In road engineering, road construction requires a large amount of natural aggregate; its substitution with recycled construction-solid-waste aggregate not only saves resources but also reduces the burden on the environment. The main components of construction solid waste are concrete blocks and brick slag; the breakability of the latter can affect the performance of mixed recycled aggregate, which hinders the use of construction solid waste in road engineering applications. To analyze the applicability of recycled construction-solid-waste aggregate containing brick slag aggregate in the subgrade layer, the effect of brick aggregate content on the CBR (California bearing ratio) and crushing value of mixed recycled aggregates was evaluated based on laboratory tests, and the field compaction quality of the recycled aggregates was analyzed. The results show that the 9.5–19 mm mixed recycled aggregate samples were crushed to a higher degree during the compaction process. A brick aggregate content less than 40% had little effect on the performance of mixed recycled construction-solid-waste aggregate. It is recommended to use a 22 t road roller for five passes (two weak vibrations + two strong vibrations + one weak vibration) at a speed of 3 km/h in the main compaction stage of the subgrade filling.

Impact-Echo (IE) testing is a promising nondestructive evaluation technique for concrete structures, but interpreting its frequency response in cylindrical bridge piers remains challenging because structural geometry, propagation paths, and internal discontinuities can produce overlapping spectral characteristics. This study investigates IE responses of cylindrical concrete bridge piers through field measurements and three-dimensional finite-element simulations. Field tests were conducted on two in-service bridges to characterize dominant-frequency responses and measurement repeatability under practical conditions. The numerical investigation considered hollow voids and segregation-like low-stiffness regions with different sizes and spatial configurations. The effects of defect type, size, and location on dominant frequency, waveform timing, and transmission characteristics were examined to interpret the underlying response mechanisms. The simulations showed that hollow voids generally produced stronger transmission blockage and more pronounced temporal shifts in the backside response than segregation-like regions in the representative cases, although their frequency responses were not uniquely distinguishable. A normalized dominant-frequency-shift index was subsequently evaluated as a candidate measure of relative spectral deviation. Within the deduplicated numerical parameter set, hollow-void cases generally exhibited larger dominant-frequency shifts than segregation cases. However, the distributions overlapped, and one segregation case was highly sensitive to dominant-peak selection because of two nearly equal spectral peaks. The field measurements demonstrated repeatable dominant-frequency responses under the investigated conditions, while spatial variations and measurement geometry highlighted the limitations of single-frequency interpretation. The findings support comparative interpretation of IE responses in cylindrical members but also highlight the need for further validation using independently confirmed defect conditions.

Subgrades are vulnerable to hydrological effects, repeated loading, and material degradation, which may induce settlement, voids, loosening, and differential deformation and ultimately threaten the safety and serviceability of transportation infrastructure. With the growing demand for continuous and intelligent condition assessment, conventional single modality inspection methods are increasingly insufficient for complex field environments. This paper reviews recent advances in intelligent subgrade inspection and distress identification following a “sensing–identification–application” framework. Multi-physics sensing technologies, including ground-penetrating radar, elastic-wave testing, intelligent compaction, and embedded sensing, are summarized with emphasis on their applicable distress types, sensing characteristics, and life-cycle stages. AI-based methods for distress identification and condition prediction are then discussed, covering image recognition, object detection, semantic segmentation, time-series forecasting, and physics-informed data fusion. Engineering applications involving vehicle, rail, UAV, and robot-based inspection platforms are also examined. Future research should focus on multisource sensing fusion, stronger integration of data-driven models with engineering mechanics, improved cross-scenario adaptability, and closer links between inspection results and maintenance decisions. These advances are expected to support a transition from passive distress detection toward proactive prediction and intelligent infrastructure management.