Civil engineering is a field of knowledge in direct contact with the citizen, not only in the design and construction of infrastructure but also in its maintenance, conservation, monitoring, and management. The integration of new technologies, such as drones, is revolutionizing work methodologies, offering new possibilities for the execution and management of infrastructure and minimizing human intervention in these jobs, with the increase in occupational safety and cost reduction that this entails. This study presents a comprehensive review of the literature on UAV applications for the monitoring and management of civil infrastructure. The applicability of UAVs and their connection with the main existing sensors and technologies are analyzed, such as visible cameras (RGB), multispectral cameras, and hyperspectral cameras, in the most relevant areas of civil engineering, such as building inspection, bridge inspection, dams, power line inspection, photovoltaic plants, inspection, hydrological studies road inspection, slope supervision, and the maintenance and monitoring of landfill operation. The impact and scope of these technologies are addressed, as well as the benefits in terms of process automation, efficiency, safety, and cost reduction. The incorporation of drones promises to significantly transform the practice of civil engineering, improving the sustainability and resilience of infrastructures.

Predictive maintenance of semi-autogenous grinding (SAG) mills reduces unplanned downtime and improves throughput. This study develops a data-driven prognostic model for production SAG mill using four years of operational data (temperature, voltage, current, motor speed, etc.). We follow a MATLAB (R2025a)-based prognostics and health management (PHM) workflow: data cleaning and synchronization; feature engineering in time and frequency domains (statistical moments, spectral power, bandwidth); normalization and clustering to separate operating regimes; and labeling of run-to-failure sequences for a recurring electrical failure mode. A health indicator is derived by scoring candidate features for monotonicity, trendability, and prognosability and fusing them into a condition index. Using MATLAB Predictive Maintenance Toolbox, we train and validate multiple Remaining Useful Life (RUL) learners including similarity-based, regression, and survival models on run-to-failure histories, selecting the best via cross-validated error and prediction stability. On held-out sets, the selected model forecasts RUL consistent with observed failure dates, providing actionable lead time for maintenance planning. The results highlight the practicality of deploying a PHM pipeline for SAG mills using existing plant data and commercial toolchains.