Lithological Control on the Estimation of Uniaxial Compressive Strength by the P-Wave Velocity Using Supervised and Unsupervised Learning

Rahman, Tabish; Sarkar, Kripamoy

doi:10.1007/s00603-021-02445-8

Cited by 29 publications

(6 citation statements)

References 35 publications

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“…The uniaxial compressive strength test is conducted to obtain the stress strain behavior of the tested rock samples. The testing can also indicate the quantitative value in determining the strength of the rock [18,19,20]. Overall result is summarized in Table 1.…”

Section: Uniaxial Compressive Strength Test Resultsmentioning

confidence: 99%

“…The lowest reading is found at S14 which it is classify as grade IV. Grade IV is considered as weak rock in the rock mass classification system [17,18].…”

Section: Pundit Test Resultsmentioning

confidence: 99%

“…From the figure, it is shows that when the velocity of P-wave increased automatically the strength also increased. It means that the faster velocity could grow as the strength of sandstone increased [18,21]. The data that plotted on the graph shows that velocity of P-wave for sandstones ranging from 1000 m/s to 4000 m/s.…”

Section: Correlation Of Velocity Of P-wave and Uniaxial Compressive S...mentioning

confidence: 94%

“…Additionally, it provides a variable rate of loading with data that is accurate to two decimal places. Uniaxial Compression Strength is the most extensively used method for determining strength since it is the most standardised [10,17,18,19]. The two loading faces of machine are parallel to each other.…”

Section: Pundit Testmentioning

confidence: 99%

“…Based on the laboratory work, the sample of sandstone that is found with different hardness although the samples were collected from the Kenny's Hill Formation area. The samples hardness grade is consisting of slightly, moderately and highly decomposed [16,18,21].…”

Section: Correlation Of Laboratory Index Testmentioning

confidence: 99%

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Characterization of Tropical Kenny Hill Weathered Sandstone using Non-Disruptive Testing of Pundit and Resistivity Testing

Hamzah¹,

Yusof²

2022

JMechE

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Rock characterization of tropical Kenny Hill weathered sandstone have been an effort to be explored by using non-disruptive testing of pundit and resistivity testing to undertake sustainability issues and providing technical approach in exploration of weak rock materials solving. The purpose of this research is to determine the relationship between resistivity and uniaxial compressive strength of the most problematic rock type in Malaysia, namely Kenny Hill weathered sedimentary rocks, more usually referred to as sandstone. According to earlier study, sandstone has degraded in quality and has a tendency to disintegrate due to the tropical climate. Sandstone has a higher strength feature as compared to shale from Bandar Nusa Rhu, Shah Alam. Series of uniaxial compression were carried out to obtain the strength for rock samples. The strength properties of weathered sandstone are studied to quantify the weathering impact to these materials. Resistivity test and Pundit test were laboratory method conducted on the rock samples for verification of the correlation between resistivity and uniaxial compression. Results from the test were analyzed and their relationship with weathering grade was established. It signifies that sandstone strength increasing with the weathering grade.

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Section: Uniaxial Compressive Strength Test Resultsmentioning

confidence: 99%

“…The lowest reading is found at S14 which it is classify as grade IV. Grade IV is considered as weak rock in the rock mass classification system [17,18].…”

Section: Pundit Test Resultsmentioning

confidence: 99%

Section: Correlation Of Velocity Of P-wave and Uniaxial Compressive S...mentioning

confidence: 94%

Section: Pundit Testmentioning

confidence: 99%

Section: Correlation Of Laboratory Index Testmentioning

confidence: 99%

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Characterization of Tropical Kenny Hill Weathered Sandstone using Non-Disruptive Testing of Pundit and Resistivity Testing

Hamzah¹,

Yusof²

2022

JMechE

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A systematic review and meta-analysis of artificial neural network, machine learning, deep learning, and ensemble learning approaches in field of geotechnical engineering

Yaghoubi,

Khamees

et al. 2024

Neural Comput & Applic

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Artificial neural networks (ANN), machine learning (ML), deep learning (DL), and ensemble learning (EL) are four outstanding approaches that enable algorithms to extract information from data and make predictions or decisions autonomously without the need for direct instructions. ANN, ML, DL, and EL models have found extensive application in predicting geotechnical and geoenvironmental parameters. This research aims to provide a comprehensive assessment of the applications of ANN, ML, DL, and EL in addressing forecasting within the field related to geotechnical engineering, including soil mechanics, foundation engineering, rock mechanics, environmental geotechnics, and transportation geotechnics. Previous studies have not collectively examined all four algorithms—ANN, ML, DL, and EL—and have not explored their advantages and disadvantages in the field of geotechnical engineering. This research aims to categorize and address this gap in the existing literature systematically. An extensive dataset of relevant research studies was gathered from the Web of Science and subjected to an analysis based on their approach, primary focus and objectives, year of publication, geographical distribution, and results. Additionally, this study included a co-occurrence keyword analysis that covered ANN, ML, DL, and EL techniques, systematic reviews, geotechnical engineering, and review articles that the data, sourced from the Scopus database through the Elsevier Journal, were then visualized using VOS Viewer for further examination. The results demonstrated that ANN is widely utilized despite the proven potential of ML, DL, and EL methods in geotechnical engineering due to the need for real-world laboratory data that civil and geotechnical engineers often encounter. However, when it comes to predicting behavior in geotechnical scenarios, EL techniques outperform all three other methods. Additionally, the techniques discussed here assist geotechnical engineering in understanding the benefits and disadvantages of ANN, ML, DL, and EL within the geo techniques area. This understanding enables geotechnical practitioners to select the most suitable techniques for creating a certainty and resilient ecosystem.

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Modeling and Prediction of Temporal Biogeomechanical Properties Using Novel Machine Learning Approach

Kolawole

Assaad

2023

Rock Mech Rock Eng

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Lithological Control on the Estimation of Uniaxial Compressive Strength by the P-Wave Velocity Using Supervised and Unsupervised Learning

Cited by 29 publications

References 35 publications

Characterization of Tropical Kenny Hill Weathered Sandstone using Non-Disruptive Testing of Pundit and Resistivity Testing

Characterization of Tropical Kenny Hill Weathered Sandstone using Non-Disruptive Testing of Pundit and Resistivity Testing

A systematic review and meta-analysis of artificial neural network, machine learning, deep learning, and ensemble learning approaches in field of geotechnical engineering

Modeling and Prediction of Temporal Biogeomechanical Properties Using Novel Machine Learning Approach

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