Low-cost unmanned aerial system (UAS) photogrammetry and terrestrial laser scanner (TLS, terrestrial LiDAR) technologies are being used as noncontact measurement methods for collecting unstructured data for the maintenance of construction infrastructure facilities. This study investigated the possibility of settlement, which is a maintenance condition evaluation item for fill-dam bodies, using point clouds based on the UAS (unmanned aerial system) structure from motion (UAS-SfM) and TLS (terrestrial laser scanner) point clouds. Specifically, the Z-axis RMSE of the point cloud improved to 0.012 m and the shape reproducibility rate to 98.53% by complementing the heterogeneous data of the UAS and TLS by combining the two systems with block coordination and ICP algorithms. The maximum settlement height and volume (heaving) of the dam crest and upstream and downstream slopes were derived from the combined UAS/TLS point-cloud-based 3D model. The quantitative values for the settlement of the fill-dam body were derived using the combined 3D model with high accuracy and density. This result verified the possibility of using the combined 3D model for evaluation of the maintenance condition.

The mobile tunnel scanning system (MTSS) enables efficient tunnel inspection; however, motion blur (MB) generated at high travel speeds remains a major factor undermining the reliability of deep-learning-based crack detection. This study focuses on investigating how horizontally oriented MB in MTSS imagery affects the crack-detection performance of convolutional neural networks (CNNs) and proposes a data-centric quality-assurance framework that leverages no-reference image quality assessment (NR-IQA) to optimize model performance. By intentionally applying MB to both public and real-world MTSS datasets, we analyzed performance changes in ResNet-, VGG-, and AlexNet-based models and established the correlations between four NR-IQA metrics (BRISQUE, NIQE, PIQE, and CPBD) and performance (F1 score). As the MB intensity increased, the F1 score of ResNet34 dropped from 89.43% to 4.45%, confirming the decisive influence of image quality. PIQE and CPBD exhibited strong correlations with F1 (−0.87 and +0.82, respectively), emerging as the most suitable indicators for horizontal MB. Using thresholds of PIQE ≤ 20 and CPBD ≥ 0.8 to filter low-quality images improved the AlexNet F1 score by 1.46%, validating the effectiveness of the proposed methodology. The proposed framework objectively assesses MTSS data quality and optimizes deep learning performance, enhancing the reliability of intelligent infrastructure maintenance systems.

This study quantitatively evaluates the impact of motion blur—caused by high-speed movement—on image quality in a mobile tunnel scanning system (MTSS). To simulate movement at speeds of up to 70 km/h, a high-speed translational motion panel was developed. Images were captured under conditions compliant with the ISO 12233 international standard, and image quality was assessed using two metrics: blurred edge width (BEW) and the spatial frequency response at 50% contrast (MTF50). Experiments were conducted under varying shutter speeds, lighting conditions (15,000 lx and 40,000 lx), and motion speeds. The results demonstrated that increased motion speed increased BEW and decreased MTF50, indicating greater blur intensity and reduced image sharpness. Two-way analysis of variance and t-tests confirmed that shutter and motion speed significantly affected image quality. Although higher illumination levels partially improved, they also occasionally led to reduced sharpness. Field validation using MTSS in actual tunnel environments demonstrated that BEW and MTF50 effectively captured blur variations by scanning direction. This study proposes BEW and MTF50 as reliable indicators for quantitatively evaluating motion blur in tunnel inspection imagery and suggests their potential to optimize MTSS operation and improve the accuracy of automated defect detection.

Precise maintenance is vital for ensuring the safety of tunnel structures; however, traditional visual inspections are subjective and hazardous. Digital technologies such as LiDAR and imaging offer promising alternatives, but each has complementary limitations in geometric precision and visual representation. This study addresses these limitations by developing a three-dimensional modeling framework that integrates image and point cloud data and evaluates its effectiveness. Terrestrial LiDAR and UAV images were acquired three times over a freeze–thaw cycle at an aging, abandoned tunnel. Based on the data obtained, three types of 3D models were constructed: TLS-based, image-based, and fusion-based. A comparative evaluation results showed that the TLS-based model had excellent geometric accuracy but low resolution due to low point density. The image-based model had high density and excellent resolution but low geometric accuracy. In contrast, the fusion-based model achieved the lowest root mean squared error (RMSE), the highest geometric accuracy, and the highest resolution. Time-series analysis further demonstrated that only the fusion-based model could identify the complex damage progression mechanism in which leakage and icicle formation (visual changes) increased the damaged area by 55.8% (as measured by geometric changes). This also enabled quantitative distinction between active damage (leakage, structural damage) and stable-state damage (spalling, efflorescence, cracks). In conclusion, this study empirically demonstrates the necessity of data fusion for comprehensive tunnel condition diagnosis. It provides a benchmark for evaluating 3D modeling techniques in real-world environments and lays the foundation for digital twin development in data-driven preventive maintenance.

This study utilized unmanned aerial systems (UAS) and terrestrial laser scanners (TLS) to develop a 3D numerical model of slope anchors and conduct a comprehensive analysis. Initial data were collected using a UAS with 4 K resolution, followed by a second dataset captured 6 months later with 8 K resolution after artificially damaging the anchor. The model analyzed damage factors such as cracks, destruction, movement, and settlement. Cracks smaller than 0.3 mm were detected with an error margin of ±0.05 mm. The maximum damaged area on the anchor head was within 3% of the designed value, and the volume of damaged regions was quantified. A combination analysis examined elevation differences on the anchor’s irregular bottom surface, resulting in an average difference at 20 points, reflecting ground adhesion. The rotation angle (<1°) and displacement of the anchor head were also measured. The study successfully extracted quantitative damage data, demonstrating the potential for an accurate assessment of anchor performance. The findings highlight the value of integrating UAS and TLS technologies for slope maintenance. By organizing these quantitative metrics into a database, this approach offers a robust alternative to traditional visual inspections, especially for inaccessible facilities, providing a foundation for enhanced safety evaluations.