Aug 2026· IOP Conference Series: Earth and Environment· Vol 1656· 0 citations· 8 references
Physics
Abstract
Pavement condition assessment is essential for effective road network management, as paved roads deteriorate over time due to traffic loading and environmental effects. Traditional pavement surveys rely on in-situ measurements and visual inspections to identify surface distresses such as cracking, raveling, and weathering. Although widely used, these methods are often labour-intensive, time-consuming, costly, and may disrupt traffic while exposing inspectors to safety risks. Recent advances in unmanned aerial systems (UAS) provide a promising alternative for pavement condition assessment. UAV-based surveys enable rapid data collection over large areas using high-resolution imaging and sensor technologies, which can be integrated with artificial intelligence (AI) techniques for automated pavement distress detection and analysis. In Egypt, the rapid expansion of the road network and increasing maintenance demands highlight the need for an efficient, continuous, and reliable pavement monitoring system. This study presents an Egypt-focused framework that links UAV data acquisition, AI-based distress detection, and PCI-based decision-making to support the integration of UAV-based pavement inspection into existing road management practices. This study supports an Egypt-focused framework for integrating UAV-based pavement inspection into existing road management practices. The proposed framework outlines UAV data acquisition, AI-based distress detection, and pavement condition evaluation workflows, while considering local environmental, operational, and regulatory constraints. The framework is informed by successful international applications and is intended to enable safer, faster, and more cost-effective pavement assessment to support sustainable road network management in Egypt.
Effective inspection and maintenance of dams are essential for infrastructure longevity, water resource management, and public safety. Historically, dam maintenance relied heavily on visual inspections, which were limited in scope and accuracy, especially in detecting internal structural issues such as seepage and foundational degradation. This paper presents an industry-oriented evaluation of Unmanned Aerial Vehicles (UAVs) as a transformative tool in modern dam inspection and maintenance. The study explores UAVs' capacity to enhance efficiency, safety, and precision in identifying structural anomalies, erosion, and environmental degradation. Survey data collected from contractors in the Saudi water sector provides insight into current adoption levels, operational challenges, and perceived benefits. Return on investment (ROI) and Strength, weaknesses, opportunities, and threats (SWOT) analysis applied to evaluate and validate UAVs' potential to modernize dam maintenance practices, while highlighting technical, financial, and regulatory constraints. This research underscores UAVs' scalable utility and calls for further industry engagement to overcome adoption barriers and unlock their full potential in dam maintenance.
A. Alzahrani, Mohammed Sulaiman· University of Bisha Journal...· 0 citations
This study proposes an AI-enabled autonomous drone framework for infrastructure inspection that integrates intelligent flight planning, automated data collection, computer vision-based defect detection, and condition assessment that enhances inspection accuracy, operational safety, and scalability compared to traditional methods.
Yuki Nakamura· International Journal of Mod...· 0 citations
: This paper explores the applications of intelligent traffic inspection based on unmanned aerial vehicle (UAV) imaging. Addressing the issues of low efficiency, high safety risks, and limited coverage in traditional traffic inspection methods, it proposes an automated inspection solution that integrates UAV technology with image recognition technology. The study employs a DJI M300 RTK UAV equipped with visual sensors for data collection, utilizing the YOLO 11 algorithm to achieve efficient and precise recognition of vehicles and traffic anomalies. Imaging optimization of the visual sensors has been carried out to ensure image quality under varying weather and lighting conditions. Compared to its predecessors, the YOLO 11 algorithm demonstrates significant improvements in inference speed, recognition accuracy, occlusion tolerance, and model lightweightness, making it particularly suitable for small object recognition tasks in traffic inspection. Through the design of a standardized UAV traffic inspection process, full-chain automated processing is realized, covering video collection, quality verification, object detection and tracking, to result output and data archiving. Experimental results indicate that the proposed solution can effectively identify and track different types of vehicles in port traffic management scenarios, validating its accuracy and reliability in practical applications and providing a new solution for intelligent traffic inspection.
Xian-Guo Huang· Academic Journal of Engineer...· 0 citations
Repeated non-equidistant unmanned aerial vehicle (UAV) inspections of slope-road cracks require measurements from different distances, poses, and image scales to remain comparable and sufficiently precise for engineering-state decisions. Existing studies rarely integrate cross-view physical conversion, measurement uncertainty, and a project-defined minimum detectable change. We develop an engineering-detectability framework that defines cross-period criteria for crack width and displacement and derives equivalent widths for ideal, representative non-standard, and arbitrary viewpoints. First-order error propagation and reliability allocation convert the minimum detectable change into accuracy requirements for range, field of view, and normalized image coordinates. Crack-boundary coordinates and localization uncertainties provide a common interface for interchangeable detection and photogrammetric modules. Validation combines a controlled fixed-camera sequence with a close-range field-camera multiview test of seven physical openings under local coplanarity. All six determinate stages in the controlled sequence agreed with the digital image correlation (DIC) comparison, while one borderline stage required review. Across the seven openings, the four-view means gave a mean absolute error (MAE) of 0.196 mm and a root mean square error (RMSE) of 0.270 mm, with cross-view coefficients of variation (CVs) of 0.33–4.93%. An illustrative error budget demonstrates reverse screening of system configurations from project thresholds. The framework therefore connects viewpoint-equivalent measurements, uncertainty constraints, and engineering-state decisions in an auditable chain.
In the aftermath of natural disasters, roadway obstructions can hinder access to impacted communities, which can severely impact emergency response and evacuation efforts. Traditional ground-based and aerial reconnaissance methods for obstruction detection are often limited by cost, accessibility, and efficiency. This study introduces a novel framework that compares postdisaster unmanned aerial vehicle (UAV) images with predisaster satellite images to detect and segment roadway obstructions and estimate remaining accessible road width to provide emergency managers with updated status of roadway networks. The approach uses the You Only Look Once version 8 (YOLOv8) algorithm to segment aerial roadways, and then these are compared with predisaster reference images at the same location to identify changes in road conditions, notably reducing false positive results and enhancing detection accuracy. Due to a dearth of availability in training data for UAV-based aerial images of obstructions on roadways, synthetic data are generated through data augmentation techniques to bolster model performance. The developed framework achieved a mean average precision (mAP) of 98.5% (mAP 50), which evaluates detection accuracy at an Intersection Over Union (IoU) threshold of 0.5, and 91.2% (mAP 50–95). Results demonstrated improved prediction accuracy with reference images, achieving a 94.67% success rate compared with 48% without them. The methodology enables precise estimation of road usability for various vehicle types, facilitating efficient route planning and debris clearance. This research advances postdisaster roadway assessment by leveraging UAV and photogrammetry techniques, offering a rapid and accurate solution for postdisaster management, and planning for recovery operations.
Chonnapat Opanasopit, Joseph Louis· Journal of computing in civi...· 0 citations
The Sabha–Ash-Shuwayrif Highway is one of Libya's most strategically important transportation corridors, providing the principal connection between the southern region, the capital Tripoli, and international border crossings. Continuous exposure to heavy freight traffic, extreme thermal fluctuations, and localized moisture intrusion has accelerated asphalt pavement deterioration, resulting in frequent pothole formation, increased maintenance costs, and compromised road safety. Conventional reactive maintenance practices are insufficient for preserving pavement performance because interventions are typically performed only after severe structural damage has occurred. This study proposes an Artificial Intelligence (AI)-based predictive pavement management framework that integrates computer vision, deep learning, and predictive analytics to enable proactive pavement monitoring and maintenance. The proposed framework combines high-resolution pavement imagery, 3D laser profiling, Ground Penetrating Radar (GPR), traffic loading records, Pavement Condition Index (PCI), and environmental data to establish a comprehensive multi-modal dataset. A Convolutional Neural Network (CNN) is employed to automatically detect and classify early-stage pavement distresses, including micro-cracks, alligator cracking, and incipient potholes, while Focal Loss is incorporated to improve the detection of minority distress classes. A Long Short-Term Memory (LSTM) network models the temporal evolution of pavement deterioration using historical PCI, equivalent single axle loads, temperature variations, and precipitation data to forecast future pavement conditions. The predicted deterioration is integrated into an AI-driven risk assessment and decision-support system that prioritizes maintenance activities, recommends appropriate rehabilitation treatments, and optimizes intervention timing according to predicted distress severity. Furthermore, a continuous feedback mechanism updates the predictive models using newly acquired field observations, enabling adaptive learning and long-term performance improvement. The proposed framework is expected to enhance early pothole detection accuracy, reduce lifecycle maintenance costs, improve traffic safety, extend pavement service life, and support data-driven infrastructure management for the Sabha and Ash-Shuwayrif Highway and other strategic transport corridors operating under similar environmental and traffic conditions.
Hana Farhat, Mohammed Hamad, Llahm Omar et al.· Al-Farooq Journal of Science...· 1 citation
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