Ramón Díaz-Noriega

The digitization and modelling of mining installations has been revolutionized by the introduction of UAV photogrammetry. However, due to the large size of mining operations and facilities, the high volume of images to be processed implies hardware requirements, computational power and time consumption, which complicates the rapid obtaining of results. Coverage Path Planning allows to define how the images are captured. Conventional approaches define automatic flights with cross-flight trajectories using high coverage values (over 80% frontlap and 70% sidelap) and consequently a high number of images, which increases processing time. To optimize the time of UAV photogrammetry, this research proposes the use of grid-based flight with reduced overlap values (less than 70% frontlap and 60% sidelap) at different heights. The case study proposed for an observation area of average size and moderate elevation difference using low-cost equipment employs a single commercial software package to generate 3D representations. The results show that it is possible to obtain around 48.5% and even 74.2% fewer images while maintaining the accuracy of the final model, with an increase in RMSE of 9 mm in the worst-case scenario. In terms of model information or point density, the optimized models show reductions in the amount of information of less than 5%, reaching 21.0% in the most unfavorable case considered. In addition, reductions in processing time of around 50% are achieved depending on the conditions, while reducing the computational demands to obtain results quickly through a rapid mapping mode for application in the mining field.

The increasing generation of mine tailings poses significant environmental challenges, but their reuse in construction materials offers a sustainable solution by reducing the demand for natural aggregates. To advance the use of tailings in construction, the thermal conductivity of mortar samples incorporating copper mine tailings as partial replacements (10% to 50%) for coarse aggregates was investigated. The thermal conductivity was measured using the transient line source method, revealing a progressive increase from 0.32 W/m·K (control sample) to 0.52 W/m·K (50% replacement sample). The statistical analysis (ANOVA) confirmed significant differences among the sample groups, with the tailings content being a key factor. The higher thermal conductivity is attributed to the quartz-rich composition of the tailings, which enhances the heat transfer compared to conventional aggregates. The findings of this study contribute to demonstrating the feasibility of using mortar with copper tailings to modify the thermal conductivity of mortar.

One crucial property of concrete, particularly in construction, is its thermal conductivity, which impacts heat transfer through conduction. For example, reducing the thermal conductivity of concrete can lead to energy savings in buildings. Various techniques exist for measuring the thermal conductivity of materials, but there is limited discussion in the literature about suitable methods for concrete. In this study, the transient line source method is employed to evaluate the thermal conductivity of concrete samples with natural and synthetic fibers after 7 and 28 days of curing. The results indicate that concrete with hemp fiber generally exhibits higher thermal conductivity values, increasing by 48% after 28 days of curing, while synthetic fibers have a minimal effect. In conclusion, this research opens the door to using natural alternatives like hemp fiber to improve concrete’s thermal properties, providing alternatives for thermo-active foundations and geothermal energy piles which require high thermal conductivities.