Assessing excavatability in varied rockmass conditions using real-time data and machine learning technique
This study investigates shovel excavation performance across various rockmass conditions by integrating real-time performance assessments, rockmass property analysis, and machine learning techniques. Correlation analysis revealed significant positive relationships between Total Loading Time (TLT) an...
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| Format: | Article |
| Language: | English |
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Mehran University of Engineering and Technology
2025-01-01
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| Series: | Mehran University Research Journal of Engineering and Technology |
| Online Access: | https://publications.muet.edu.pk/index.php/muetrj/article/view/3408 |
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| author | Shafi Muhammad Pathan Abdul Ghani Pathan Muhammad Saad Memon |
| author_facet | Shafi Muhammad Pathan Abdul Ghani Pathan Muhammad Saad Memon |
| author_sort | Shafi Muhammad Pathan |
| collection | DOAJ |
| description | This study investigates shovel excavation performance across various rockmass conditions by integrating real-time performance assessments, rockmass property analysis, and machine learning techniques. Correlation analysis revealed significant positive relationships between Total Loading Time (TLT) and selected rock properties, specifically uniaxial compressive strength (UCS), tensile strength (TS) cohesion (C), and moisture content (M), while a negative correlation was observed with wet bulk density (WBD). Pareto analysis further highlighted C, UCS, and TS as the most impactful factors, cumulatively accounting for 56% of the total effect on excavation performance. A multiple linear regression model, using TLT as the dependent variable and significant rock properties (C, UCS, M) as predictors, achieved a strong correlation (R=0.76) and explained 76% of the variance, demonstrating the model’s effectiveness in estimating shovel performance. K-nearest neighbors (KNN) classification, optimized with a k-value of 7 and Manhattan distance, achieved a high accuracy of 99.43% in categorizing the excavation difficulty into four distinct classes. The frequency distribution of TLT data indicates that most materials in the pit fall under “Very Easy” and “Easy” classes, simplifying excavation processes. This research underscores the importance of the key rock properties in evaluating the excavation performance predictions and support optimized operational strategies in mining. Future work could expand on these findings by using additional machine learning techniques and exploring non-linear models to capture complex relationships. |
| format | Article |
| id | doaj-art-e9f15e80ad6448e8812cd3a859670074 |
| institution | Kabale University |
| issn | 0254-7821 2413-7219 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Mehran University of Engineering and Technology |
| record_format | Article |
| series | Mehran University Research Journal of Engineering and Technology |
| spelling | doaj-art-e9f15e80ad6448e8812cd3a8596700742025-01-03T05:23:58ZengMehran University of Engineering and TechnologyMehran University Research Journal of Engineering and Technology0254-78212413-72192025-01-0144117118310.22581/muet1982.34083408Assessing excavatability in varied rockmass conditions using real-time data and machine learning techniqueShafi Muhammad Pathan0Abdul Ghani Pathan1Muhammad Saad Memon2Department of Mining Engineering, Mehran University of Engineering and Technology, 76062, PakistanDepartment of Mining Engineering, Mehran University of Engineering and Technology, 76062, PakistanDepartment of Industrial Engineering and Management, Mehran University of Engineering and Technology, 76062, PakistanThis study investigates shovel excavation performance across various rockmass conditions by integrating real-time performance assessments, rockmass property analysis, and machine learning techniques. Correlation analysis revealed significant positive relationships between Total Loading Time (TLT) and selected rock properties, specifically uniaxial compressive strength (UCS), tensile strength (TS) cohesion (C), and moisture content (M), while a negative correlation was observed with wet bulk density (WBD). Pareto analysis further highlighted C, UCS, and TS as the most impactful factors, cumulatively accounting for 56% of the total effect on excavation performance. A multiple linear regression model, using TLT as the dependent variable and significant rock properties (C, UCS, M) as predictors, achieved a strong correlation (R=0.76) and explained 76% of the variance, demonstrating the model’s effectiveness in estimating shovel performance. K-nearest neighbors (KNN) classification, optimized with a k-value of 7 and Manhattan distance, achieved a high accuracy of 99.43% in categorizing the excavation difficulty into four distinct classes. The frequency distribution of TLT data indicates that most materials in the pit fall under “Very Easy” and “Easy” classes, simplifying excavation processes. This research underscores the importance of the key rock properties in evaluating the excavation performance predictions and support optimized operational strategies in mining. Future work could expand on these findings by using additional machine learning techniques and exploring non-linear models to capture complex relationships.https://publications.muet.edu.pk/index.php/muetrj/article/view/3408 |
| spellingShingle | Shafi Muhammad Pathan Abdul Ghani Pathan Muhammad Saad Memon Assessing excavatability in varied rockmass conditions using real-time data and machine learning technique Mehran University Research Journal of Engineering and Technology |
| title | Assessing excavatability in varied rockmass conditions using real-time data and machine learning technique |
| title_full | Assessing excavatability in varied rockmass conditions using real-time data and machine learning technique |
| title_fullStr | Assessing excavatability in varied rockmass conditions using real-time data and machine learning technique |
| title_full_unstemmed | Assessing excavatability in varied rockmass conditions using real-time data and machine learning technique |
| title_short | Assessing excavatability in varied rockmass conditions using real-time data and machine learning technique |
| title_sort | assessing excavatability in varied rockmass conditions using real time data and machine learning technique |
| url | https://publications.muet.edu.pk/index.php/muetrj/article/view/3408 |
| work_keys_str_mv | AT shafimuhammadpathan assessingexcavatabilityinvariedrockmassconditionsusingrealtimedataandmachinelearningtechnique AT abdulghanipathan assessingexcavatabilityinvariedrockmassconditionsusingrealtimedataandmachinelearningtechnique AT muhammadsaadmemon assessingexcavatabilityinvariedrockmassconditionsusingrealtimedataandmachinelearningtechnique |