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the claim
Standard geotechnical engineering values definitively classify rock strength, hardness, and toughness
the verdict
INSUFFICIENT LEANING
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the weight of evidence
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Geotechnical literature indicates that engineering fields use various testing standards and classification methods for specific mechanical properties like compressive strength and hardness, but the evidence only partially covers the claim and does not establish a single definitive standard that classifies rock strength, hardness, and toughness.

Evidence for · 2
2026 · cited by 0
To enable rapid, accurate grading of tunnel surrounding rock during construction, we propose a real-time grading method that integrates image processing with lightweight deep learning. We developed an automated pipeline that combines image-processing techniques and machine-learning algorithms to extract and classify characteristic parameters of tunnel surrounding rock, enabling real-time monitoring and classification at the tunnel palm surface. The study demonstrates that: (1) Following the proposed image-acquisition standards for rock and tunnel palm surfaces, images are converted to grayscale, denoised, enhanced, and normalized, which facilitates efficient and accurate extraction of structural features and improves the precision of classification parameters; (2) An optimized lithology identification and classification model was built, and a rock-hardness, strength, and integrity sensing approach based on the ShuffleNetV2 convolutional neural network was introduced to achieve real-time surrounding-rock grading. On an engineering site, the method attains 85% accuracy for lithology classification, 75% for rock-mass integrity, and 80% for overall surrounding-rock grade, confirming its feasibility and practical value. These results offer theoretical insight and engineering utility for the scientific evaluation of tunnel surrounding-rock grade. The study demonstrates that: (1) Following the proposed image-acquisition standards for rock and tunnel palm surfaces, images are converted to grayscale, denoised, enhanced, and normalized, which facilitates efficient and accurate extraction of structural features and improves the precision of classification parameters; (2) An optimized lithology identification and classification model was built, and a rock-hardness, strength, and integrity sensing approach based on the ShuffleNetV2 convolutional neural network was introduced to achieve real-time surrounding-rock grading. On an engineering site, the method attains 85% accuracy for lithology classification, 75% for rock-mass integrity, and 80% for overall surrounding-rock grade, confirming its feasibility and practical value. These results offer theoretical insight and engineering utility for the scientific evaluation of tunnel surrounding-rock grade. image processing machine learning ShuffleNetV2 surrounding rock classification tunnel engineering The author(s) declared that financial support was received for this work and/or its publication. Consequently, traditional construction methods can no longer satisfy current demands for quality and schedule. As the foundation for tunnel design and construction, surrounding rock classification strongly influences both construction quality and progress. Consequently, achieving dynamic classification of tunnel surrounding rock has become a central research focus in geotechnical engineering. Numerous scholars have investigated surrounding rock classification methods from both qualitative and Wu et al. (2020) proposed a stability classification model for surrounding rock in underground engineering, utilizing conceptual lattice and TOPSIS, which is based on five indicators: rock quality grade, saturated uniaxial compressive strength, integrity coefficient, longitudinal wave velocity, and fractal dimension. Tan et al. (2022) employed the discrete element method to simulate the rock-breaking process of a pneumatic rock drill and, in conjunction with field data, established a standard database for the dynamic classification of surrounding rock. Nevertheless, the above conventional classification approaches are constrained by protracted parameter acquisition and the subjective nature of pivotal indicators. Consequently, numerous scholars have adopted intelligent perimeter rock grading methods for their research, significantly reducing both time and economic costs while yielding substantial results. Li et al. (2018) and colleagues introduced a reliability analysis theory grounded in the national standard BQ method and employed the Monte Carlo method to classify surrounding rock based on evaluation indices such as rock toughness and integrity. Shi et al. Feature inference and parameter perception are performed, including lithology recognition using a lightweight CNN, fissure detection, geometric feature extraction, and determining the rock mass integrity coefficient. The final stage is surrounding rock grading, which includes inputting rock strength parameters, integrity quantification, BQ-based classification, and providing real-time grade output. 2.1 Rock image acquisition standards Rock hardness is primarily influenced by lithology, which can be identified through rock imaging. Consequently, rock images serve as a crucial characteristic parameter of the surrounding rock, significantly contributing to lithological classification. A ShuffleNet convolutional neural network model is developed to classify and recognize the properties of rock images. Additionally, image processing technology is utilized to analyze the palm surface images, enabling the extraction of structural characteristics and the identification of key feature parameters for enclosing rock grading. 3.1 Automatic perception of rock hardness In the BQ method for perimeter rock classification, rock hardness serves as a critical index. When classifying surrounding rock, it is essential to not only qualitatively assess the rock’s hardness but also to determine its specific uniaxial saturated compressive strength. The ShuffleNet V2 convolutional neural network, as previously discussed, can effectively classify the lithology of rock images. Subsequently, the geological investigation report of the particular tunnel allows for the determination of the weathering degree of the strata and the uniaxial saturated compressive strength of the corresponding rocks. Ultimately, based on Table 7 , a comprehensive classification of rock hardness is conducted. Table 7 Classification of rock hardness levels.
