This paper introduces a novel approach to pavement material crack detection, classification, and segmentation using advanced deep learning techniques, including multi-scale feature aggregation and transformer-based attention mechanisms. The proposed methodology significantly enhances the model’s ability to handle varying crack sizes, shapes, and complex pavement textures. Trained on a dataset of 10,000 images, the model achieved substantial performance improvements across all tasks after integrating transformer-based attention. Detection precision increased from 88.7% to 94.3%, and IoU improved from 78.8% to 93.2%. In classification, precision rose from 88.3% to 94.8%, and recall improved from 86.8% to 94.2%. For segmentation, the Dice Coefficient increased from 80.3% to 94.7%, and IoU for segmentation advanced from 74.2% to 92.3%. These results underscore the model’s robustness and accuracy in identifying pavement cracks in challenging real-world scenarios. This framework not only advances automated pavement maintenance but also provides a foundation for future research focused on optimizing real-time processing and extending the model’s applicability to more diverse pavement conditions.

A modified strain-hardening cementitious composite (SHCC) material, fabricated using corundum aggregates (SHCC-Cor), has been proposed for roadway applications as it offers high structural performance and high skid resistance. However, the acoustic performance of SHCC is unclear and has not been well studied in the past. Theoretically, SHCC may not provide the optimum solution in acoustic performance as it provides a low texture profile, high density, and low porosity. In this study, the acoustic performance of pavement slabs made of SHCC and modified SHCC-Cor are investigated using a nondestructive method to determine the surface roughness (macro texture) of slab surfaces. The pavement–tire noise level was then simulated using SPERoN software. As result, the noise level coming from the pavement made of SHCC could be up to 65 dB(A), while the noise level for SHCC-Cor increased up to 69.2 dB(A) because of the lower shape factor (G) due to a rougher surface as a result of the existence of corundum aggregate on the SHCC surface. The aeroacoustics were also increased compared to the SHCC slab. The modification of SHCC-Cor by introducing grooves (SHCC-Cor-Gro) successfully reduced the sound level coming from the vibration.