This paper presents a Bayesian inference framework for updating the structural rigidity ratio of aging hollow slab RC bridges using deflection measurements. The framework models the structural rigidity ratio as a stochastic field along the hollow RC slabs, using the Karhunen–Loeve (KL) transform to capture spatial correlation and variation. Bayesian inference is then applied using deflection data from static loading tests, supported by a finite element model (FEM) and a Kriging surrogate model to enhance computational efficiency. The posterior distribution of the structural rigidity ratio is derived using a Markov chain Monte Carlo (MCMC) sampler. The proposed method was tested on an RC bridge with hollow slabs, using deflection measurements taken before and after reinforcement. The Bayesian updates indicated increased structural rigidity ratios after reinforcement, validating the effectiveness of the reinforcement. The deflection predictions from the updated models closely matched the measurements, with the 95% confidence bounds encompassing most of the data. This demonstrates the method’s validity and robustness in capturing the structural improvements post-reinforcement.

Efficient recycling of reclaimed asphalt pavement (RAP) is essential to the low-carbon transformation of transportation infrastructure. Traditional carbon accounting methods based on four factors—RAP content, aging level, rejuvenator dosage, and mixing temperature—struggle to capture emission patterns under multi-parameter coupling or isolate confounders to identify true causal effects. The study considers a single-lane asphalt pavement functional unit (1 km length, 3.75 m width, and 0.18 m thickness), and to address this problem, this paper integrates life cycle assessment (LCA), a thermodynamic mechanism model, multi-model machine learning, and double/debiased machine learning (DML) to construct a carbon emission prediction and causal analysis framework. First, reclaimed asphalt pavement is used as the LCA model to generate 10,206 carbon emissions and cost datasets covering the four factors, with embedded nonlinear thermodynamic calculations. Then, 3000 semi-synthetic plant-day observations are constructed by introducing observable engineering conditions, batch variation, and Gaussian white noise. Finally, four prediction models—BP neural network, random forest, XGBoost, and SVR-RBF—were compared using grouped five-fold cross-validation. The BP model achieved the highest predictive accuracy (R² = 0.9638) and was selected as the auxiliary learner for cross-fitted DML. The estimated model-based intervention effects for a 10-percentage-point increase in RAP content, a 0.1 increase in aging coefficient, a 1-percentage-point increase in rejuvenator dosage, and a 10 °C increase in mixing temperature were 12.492, 7.077, 13.189, and 8.632 t CO₂-eq, respectively. These results show that material circularity and carbon reduction do not automatically guarantee economic sustainability. The framework links resource circularity, causal process understanding, and low-carbon process optimization to support sustainability-oriented RAP decisions. This study offers a reference for low-carbon design and process optimization of asphalt pavement recycling.

To improve the low-temperature performance of direct coal liquefaction residue (DCLR)-modified asphalt, a direct-addition composite modifier (DCLAM) containing DCLR, SBS, crumb rubber, activated nano-CaCO₃, and aromatic oil was developed. A four-factor, three-level orthogonal design was used to optimize the formulation based on dynamic shear rheometer (DSR) and bending beam rheometer (BBR) tests. The optimal formulation contained 5% DCLR, 1% SBS, 15% crumb rubber, 3.5% nano-CaCO₃, and 2% aromatic oil. At 64 °C, the optimized mastic exhibited a complex shear modulus of 18,506.7 Pa and a rutting factor of 19.4 kPa, while its phase angle decreased to 72.4°. At −12 °C, the creep stiffness was 167.3 MPa and the m-value was 0.383. Mixture-level performance was further evaluated through wheel-tracking, low-temperature beam bending, freeze–thaw splitting, and dynamic modulus tests. The DCLAM-designed mixture exhibited a dynamic stability of 7176.0 cycles/mm, a maximum flexural tensile strain of 3087 με, and a freeze–thaw tensile strength ratio of 78.3%. It also showed higher complex modulus and lower phase angle values than the base mixture over the investigated temperature–frequency range. These results demonstrate the potential of the optimized DCLAM formulation for balancing high-temperature deformation resistance and low-temperature cracking resistance in asphalt pavement materials.

To address the issue of insufficient low-temperature performance in asphalt modified with Direct Coal Liquefaction Residue (DCLR), this study developed a direct addition DCLR composite modifier (DCLAM). The DCLAM-modified asphalt was prepared by blending DCLR with SBS, crumb rubber, activated nano-CaCO3, and aromatic oil to systematically optimize its high- and low-temperature performance and rheological characteristics. A four-factor, three-level orthogonal experimental design was adopted. Key parameters of the modified asphalt mastic, including the complex shear modulus (G*), phase angle (δ), rutting factor (G*/sin δ), creep stiffness (S), and creep rate (m), were analyzed using a Dynamic Shear Rheometer (DSR) and a Bending Beam Rheometer (BBR). The results indicate that DCLAM markedly improves the high-temperature stability and low-temperature cracking resistance of asphalt. At 64℃, the optimal formulation (5% DCLR, 1% SBS, 15% crumb rubber, 3.5% nano-CaCO3, 2% aromatic oil) achieved a G* of 17,749 Pa, which represents an increase of approximately 65% compared to the base asphalt. The rutting factor reached 75.2 kPa, showing an improvement of about 48%, while the phase angle decreased to 34.9°, indicating a stronger elastic response. At -12℃, the creep stiffness was reduced to 167.3 MPa, and the creep rate increased to 0.383, meeting the low-temperature specifications of the Superpave system. This composite modifier demonstrates promising engineering applicability, offering a novel approach for the efficient utilization of coal-based asphalt.