Grouting technology is widely used in foundation treatment to achieve the uplifting and correction of buildings. In this context, analyzing the slurry diffusion mechanism and the resulting behavioral characteristics is crucial for guiding precise engineering design practices. This study utilized an independently developed grouting model testing system to conduct grouting experiments on sandy soil employing diverse grouting methodologies and infiltration diffusion patterns. The objectives were to elucidate the characteristics of grouting pressure, lifting displacement, and stress variations within the sandy soil. The findings indicate that slurry diffusion in sandy soil typically progresses through three distinct stages, exhibiting a cyclic pattern of “compaction–splitting–compaction”. We observed that the slurry diffusion pattern closely aligns with the trend of uplift displacement changes. Furthermore, a general downward trend was observed in the stress attenuation of sand during the grouting process. Marked disparities exist in the slurry diffusion mechanism and stress characteristics between point source and perforated pipe grouting. These research outcomes contribute significantly to advancing the theoretical understanding and experimental design of grouting techniques in sandy soil.

Desiccation cracking in expansive soils on slopes accelerates soil erosion and reduces water retention capacity, posing significant threats to agricultural sustainability. This study investigates the combined effects of organic fertilizer (OF), slow-release fertilizer (SRF), and rice straw (RS) on crack inhibition in expansive soil under wet–dry (W-D) cycles. A three-factor Box–Behnken design (BBD) was used with OF at 5–15%, SRF at 0.3–0.9 kg/m³, and RS at 0.25–0.75%, and crack development was followed through 20 W-D cycles. The quadratic response-surface model fitted to the BBD crack-ratio data was significant (R² = 0.952, adjusted R² = 0.865, p = 0.008), with significant linear effects of OF, SRF, and RS, whereas the two-factor interaction terms were not statistically significant as a group (p = 0.701). The lowest observed crack ratio in the BBD matrix was 1.7% at 10% OF + 0.9 kg/m³ and SRF + 0.75% RS; this is therefore reported as an observed low-response combination rather than evidence of a statistically confirmed SRF–RS interaction. An LSTM model was retained as a complementary dynamic predictor and yielded R² = 0.927, MAE = 0.325, and RMSE = 0.478 for the evaluated dataset; however, divergence between training and validation losses indicated overfitting, so external generalizability remains to be established. The results support agricultural-waste-based amendment as a promising crack-control strategy while emphasizing the need for independent replication and field validation.

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.