC are 90.6250% (58/64) and 93.7500% (15/16), respectively. For Grades I, II, III, IV and V, the precision value is 1, 0.93, 0.90, 0.92, 0.83, the recall value is 1, 1, 0.93, 0.73, 0.83, and the F1 value is 1, 0.96, 0.92, 0.81, 0.83, respectively. Sensitivity analysis is performed to understand the influence of input parameters on rock mass classification. It shows that the sensitive factor in rock mass quality is the RQD. Finally, the GWO-SVC is employed to assess the quality of rocks from the southeastern ore body of the Chambishi copper mine. Overall, the current study demonstrates the potential of using artificial intelligence methods in rock mass assessment, rendering far better results than the previous reports.

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Using Indirect Testing Methods to Quickly Acquire the Rock Strength and Rock Mass Classification in Tunnel Engineering

Cited by 25 publications

References 45 publications

A quantitative method for the determination of rock fragmentation based on crack density and crack saturation

A quantitative method for the determination of rock fragmentation based on crack density and crack saturation

An experimental approach to evaluate the role of rock failure modes in mechanical characterization of metabasalts

Rock mass classification prediction model using heuristic algorithms and support vector machines: a case study of Chambishi copper mine

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