Excavation Quality Evaluation of Highway Tunnels Using 3D Laser Scanning

Workspace information acquisition for rapid roadway excavation equipment using laser scanning

UAV Photogrammetry and Geodetic Techniques for Concrete Dome Deformation Assessment

Historic reinforced concrete domes represent valuable cultural and architectural heritage that requires accurate geometric assessment to ensure their structural stability and support long-term conservation. This study proposes an integrated methodology for assessing the geometric deviations of a historic reinforced concrete dome at St. Mary and St. Joseph Church in Alexandria, Egypt. The proposed methods combine UAV close-range photogrammetry, terrestrial laser scanning (TLS), and total station observations to generate high-resolution three-dimensional models and evaluate the as-built geometry against the theoretical design. Cross-sectional and geometric analyses were performed to investigate radial, vertical, horizontal, and centric deviations, while the reliability of the photogrammetric data was verified through camera calibration and comparison with independent geodetic measurements. The results demonstrated that the integrated methodology successfully identified geometric irregularities and deformation patterns with a high level of consistency among the three surveying techniques. The validation process confirmed the reliability of UAV photogrammetry for structural deformation assessment when integrated with TLS. The proposed framework provides a practical and accurate solution for the geometric evaluation and long-term monitoring of historic concrete domes, contributing to improved structural condition assessment and supporting maintenance, conservation, and heritage preservation programs.

An Intelligent Method for Bearing Pad Flatness Inspection Based on UAV-Enabled 3D Reconstruction

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.

3D Point Cloud from Close-Range Photogrammetry for Defect Characterisation of Rubberised Concrete

Abstract. While three-dimensional (3D) point clouds are widely used in civil engineering, mainstream LiDAR systems such as Terrestrial Laser Scanning (TLS) are physically constrained to laboratory environments. Since their laser spot size typically exceeds the width of microcracks, the beam physically bridges over voids, rendering TLS unsuitable for fine-scale defect analysis. Alternatively, close-range photogrammetry utilising Structure-from-Motion (SfM) and Multi-View Stereo (MVS) algorithms offers a solution for testing highly tortuous materials, and its utility at fine-scale remains underexplored. This study adapts photogrammetric workflows specifically for rubberised concrete (RuC), a sustainable composite exhibiting high ductility and complex fracture morphologies. High-resolution image sets were captured using a Canon DSLR and an iPhone 16 to generate dense 3D models. Comparisons revealed that the DSLR-based reconstruction achieved sub-millimetre resolution, demonstrating superior performance for fine-scale surface monitoring. An RGB-guided crack extraction method was developed to enhance the identification of surface defects and isolate potential crack areas from the background. The extracted crack regions were visually distinguishable and provided a well-structured geometrical representation of defect morphology. Furthermore, a Pre and Post-Test deformation analysis was conducted to quantify surface displacement across testing stages. The results confirm that this close-range photogrammetry workflow is a flexible, high-resolution alternative to LiDAR for surface inspection and deformation monitoring of specimens in laboratory settings. Ultimately, this approach establishes a robust geometric baseline for future automated 3D feature characterisation and material performance evaluation.

A method for 3D reconstruction of train carriages based on LiDAR point clouds

In the context of the continuous growth of railway transportation volume, the safe operation and maintenance of train carriages have placed higher demands on the accuracy and efficiency of damage detection. Traditional detection methods are unable to effectively quantify various types of damage such as corrosion, dents, and surface deformations on the carriages. The digital detection technology based on 3D reconstruction provides a new approach to this problem. However, most existing 3D reconstruction methods rely on multi-view registration, which has the drawbacks of complex processes and long time consumption, making it difficult to meet the actual requirements of rapid detection during train operation. Therefore, this paper proposes a lightweight single-station laser radar reconstruction method without multiview registration. The comparative experiments conducted on four different types of carriages have verified that the comprehensive performance of this method is excellent. The BPA algorithm is prone to surface overfitting and loss of key features, while the Alpha-shape algorithm is prone to generating small holes, disordered structures, and poor noise resistance. The minimum Chamfer distance (CD) of this method can reach 0.006346 millimeters, the point-to-grid distance (PTD) is stable between 0.098 and 0.227 millimeters, and the reconstruction time is controlled within 5.34 to 6.38 seconds. This method demonstrates excellent feasibility and applicability in 3D reconstruction of train carriages, and can provide an efficient and feasible technical solution for the digital detection and intelligent maintenance of train carriages.

Dynamic Shadow Removal and Quality Assessment of High-Resolution Orthophotos for Pavement Inspection

Abstract. Traditional pavement inspection and data collection are often constrained by traffic conditions, operational safety, and equipment costs, making it difficult to achieve both efficiency and large-scale coverage. To address these limitations, this study employs a Pavement Roughness Index and Distress Extraction System (PRIDEs), which integrates high-resolution industrial cameras, high-precision global navigation satellite system (GNSS), wheel pulse sensors, and an onboard computer to acquire high-quality images under high-speed driving conditions. Using photogrammetry and computer vision techniques, camera poses are reconstructed to generate dense point clouds, digital surface models (DSMs), and orthophotos for detailed pavement distress analysis. However, the acquired imagery is affected by dynamic shadows and lens-focusing induced blur, resulting in ghosting artifacts and inconsistent orthophoto quality. To mitigate these issues, this study proposes a masking strategy during orthophoto generation, where U-Net is employed to detect shadow regions and Laplacian variance is used to identify blurred areas. By integrating these masks, more uniform and higher-quality orthophotos can be produced. Experimental results demonstrate that the proposed approach effectively reduces false positives and false negatives of crack detection caused by shadows and blur, thereby improving the reliability of orthophotos for automated pavement condition assessment.