Applications of Intelligent Technologies in the Life Cycle of Transportation Infrastructure

This study investigates the transferability of measured road-damage data between distinct geographic domains using the YOLOv8s deep-learning framework. A comparative evaluation was performed on two datasets: the locally developed RDD2024_SA (South Africa) and the publicly available RDD2022_India (India). Five training–testing scenarios were designed to analyze intra- and inter-dataset generalization, emphasizing the influence of dataset scale, annotation consistency, and class structure on detection performance. When trained and tested within the same domain, YOLOv8s achieved high accuracy (mAP@0.5 > 0.95), confirming the strength of localized feature learning. However, performance degraded substantially under cross-domain testing, revealing a sensitivity to differences in road texture, illumination, and labeling style. Reducing the number of classes from six to four dominant types improved stability (mAP@0.5 ≈ 0.78) by mitigating annotation noise and class imbalance. Furthermore, a transfer-learning configuration, in which the India-trained model was fine-tuned on 20% of the South-African dataset, achieved mAP@0.5 = 0.86, demonstrating effective recovery of cross-domain detection performance. These findings highlight the importance of domain-aligned data preparation, targeted fine-tuning, and balanced class representation in building robust and transferable AI systems for sustainable, data-driven road maintenance. Full article

Aiming at the signal interference and feature recognition difficulties existing in the detection of concealed defects such as cracks and voids behind the tunnel lining, this study carried out a 1:1 reinforced concrete–steel arch frame composite lining model test; simulated the surrounding rock defects scenarios of three types of filling media, namely crushed stone, air, and water; and analyzed the time-domain, frequency-domain, and time–frequency-domain characteristics of the geological radar signal data. The research finds that the water-filled area generates a strong reflection due to the high dielectric constant, with the spectral peak reaching 712 MHz and the high-frequency component significantly enhanced. The peak frequency of the air-filled zone spectrum is 531 MHz, and the high-frequency bandwidth is broadened. The spectral peak of the crushed stone filling area is 507 MHz, with fast high-frequency attenuation and energy dispersion. The time-domain waveforms show that the amplitude in the water-filled area is the highest and the tailing is obvious, the waveform in the air-filled area is sharp, and the amplitude in the crushed stone-filled area is gentle. The peak frequency of the spectrum, the amplitude attenuation law, and the waveform shape can be used as the key indicators for discriminating the category of filling materials. The analysis method of feature fusion in the time–frequency domain has important engineering application value for improving the detection accuracy of geological radar in complex lining structures. Full article

Efficient generation of 3D mesoscale concrete models with high aggregate volume fractions remains challenging due to the computational complexity of detecting overlaps between irregularly shaped aggregates. This study presents an efficient modeling approach utilizing an improved Gilbert–Johnson–Keerthi (GJK) algorithm for rapid collision detection between convex polyhedral aggregates. The enhanced algorithm significantly reduces computational time by approximately 20–25% compared to the classical GJK algorithm, while maintaining detection accuracy, enabling the direct generation of high-volume-fraction (50%) concrete models without requiring additional settlement procedures. The “take-and-place” method is employed to generate and place aggregates according to specified gradation and volume fraction of aggregates. The model is validated against experimental uniaxial compression tests; the simulations accurately capture the macroscopic mechanical response and failure patterns, with the peak stress showing good agreement with experimental data (relative error ≈ 7.6%). The validated model is then employed in a comprehensive parametric study to systematically investigate the influence of key mesoscale parameters, providing profound insights into the underlying failure mechanisms. The proposed approach provides an efficient solution for rapid generation of realistic 3D mesoscale concrete models, facilitating more extensive parametric studies and mechanical analyses. Future extensions may include handling more complex aggregate shapes and leveraging parallel computing for further acceleration. Full article

Reclaimed coastal areas are highly susceptible to uneven subsidence caused by the consolidation of soft marine deposits, which can induce differential settlement, structural deterioration, and systemic risks to urban infrastructure. Further, engineering activities, such as construction and loadings, exacerbate subsidence, impacting infrastructure stability. Therefore, monitoring the integrity and vulnerability of linear urban infrastructure after construction on reclaimed land is critical for understanding settlement dynamics, ensuring safe and reliable operation and minimizing cascading hazards. Subsequently, in the present study, to monitor deformation of the linear infrastructure constructed over decades-old reclaimed land in Mokpo city, South Korea (where 70% of urban and port infrastructure is built on reclaimed land), we analyzed 79 Sentinel-1A SLC ascending-orbit datasets (2017–2023) using the Persistent Scatterer Interferometry (PSInSAR) technique to quantify vertical land motion (VLM). Results reveal settlement rates ranging from −12.36 to 4.44 mm/year, with an average of −1.50 mm/year across 1869 persistent scatterers located along major roads and railways. To interpret the underlying causes of this deformation, Casagrande plasticity analysis of subsurface materials revealed that deep marine clays beneath the reclaimed zones have low permeability and high compressibility, leading to slow pore-pressure dissipation and prolonged consolidation under sustained loading. This geotechnical behavior accounts for the persistent and spatially variable subsidence observed through PSInSAR. Spatial pattern analysis using Anselin Local Moran’s I further identified statistically significant clusters and outliers of VLM, delineating critical infrastructure segments where concentrated settlement poses heightened risks to transportation stability. A hyperbolic settlement model was also applied to anticipate nonlinear consolidation trends at vulnerable sites, predicting persistent subsidence through 2030. Proxy-based validation, integrating long-term groundwater variations, lithostratigraphy, effective shear-wave velocity (V_s³⁰), and geomorphological conditions, exhibited the reliability of the InSAR-derived deformation fields. The findings highlight that Mokpo’s decades-old reclamation fills remain geotechnically unstable, highlighting the urgent need for proactive monitoring, targeted soil improvement, structural reinforcement, and integrated InSAR-GNSS monitoring frameworks to ensure the structural integrity of road and railway infrastructure and to support sustainable urban development in reclaimed coastal cities worldwide. Full article