The purpose of this paper is to promote the application of nano-TiO₂/CaCO₃ in bituminous materials and present an experimental characterization of viscoelastic behaviors of bitumen and bituminous mixture modified by nano-TiO₂/CaCO₃. In this work, a series of viscoelastic behavior characterization tests were conducted, including dynamic shear rheometer (DSR) test for bitumen, uniaxial static compression creep test and dynamic modulus test for bituminous mixture. Moreover, various viscoelastic models with clear physical meanings were used to evaluate the influence of nano-TiO₂/CaCO₃ on the macroscopic performance of bitumen and bituminous mixture. The results show that bitumen and its mixtures are time-temperature dependent. The Christensen-Anderson-Marasteanu (CAM) model of frequency sweep based on DSR test indicated that adding nano-TiO₂/CaCO₃ can effectively capture the sensitivity of temperature. In addition, the incorporation of nano-TiO₂/CaCO₃ in bituminous mixture can significantly enhance the high-temperature anti-rutting, and slightly improve the low-temperature anti-cracking as well. At the same time, the modified Burgers model can accurately describe the viscoelastic behavior of bituminous mixtures in the first two creep stages, reflecting the consolidation effect of bituminous mixture. Also, the generalized Sigmoidal model can accurately grasp the characteristics of the relationship between dynamic modulus and reduced frequency and achieve good prediction effects in a wider frequency range.

Asphalt mixture proportion design is one of the most important steps in asphalt pavement design and application. This study proposes a novel multi-objective particle swarm optimization (MOPSO) algorithm employing the Gaussian process regression (GPR)-based machine learning (ML) method for multi-variable, multi-level optimization problems with multiple constraints. First, the GPR-based ML method is proposed to model the objective and constraint functions without the explicit relationships between variables and objectives. In the optimization step, the metaheuristic algorithm based on adaptive weight multi-objective particle swarm optimization (AWMOPSO) is used to achieve the global optimal solution, which is very efficient for the objectives and constraints without mathematical relationships. The results showed that the optimal GPR model could describe the relationship between variables and objectives well in terms of root-mean-square error (RMSE) and R². After the optimization by the proposed GPR-AWMOPSO algorithm, the comprehensive pavement performances were enhanced in terms of the permanent deformation resistance at high temperature, crack resistance at low temperature as well as moisture stability. Therefore, the proposed GPR-AWMOPSO algorithm is the best option and efficient for maximizing the performances of composite modified asphalt mixture. The GPR-AWMOPSO algorithm has advantages of less computational time and fewer samples, higher accuracy, etc. over traditional laboratory-based experimental methods, which can serve as guidance for the proportion optimization design of asphalt pavement.

To optimize the properties of asphalt mixtures and make full use of waste rubber tires, diatomite and crumb rubber particles were applied to reinforce the asphalt mixtures in this study. The rutting tests, the three-point bending tests, the freeze-thaw splitting tests, and the uniaxial compression creep tests were performed to analyze the effects of asphalt types and aggregate gradation on the pavement properties of diatomite and crumb rubber particles reinforced asphalt mixtures (DRPAM). Subsequently, the creep and relaxation characteristics of DRPAM were analyzed by the Burgers model, the modified Burgers model, the second-order extensive Maxwell model, and the Scott–Blair model. The results show that rubber particles and diatomite can reinforce the high temperature, low temperature, and viscoelastic properties of asphalt mixtures, although the improvement effect is weaker than styrene-butadiene-styrene (SBS). Consequently, it is concluded that rubber particle and diatomite compound modified asphalt mixture with suspension dense gradation and SBS binder will have better performance.

As an eco-friendly pavement material, waste tires rubber-modified asphalt mixtures (WRMs) have been applied in pavement engineering widely. To further improve the performance and adaptability of WRM, diatomite and basalt fibers are, respectively, added to WRM. Subsequently, the Marshall tests, the rutting tests, the low-temperature splitting tests, the freeze-thaw splitting tests, and the vibration attenuation tests are conducted to study the effect of diatomite and basalt fibers on pavement properties of WRM. Furthermore, the correlation degree between the content of diatomite, basalt fibers, asphalt, and the pavement properties of WRM is analysed by the grey correlation grade analysis (GCGA). The results show that the addition of diatomite and basalt fibers can significantly improve the pavement and vibration attenuation properties of WRM. The improvement of high-temperature permanent deformation resistance, low-temperature cracking resistance, and water damage resistance of WRM is mainly attributed to diatomite, basalt fibers, and asphalt-aggregate ratio, respectively. The improvement of the vibration attenuation of WRM by diatomite and basalt fibers is mainly attributed to the increase of waste tires rubber-modified asphalt (WRA) content caused by adding diatomite and basalt fibers.