Predicting Sandstone Brittleness under Varying Water Conditions Using Infrared Radiation and Computational Techniques Article Swipe
Naseer Muhammad Khan
,
Liqiang Ma
,
Muhammad Zaka Emad
,
Tariq Feroze
,
Qiangqiang Gao
,
Saad S. Alarifi
,
Li Sun
,
Sajjad Hussain
,
Hui Wang
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/w16010143
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/w16010143
The brittleness index is one of the most integral parameters used in assessing rock bursts and catastrophic rock failures resulting from deep underground mining activities. Accurately predicting this parameter is crucial for effectively monitoring rock bursts, which can cause damage to miners and lead to the catastrophic failure of engineering structures. Therefore, developing a new brittleness index capable of effectively predicting rock bursts is essential for the safe and efficient execution of engineering projects. In this research study, a novel mathematical rock brittleness index is developed, utilizing factors such as crack initiation, crack damage, and peak stress for sandstones with varying water contents. Additionally, the brittleness index is compared with previous important brittleness indices (e.g., B1, B2, B3, and B4) predicted using infrared radiation (IR) characteristics, specifically the variance of infrared radiation temperature (VIRT), along with various artificial intelligent (AI) techniques such as k-nearest neighbor (KNN), extreme gradient boost (XGBoost), and random forest (RF), providing comprehensive insights for predicting rock bursts. The experimental and AI results revealed that: (1) crack initiation, elastic modulus, crack damage, and peak stress decrease with an increase in water content; (2) the brittleness indices such as B1, B3, and B4 show a positive linear exponential correlation, having a coefficient of determination of R2 = 0.88, while B2 shows a negative linear exponential correlation (R2 = 0.82) with water content. Furthermore, the proposed brittleness index shows a good linear correlation with B1, B3, and B4, with an R2 > 0.85, while it shows a poor negative linear correlation with B2, with an R2 = 0.61; (3) the RF model, developed for predicting the brittleness index, demonstrates superior performance when compared to other models, as indicated by the following performance parameters: R2 = 0.999, root mean square error (RMSE) = 0.383, mean square error (MSE) = 0.007, and mean absolute error (MAE) = 0.002. Consequently, RF stands as being recommended for accurate rock brittleness prediction. These research findings offer valuable insights and guidelines for effectively developing a brittleness index to assess the rock burst risks associated with rock engineering projects under water conditions.
Related Topics
Concepts
Brittleness
Exponential function
Index (typography)
Linear regression
Correlation coefficient
Linear correlation
Stress (linguistics)
Infrared
Materials science
Geotechnical engineering
Geology
Computer science
Composite material
Mathematics
Statistics
Physics
Optics
Philosophy
Linguistics
Mathematical analysis
World Wide Web
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/w16010143
- https://www.mdpi.com/2073-4441/16/1/143/pdf?version=1703861729
- OA Status
- gold
- Cited By
- 6
- References
- 83
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4390402953
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4390402953Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/w16010143Digital Object Identifier
- Title
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Predicting Sandstone Brittleness under Varying Water Conditions Using Infrared Radiation and Computational TechniquesWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-12-29Full publication date if available
- Authors
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Naseer Muhammad Khan, Liqiang Ma, Muhammad Zaka Emad, Tariq Feroze, Qiangqiang Gao, Saad S. Alarifi, Li Sun, Sajjad Hussain, Hui WangList of authors in order
- Landing page
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https://doi.org/10.3390/w16010143Publisher landing page
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https://www.mdpi.com/2073-4441/16/1/143/pdf?version=1703861729Direct link to full text PDF
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goldOpen access status per OpenAlex
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https://www.mdpi.com/2073-4441/16/1/143/pdf?version=1703861729Direct OA link when available
- Concepts
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Brittleness, Exponential function, Index (typography), Linear regression, Correlation coefficient, Linear correlation, Stress (linguistics), Infrared, Materials science, Geotechnical engineering, Geology, Computer science, Composite material, Mathematics, Statistics, Physics, Optics, Philosophy, Linguistics, Mathematical analysis, World Wide WebTop concepts (fields/topics) attached by OpenAlex
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6Total citation count in OpenAlex
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2025: 6Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| sustainable_development_goals[0].score | 0.49000000953674316 |
| sustainable_development_goals[0].display_name | Clean water and sanitation |
| citation_normalized_percentile.value | 0.76372452 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |