Aug 2026· Applied Sciences· Vol 16, pp. 8367· 0 citations· 26 references
Abstract
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.
Accurate crack width measurement is essential for the condition assessment of reinforced concrete (RC) bridges, yet most unmanned aerial vehicle (UAV) inspections remain limited to pixel-level observations that cannot be converted into reliable metric units without an external scale reference. This paper presents a lightweight, drone-agnostic UAV-mounted metrology system that enables standards-aligned metric crack width measurement directly from inspection imagery. The payload integrates a focusable diffractive optical element (DOE) red laser that projects a cross pattern of known angular geometry, three TF-Luna time-of-flight (ToF) distance sensors, and an ESP-WROOM-32 microcontroller that provides dual-rate sampling, Bluetooth Low Energy (BLE) streaming, and on-board logging. A two-stage calibration links the synchronized distance measurements to the physical length of the projected cross, yielding an image-specific pixel-to-millimeter scale that is applied to pixel-level crack widths obtained from a vision-based segmentation pipeline. The system is field-deployed on the Puente Huamani Bridge in Pisco, Peru, where measurements of 39 cracks classified under AASHTO MBEI condition states are compared against independent manual measurements by six inspectors. The proposed system reduces measurement variability across all condition states (CS), lowering the average coefficient of variation from 0.36 to 0.10 for fine CS1 cracks, from 0.27 to 0.11 for CS2, and from 0.22 to 0.07 for CS3. Cross-platform adaptability is demonstrated through an additional deployment on a DJI Matrice 350 RTK at the Low Level Bridge in Edmonton, Canada. The results indicate that the system provides a practical, low-cost, and scalable solution for repeatable, standards-aligned UAV-based bridge crack assessment.
During the service life of bridges, early-stage defects such as cracks are inevitable. Among them, fine cracks with a width of approximately 0.2 mm are critical control indicators in structural safety assessment and durability evaluation, and bridge inspection codes explicitly require their detection and quantification. However, bridge towers and other tall components contain extensive areas that are difficult to access through manual inspection. Although non-contact inspection based on unmanned aerial vehicles (UAV) offers significant advantages in safety and efficiency, reliably identifying 0.2 mm cracks at practical inspection distances remains a key engineering challenge.
To address this issue, this study focuses on developing a data acquisition strategy for UAV inspection that is aligned with the 0.2 mm crack threshold specified in bridge inspection codes. Particular attention is given to the influence of imaging distance and illumination conditions on crack recognizability. An already damaged beam was selected as the experimental subject, and a DJI Matrice 4T aircraft was employed to collect crack images under systematically controlled distance conditions. The flight path was arranged perpendicular to the beam surface, with constant gimbal orientation and imaging angle to minimize pose-related variability.
Image acquisition started at 2.5 m from the target surface and increased in 0.5 m intervals, reaching a maximum distance of 45.0 m. A total of 280 crack images were collected under clear afternoon conditions. Target cracks with widths of approximately 0.2 mm were identified using a crack width comparison gauge. Pixel-level annotations were performed using an open-source image annotation tool, and crack recognition was evaluated through a semantic segmentation task implemented with a standard version of the You Only Look Once 11(YOLO11) model, without architectural modification. This choice was made to avoid dependence on model-specific enhancements and to better ensure the general applicability of the proposed data acquisition strategy across commonly used detection frameworks.
Illumination effects were preliminarily analyzed by applying synthetic brightness adjustments to the collected images. Under these controlled adjustments, the segmentation recall remained above 0.80 and the mean Intersection over Union (mIoU) remained above 0.65, suggesting limited sensitivity to moderate brightness variation. However, this conclusion is restricted to simulated brightness changes and does not account for complex real-world lighting factors such as shadows, glare, and non-uniform illumination.
The influence of imaging distance was systematically evaluated. Results indicate that 0.2 mm cracks can be reliably recognized when the imaging distance does not exceed 25.5 m. Based on camera imaging principles, the corresponding ground sampling distance was calculated to be approximately 1.29 mm per pixel. Given known camera parameters, the maximum permissible imaging distance for satisfying the 0.2 mm detection requirement can be inversely derived. The findings provide practical guidance for planning inspection flights and selecting appropriate imaging distances in bridge crack detection tasks subject to code-specified crack width limits.
Huijie Zheng, Wen Xiong, Yanjie Zhu et al.· e-Journal of Nondestructive...· 0 citations
The flatness of bearing pads directly affects structural load transfer safety. However, conventional total station-based inspection methods suffer from limited spatial sampling, low inspection efficiency, and high safety risks associated with working at height. To address these limitations, this paper proposes UAV-FIBP (Unmanned Aerial Vehicle-based Flatness Inspection for Bridge Pads), an automated and intelligent method for pad flatness assessment utilizing UAV-based 3D reconstruction. By designing a close-range, multi-orbit circumnavigational UAV flight path and acquiring high-overlap imagery (85% forward and 80% side overlap), a millimeter-accurate 3D model is generated via photogrammetry, achieving a high-density point cloud of ≥200 points/cm2 on the pad surface. Following point cloud denoising and region-of-interest segmentation using the Random Sample Consensus (RANSAC) algorithm, Principal Component Analysis (PCA) is employed to fit a reference plane. A dual-parameter evaluation framework is subsequently introduced: the root mean square (RMS) deviation quantifies local surface roughness, while the maximum elevation difference is derived from the angle between the normal vectors of the fitted plane and the horizontal plane, thereby enabling a comprehensive assessment of global inclination. Validation experiments conducted on laboratory-scale setups and real construction sites (involving four bridge pads) demonstrate that the proposed method achieves deviations ≤ 2 mm compared to total station measurements, satisfying the requirements stipulated in the Standards for Quality Inspection and Verification of Highways (JTG F80/1-2017). Results indicate that UAV-FIBP enables non-contact, full-coverage, and automated flatness inspection, significantly improving inspection efficiency and construction safety. This work establishes a scalable technical pathway for intelligent bridge construction.
Yuchi Xupan, Yu Ling, Hua Liu et al.· Buildings· 0 citations
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.
Abdel-Halem A. Abdel-hamed, Abdallah Samir Abdallah, Ibrahim Elnaml et al.· IOP Conference Series: Earth...· 0 citations
Spatiotemporal change detection of surface cracks in concrete structures is of great importance for evaluating and maintaining their structural health. The development of robotics and 3D computer vision technologies provides new solutions for key subtasks in this process, including automated data acquisition, spatial localization and quantification of cracks, and multi-temporal crack registration. This study proposes an automated UAV- and point cloud-based framework for detecting spatiotemporal changes in cracks in concrete structures. First, the proposed autonomous UAV path-planning algorithm is used to achieve data acquisition that conforms to complex structural geometries. Then, an improved SfM algorithm is employed to realize spatial crack localization and local point cloud densification. Finally, accurate registration of crack point clouds from different periods is achieved based on a two-step registration strategy. Experimental results on a real large-scale concrete structure show that the proposed path-planning algorithm can achieve complete envelope coverage conforming to the structural geometry, with an effective coverage ratio above 99.7%. The dimensional error of structural reconstruction is controlled within 20 mm. The average crack localization time is 4.00 s, and mean absolute error of crack width quantification is 0.44 mm. The average crack registration error is 0.97 mm, thereby enabling accurate tracking of crack evolution.
Xubin He, Xingjian Shi, Jiawang Song et al.· Infrastructures· 0 citations
This study conducted a kinematic analysis of rock slopes in an open-pit feldspar mine in Çine (Aydın, Türkiye) and explicitly focused on the validation of uncrewed aerial vehicle (UAV)–based photogrammetry with structural data obtained from traditional field surveys. Input parameters (dip direction and dip angles) were collected using a traditional geological compass at 119 observation points and extracted from a UAV-generated dense point cloud using the CloudCompare Compass plugin. A direct reliability assessment between the two methods revealed that the UAV-derived orientations matched the traditional field measurements with high precision, specifically within a 2° to 3° margin of error. Furthermore, the UAV approach effectively facilitated data collection in inaccessible or hazardous zones, such as high bench faces and block debris areas, safely increasing spatial data coverage and saving time. Using these validated parameters, kinematic analyses identified 65 wedge, 40 planar, and 87 toppling failures, predominantly concentrated on northwest-facing slopes. Spatial evaluations using geographic information system (GIS) mapping techniques highlighted the finding that stable wedges are restricted to paleo-stream channels, whereas unstable cases are located in weathered zones affected by water infiltration. Ultimately, the study demonstrates that integration of UAV photogrammetry with conventional surveys provides a highly reliable, safe, and efficient framework for kinematic assessments in open-pit mines.
Melikhan Karakas, E. Kalhan, C. Kıncal· Environmental & Engineer...· 0 citations
