To protect the environment and reduce the consumption of natural resources, this study investigated the performance of self-compacting concrete (SCC) utilizing aeolian sand (AS) as a replacement for fine aggregate and recycled coarse aggregate (RCA) as a replacement for natural coarse aggregate. Twelve mixing ratios were prepared, with AS replacement ratios at 0, 20%, 40%, and 60% and RCA replacement ratios at 0, 25%, and 50%. The evaluation primarily focused on workability, uniaxial compressive strength, split tensile strength, stress–strain curve, modulus of elasticity, and axial compressive strength. The results indicated that both AS and RCA can enhance the performance of SCC at appropriate replacement ratios, and a 20% AS and 50% RCA replacement ratio significantly improved the performance of SCC. In addition, the effects of AS and RCA replacements on SCC were evaluated by several performance indexes, which provides a basis for subsequent experimental studies and demonstrates the feasibility of incorporating AS and RCA into SCC.

Aeolian sand (AS) and recycled coarse aggregate (RCA) can be reasonably utilized as green materials for concrete modification. The paucity of natural sand and gravel in the construction industry is anticipated to be remedied by the use of these two eco-friendly concrete ingredients. This is incredibly important for environmental protection. Study on the damage law of self-compacting concrete with the addition of AS and RCA (ARSCC) under severely cold conditions is of great significance for the promotion and implementation of this material. In this study, 12 groups of ARSCC specimens were prepared for freeze–thaw cycle experiments, with AS substitution rates of 0, 20%, 40%, and 60% as well as RCA replacement rates of 0, 25%, and 50%. Then, the degradation mechanism of ARSCC freeze–thaw damage was discussed from both macroscopic and microscopic perspectives via mass loss rate (W_n), relative dynamic modulus of elasticity (P_n), bubble spacing factor, and SEM analysis. Finally, the response surface method was utilized to determine the damage variable. A freeze–thaw damage model for ARSCC was developed based on the Weibull distribution and Grey theories. The results showed that the P_n could reflect the evolution law of the internal structure of ARSCC. Appropriate addition of AS to fill the large, harmful pores in RCA would inhibit freeze–thaw damage of ARSCC. The optimum substitution rates of AS and RCA were determined to be 20–40% and 25–50%, respectively. In addition, the values obtained from theoretical damage modeling and experiments were in good agreement. The acquired damage model had the potential to predict ARSCC damage under freeze–thaw cycles.

Acoustic emission (AE) signals exhibit a strong correlation with concrete damage. However, the relationship between column damage and AE signals under eccentric loading conditions, combined with the application of traditional RA-AF classification methods for crack characterization, demonstrates limitations. These approaches provide insufficient resolution to accurately identify damage types throughout the entire structural failure process. This study employed K-means clustering algorithm and Gaussian mixture models (GMMs) to analyze AE signal features from reinforced concrete (RC) columns undergoing failure under the eccentric compression loading of different eccentricity. Subsequently, a random forest model was used for automated damage stage classification. Experimental results demonstrate that the damage progression in eccentrically compressed columns comprises four distinct stages, each exhibiting unique AE signal characteristics. The integrated approach of clustering and random forest modeling demonstrates robust feasibility in identifying AE signal patterns associated with specific damage stages, achieving an 85% recognition rate for damage stage classification. These findings provide quantitatively validated evidence supporting the efficacy of machine learning-based methodologies for enabling stage-specific damage characterization in structural health monitoring applications.

Ultra-shallow prefabricated underpass tunnel technology has been widely adopted in urban transportation construction owing to its advantages of rapid construction and minimal environmental impact. However, the deformation behavior of tunnel joints under long-term vehicular dynamic loads remains unclear, which constrains the reliability and durability of this technology. To address this, this study focuses on a large cross-section tunnel with five bidirectional lanes. A combined methodology of “refined numerical simulation + long-term cyclic loading model tests” was employed to systematically investigate the dynamic response and cumulative deformation patterns of tunnel joints under different burial depths (3 m, 5 m, and 8 m) and prestress levels (0–0.5 MPa). First, based on the analysis of structural bending moment distribution, various division principles such as zero-moment points and maximum-moment points were compared, leading to the determination of a joint layout scheme primarily adopting a two-segment division. On this basis, a refined numerical model integrating pavement excitation and vehicle dynamic coupling was established, supplemented by a model test with 2 million loading cycles, to reveal the deformation mechanism of joints under both moving vehicle loads and long-term loading. The results indicate the following: (1) burial depth is the decisive factor controlling overall joint deformation—increasing the depth from 3 m to 8 m can reduce the maximum joint opening and slip by approximately 60%; (2) prestress serves as a key measure for restraining joint opening and ensuring waterproofing performance, with its effect being particularly pronounced under shallow burial conditions; (3) based on the dynamic attenuation coefficient, the concept of “sensitive burial depth” (approximately 3.7 m) is proposed, providing a quantitative criterion for identifying tunnels susceptible to surface traffic loads; (4) the recommended two-segment structural division scheme effectively controls deformation while considering construction convenience and waterproofing reliability. The methodological framework of “numerical simulation + model testing” established in this study can provide theoretical support and engineering reference for the long-term performance design and assessment of ultra-shallow prefabricated tunnels.