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Fragmentation size distribution estimation is a critical process in mining operations that employ blasting. In this study, we aim to create a low-cost, efficient system for producing a scaled 3D model without the use of ground truth data, such as GCPs (Ground Control Points), for the purpose of improving fragmentation size distribution measurement using GNSS (Global Navigation Satellite System)-aided photogrammetry. However, the inherent error of GNSS data inhibits a straight-forward application in Structure-from-Motion (SfM). To overcome this, the study proposes that, by increasing the number of photos used in the SfM process, the scale error brought about by the GNSS error will proportionally decrease. Experiments indicated that constraining camera positions to locations, relative or otherwise, improved the accuracy of the generated 3D model. In further experiments, the results showed that the scale error decreased when more images from the same dataset were used. The proposed method is practical and easy to transport as it only requires a smartphone and, optionally, a separate camera. In conclusion, with some modifications to the workflow, technique, and equipment, a muckpile can be accurately recreated in scale in the digital world with the use of positional data.

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As the world's finite resources are getting depleted, technological innovation has become more indispensable to the mining industry for it to keep up with both rising demand and decreasing mineral abundance. Hence this study discusses the development of new resource development technology, termed "smart mining" by combining mining and Information and Communications Technology (ICT). With "smart mining", nearly all aspects of a mining operation can be improved, such include efficiency and safety. One of such applications of this concept is in open pit mines, where a communication system can be created using satellite technology. In underground mines however, where satellite signals cannot penetrate, optical fiber cables have been used for mine automation and monitoring. However, such physical connections are prone to wear and inevitable breakage in an underground environment. It is against this backdrop that a new monitoring system using Wireless Sensor Networks (WSNs) was then incorporated with the aim to subvert these issues. Several studies have used WSNs to create a monitoring system using various wireless communication technologies. This study therefore proposes a monitoring system that uses Wi-Fi ad hoc wireless communication. Evaluation of results from experiments that test the system's capabilities have shown that it is sufficient for underground mine monitoring applications. Moreover, this study believes that in addition to improving underground monitoring systems, Wi-Fi ad hoc wireless communication systems could also be used for other implementations of "smart mining".

In mining operations that employ explosives and mineral processing, one of the important factors for efficient and low-cost operation is the fragmentation size distribution of rock after it has been blasted. Automatic scaling is a critical component of fragmentation size distribution measurement as it will directly determine the accuracy of the size estimation. In this study, we propose a method to create a system for creating a scaled 3D CG model, without the use of ground truth data such as GCPs (Ground Control Points), for the purpose of improving fragmentation size distribution measurement using positional data such as GNSS (Global Navigation Satellite System)-aided photogrammetry. We confirmed the validation of the method through an experimental evaluation of actual muckpiles. The results showed evidence of improving the scaling aspect of 3D fragmentation measurement systems without using GCPs or manual scales, specifically in surface mines where GNSS data are available.