Unmanned Aerial Vehicles are increasingly used for urban photogrammetry, yet best-practice guidance on flight-planning parameters in dense city blocks remains limited. However, the quality of generated orthomosaics is strongly dependent on the proper configuration of flight parameters, highlighting the need for evidence-based guidance in consolidated urban environments. This study evaluated the impact of various flight configurations on orthomosaic visual quality and operational efficiency. A total of 96 automated flights were conducted over a 1.5-hectare urban area, systematically varying height, frontal overlap, lateral overlap, camera angle, and flight pattern. Orthomosaic photogrammetric reconstructions were generated and assessed using a multi-criteria scoring system based on the image processing time and the visual clarity of control targets. Results show that a flight height of 60 m, 70% frontal overlap, 80% lateral overlap, nadir $90°$ camera angle, and a grid flight pattern provide the best balance between image quality and operational efficiency. Lower heights improved visual detail but increased processing time, while excessive overlaps did not necessarily enhance final image quality.

Sustainability in the construction sector has become a fundamental objective for mitigating escalating environmental challenges; given that concrete is the most widely used man-made material, extending its service life is therefore critical. Among durability concerns, magnesium sulfate (MgSO₄) attack is particularly deleterious to concrete structures. Therefore, this study investigates the short- and long-term performance of concrete produced with copper slag (CS)—a massive waste generated by copper mining activities worldwide—employed as a supplementary cementitious material (SCM), together with recycled coarse aggregate (RCA), obtained from concrete construction and demolition waste, when exposed to MgSO₄. CS was used as a 15 vol% cement replacement, while RCA was incorporated at 0%, 20%, 50%, and 100 vol%. Compressive strength, bulk density, water absorption, and porosity were measured after water curing (7–388 days) and following immersion in a 5 wt.% MgSO₄ solution for 180 and 360 days. Microstructural characteristics were assessed using scanning electron microscopy (SEM), X-ray diffraction (XRD), thermogravimetric analysis with its differential thermogravimetric derivative (TG-DTG), and Fourier transform infrared spectroscopy (FTIR) techniques. The results indicated that replacing 15% cement with CS reduced 7-day strength by ≤10%, yet parity with the reference mix was reached at 90 days. Strength losses increased monotonically with RCA content. Under MgSO₄ exposure, all mixtures experienced an initial compressive strength gain during the short-term exposures (28–100 days), attributed to the pore-filling effect of expansive sulfate phases. However, at long-term exposure (180–360 days), a clear strength decline was observed, mainly due to internal cracking, brucite formation, and the transformation of C–S–H into non-cementitious M–S–H gel. Based on these findings, the combined use of CS and RCA at low replacement levels shows potential for producing environmentally friendly concrete with mechanical and durability performance comparable to those of concrete made entirely with virgin materials.

The identification and monitoring of pavement pathologies are critical for maintaining road infrastructure and ensuring transportation safety. As traditional inspection methods are often time-consuming, labor-intensive, and prone to human error, in recent years, Unmanned Aerial Vehicles (UAVs) have emerged as a promising tool for pavement condition assessment due to their mobility, efficiency, and ability to capture high-resolution imagery and multi-sensor data. This Systematic Literature Review aims to synthesize and evaluate existing research on the use of UAV for identifying pavement pathologies, such as cracks, potholes, rutting, and surface degradation. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) methodology, publications were screened and selected across major academic databases such as Scopus and Web of Science. A total of 361 relevant articles published from 2020 to July 2025 were identified and analyzed using bibliometric overview. And a full-text synthesis and qualitative analysis was performed on a subset of 108 studies, which met the quality assessment criteria. The review categorizes the UAV systems, computer vision approaches, pathology types, and pavement materials examined in the studies. The findings indicate a growing trend in the use of UAV and computer vision techniques for pavement pathology detection, along with evolving preferences for UAV platforms, analytical approaches, and targeted pathology categories over time. This review highlights current gaps and outlines future research directions to advance UAV-based pavement pathology identification as a viable and reliable alternative to conventional inspection methods.

Geosynthetic materials have been demonstrated to be an accurate element in civil engineering, specifically in the field of pavement. Regarding the implementation of these materials in asphalt mixture layers, geosynthetics, such as geotextile and geogrid, have been used to delay the crack propagation and/or increase the fatigue life. However, the use of these material is based on the experience learned in the field or results obtained from testing on a reduced scale in the laboratory. This research work aims at evaluating the influence that geogrids have as a reinforcement to asphalt mixture layer samples. Within this context, in this work, two types of large-scale samples (with and without reinforcement) are subjected to a monotonic load under two support conditions (simple support and granular base). The results were summarized with the load-line displacement diagram, where parameters such as the peak load, displacement, stiffness, and work of fracture were analyzed. The results reveal that the asphalt layer with geogrid experiences a double benefit since it withstands greater magnitudes of load and delays the appearance of rutting problems. To conclude, the geogrid as a reinforcement for asphalt mixture layers strongly impact their mechanical behavior, increasing the service life of the pavement structure.

The rapid deterioration of road infrastructure requires accurate and efficient methods for detecting pavement distresses. Unmanned Aerial Vehicles (UAVs) have emerged as a reliable alternative to conventional inspection techniques, enabling high-resolution data acquisition and improved operational safety. This study investigates the application of the Smart Oblique Capture (SOC) technique for pavement inspection through a systematic calibration of UAV flight parameters, including Ground Sample Distance (GSD), frontal and lateral overlap, camera tilt angle, and flight pattern. A structured experimental campaign was conducted, comprising 135 parameter combinations evaluated across three independent scenarios, resulting in a total of 405 UAV flights. The analysis focused on assessing the impact of these parameters on the visual quality of two-dimensional pavement reconstructions and processing efficiency. The results show that a configuration consisting of a 0.5 cm/pixel GSD, 70% frontal overlap, 80% lateral overlap, and a 70° camera tilt angle achieves the best balance between reconstruction quality and computational cost. Furthermore, the findings indicate that Smart Oblique Capture does not provide a statistically significant improvement in reconstruction quality for linear infrastructure compared to conventional oblique configurations, despite requiring a higher number of images and longer processing times. Overall, the results demonstrate that flight parameter calibration plays a more critical role than the adoption of advanced acquisition strategies such as Smart Oblique Capture. This study provides practical and reproducible guidelines for UAV-based pavement inspection, supporting efficient data acquisition while minimizing redundant information and unnecessary computational costs in infrastructure monitoring workflows.