Infrastructures

Traffic Speed Deflectometer (TSD) measures deflection velocities, normalised by travel speed to obtain deflection slopes. Pavement temperature and travel speed can significantly affect deflection slopes. Therefore, correcting deflection slopes for temperature and speed effects is essential. This study employs three-dimensional (3D) finite element simulations of a three-layer flexible pavement system subjected to moving load at travel speeds from 40 km/h to 80 km/h, while varying the Asphalt Concrete (AC) layers’ thickness from 100 mm to 300 mm and the temperature from 5 °C to 45 °C. The results showed that deflection slopes at 100 mm offset distance could be corrected for the effects of temperature and speed using a correction factor comprising the sum of a parabolic function of temperature and a linear function of speed. At 600 mm and 1500 mm offset distances, simpler correction factors could be established using the sum of linear functions of temperature and speed. The Mean Absolute Percentage Error (MAPE) for all predictions was below 3%, indicating high accuracy. Accurate regression-based equations were also proposed to incorporate AC thickness in predicting the correction factors. The results highlight the potential to correct deflection slopes to a reference temperature and speed by evaluating a range of pavement systems.

Distributed fiber-optic sensing (DFOS) with intentionally spaced mechanical fixity points was experimentally evaluated for the structural health monitoring (SHM) of reinforced concrete (RC) members. A full-scale four-point bending test was conducted on a 12 m RC beam (400 × 400 mm) instrumented with a single-mode DFOS cable incorporating internal anchors at 2 m intervals and bonded externally with structural epoxy. Brillouin time-domain analysis (BOTDA) provided distributed strain measurements at approximately 0.5 m spatial resolution, with all cables calibrated to ±15,000 µε. Under stepwise monotonic loading, the system captured smooth, repeatable strain baselines and clearly resolved localized tensile peaks associated with crack initiation and propagation. Long-gauge averages exhibited a near-linear load–strain response (R² ≈ 0.99) consistent with discrete foil and vibrating-wire strain gauges. Even after cracking, the DFOS signal remained continuous, while some discrete sensors showed saturation or scatter. Temperature compensation via a parallel fiber ensured thermally stable interpretation during load holds. The fixed-point configuration mitigated local debonding effects and yielded unbiased long-gauge strain data suitable for assessing serviceability and differential settlement. Overall, the results confirm the suitability of fixed-point DFOS as a durable, SHM-ready sensing approach for RC foundation elements and as a dense data source for emerging digital-twin frameworks.

Pavement condition assessment using computer vision has emerged as an efficient alternative to traditional manual surveys, which are often labor-intensive and time-consuming. Leveraging deep learning, pavement distress such as cracks can be automatically detected, segmented, and quantified from high-resolution images captured by survey vehicles. Although numerous segmentation models have been proposed to generate crack masks, they typically require extensive pixel-level annotations, leading to high labeling costs. To overcome this limitation, this study integrates the Segmentation Anything Model (SAM), which produces accurate segmentation masks from simple bounding box prompts while leveraging its zero-shot capability to generalize to unseen images with minimal retraining. However, since SAM alone is not an end-to-end solution, we incorporate YOLOv8 for automated crack detection, eliminating the need for manual box annotation. Furthermore, the framework applies local refinement techniques to enhance mask precision and employs Optical Character Recognition (OCR) to automatically extract embedded GPS coordinates for geospatial mapping. The proposed framework is empirically validated using open-source pavement images from Yamanashi, demonstrating effective automated detection, classification, quantification, and geospatial mapping of pavement cracks. The results support automated pavement distress mapping onto real-world road networks, facilitating efficient maintenance planning for road agencies.