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More for · 1
2023 · cited by 0
The China-Pakistan Economic Corridor (CPEC) is an ongoing mega-construction project in Pakistan that necessitates further exploration of new natural resources of aggregate to facilitate the extensive construction. Therefore, the Late Permian strata of Chhidru and Wargal Limestone for aggregates resources were envisaged to evaluate their optimal way of construction usage through detailed geotechnical, geochemical, and petrographic analyses. Geotechnical analysis was performed under BS and ASTM standards with the help of employing different laboratory tests. A simple regression analysis was employed to ascertain mutual correlations between physical parameters. Based on the petrographic analysis, the Wargal Limestone is classified into mudstones and wackestone, and Chhidru Formation is categorized into wackestone and floatstone microfacies, both containing primary constituents of calcite and bioclasts. The geochemical analysis revealed that the Wargal Limestone and Chhidru Formation encompass calcium oxide (CaO) as the dominant mineral content. These analyses also depicted that the Wargal Limestone aggregates bear no vulnerability to alkali-aggregate reactions (AAR), whereas the Chhidru Formation tends to be susceptible to AAR and deleterious. Moreover, the coefficient of determination and strength characteristics, for instance, unconfined compressive strength and point load test were found inversely associated with bioclast concentrations and directly linked to calcite contents. Based on the geotechnical, petrographic, and geochemical analyses, the Wargal Limestone proved to be a significant potential source for both small and large-scale construction projects, such as CPEC, but the Chhidru Formation aggregates should be used with extra caution due to high silica content. It is important to analyze the petrography of aggregates in order to identify its texture, mineralogy, bioclasts, matrix type, microfractures, and texture type 9 . Some scholars have examined and made predictions about the engineering qualities of aggregates based on their petrographical and physical characteristics 6 , 10 . The geotechnical and rock engineering fields use various rock classification systems which are primarily based on mechanical parameters such as uniaxial compressive strength, Young's modulus, tensile strength, Poisson's ratio, and point load tests. Laboratory work comprised of several tests performed based on the standards set by the American Association of State Highway and Transportation Officials and they include point load tests (PLTs), universal compressive test, water absorption tests, aggregate porosity, specific gravity tests 37 , Los Angeles abrasion value (LAA) tests 38 , and flakiness and elongation tests following standard specifications (ASTM 39 ) along with the petrography. PLT tests were carried out according to the recommendations of the International Society of Rock Mechanics (ISRM 39 ) and core samples were extracted from bulk samples using a core drilling machine for unconfined compressive strength tests. The aggregates should be durable enough to sustain impacts without crumbling. Rocks resistant to granulation or disintegration will have a lower aggregate impact value 45 . The aggregate impact value was assessed by following the standard (BS-812) 45 by using Eq. ( 10 ). Figure 4 Geotechnical analysis of Late Permian Chhidru Formation, Western Salt Range. There is a direct relationship between specific gravity and the strength of aggregate 58 and water absorption is a direct indicator of permeability 59 . Rocks that comprise values greater than or equal to 2.55 of specific gravity are deemed acceptable for large building works 22 , 58 . Moreover, the minimum requirement for cement concrete is 2.60 (Naeem et al. 5 ). The values of specific gravity and water absorption of the Wargal Limestone and Chhidru Formation remain at 0.43 and 0.45%, and 2.63 and 2.59, respectively (Figs. 3 , 4 ). As per ASTM standards, the absorption capacity of these rocks is within the permissible level, i.e. 2%. In this research, the aggregate porosity values of the Wargal Limestone and Chhidru Formation are 1.74% and 1.91%, respectively (Figs. 3 , 4 ). According to Zada et al. 16 , limestone samples from both formations bear low porosity, yet they do impart negative impacts on the mechanical properties (UCS) of a rock. Abrasion value reflects the toughness of the aggregate under natural and stressed conditions 60 . The samples of the Wargal Limestone bear higher UCS values, i.e., greater than 95 MPa, therefore, samples of this formation can be categorized as solid rocks. The peak hardness value obtained in core samples of Wargal Limestone remains 99.2 MPa, and the lowest was 87.7 MPa, with an average value of 95.56 MPa (Figs. 3 , 4 ). Similarly, the samples of the Chhidru Formation also have higher UCS values, i.e., greater than 95 MPa, and they can also be categorized as strong/hard rocks. The characterizing attributes of the Wargal Limestone and Chhidru Formation include lower values of soundness, Los Angeles abrasion, aggregate impact, aggregate crushing, and water absorption due to lower amounts of bioclasts, microfractures (the presence of discontinuities like cracks, and stratification in rocks which reduce their strength). Moreover, both formations have higher specific gravity and lower aggregate porosity. Table 1 Comparison of geotechnical properties of current research work those of recent studies. The results obtained from the laboratory experiments of the studied rock units for evaluating their physical and mechanical properties were analyzed using simple statistical regressions. To determine whether or not they matched the criteria as a source of aggregate for the construction industry, the values of various physical characteristics were compared with the standards of BS and ASTM. The relationships between CaCO 3 , CaO, and LOI have manifested a robust positive relationship between
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  1. Real-time grading method of tunnel surrounding rock based on image recognition.peer-reviewedno side taken
  2. Exploring the potential of late permian aggregate resources for utilization in engineering structures through geotechnical, geochemical and petrographic analyses.peer-reviewedno side taken
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