A5022639014- Mineral Processing and Grinding
- Image Processing and 3D Reconstruction
- Granular flow and fluidized beds
- Construction Engineering and Safety
- Structural Engineering and Vibration Analysis
- Geophysical Methods and Applications
- Geotechnical and construction materials studies
- Rock Mechanics and Modeling
Kyushu University
2024
Jomo Kenyatta University of Agriculture and Technology
2024
This paper presents an alternative methodology for the study of flyrock hazards in mining, utilizing Artificial Intelligence (AI) through machine learning by classification. By using distance as a delineator to denote consequences blast, models generated two classes blasts: safe and unsafe. In this study, statistical best suited classification, that is, K Nearest Neighbors (KNN), Support Vector Machine (SVM), Decision Tree (DT), Neural Networks (ANNs), were used, their classification...
Accurate estimation of the mean fragment size is crucial for optimizing open-pit mining operations. This study presents an approach that combines clustering, hyperparameter optimization, and data augmentation to enhance prediction accuracy using Xtreme Gradient Boosting (XGBoost) regression model. A dataset 110 blasts was divided into 97 training testing, whereas a separate set 13 new, unseen used evaluate robustness generalization Hierarchical Agglomerative (HA) K-means clustering...
This study investigates physical and mechanical characteristics of the Pleistocene coral limestone Kenya's coastal plain by laboratory experiments based on ASTM standards.The have done include uniaxial compression test, indirect tensile ultrasonic pulse velocity (UPV) saturation porosity for direct shear test.Engineering properties brittleness, Schmidt's rebound number, fracture index drillability are calculated from empirical equations strength compressive available in published...