Wenyuan Xu

Amid the growing focus on sustainable development, the integration of renewable materials, such as straw liquefied residue oil (SLRO), into asphalt engineering is gaining significant attention. However, the underlying compatibility mechanisms and their impact on performance remain inadequately understood. This study offers a thorough investigation of the SLRO-asphalt system by combining molecular dynamics (MD) simulations with multi-scale experimental techniques. MD simulations revealed that intermolecular interactions, primarily hydrogen bonding, increase with higher SLRO content, peaking at 10 wt.%, which facilitates the formation of the most cohesive molecular network. Experimental results confirmed that this enhanced network exerts a dual influence on performance. Dynamic shear rheometer (DSR) tests demonstrated that the 10% SLRO blend significantly improved high-temperature rutting resistance following short-term aging. In contrast, bending beam rheometer (BBR) tests on long-term aged samples indicated that the same stiffening mechanism reduced low-temperature thermal cracking resistance. This study clarifies the microscopic origins of this performance trade-off, suggesting that the optimal SLRO content is context-dependent, requiring a balance between rutting and cracking resistance rather than an absolute value. These findings provide a crucial mechanistic foundation for the rational design and performance-driven optimization of sustainable bio-asphalts.

As traffic load continuously rises and climatic conditions increasingly vary, the performance of conventional base asphalt can no longer satisfy the needs of modern road engineering in low-temperature cracking resistance, high-temperature stability, and long-term durability. Therefore, the development of novel and efficient asphalt modifiers holds significant engineering value and practical importance. In this study, modified asphalt was prepared using varying dosages of ZM modifier (direct-injection asphalt mixture modified polymer additive). A series of experiments was executed to assess its influence on asphalt properties. First, fundamental property tests were implemented to determine the regulating effect of the ZM modifier on basic physical performances, like the softening point and penetration of the base asphalt. Penetration tests at different temperatures were performed to calculate the penetration index, thereby assessing the material’s temperature sensitivity. Subsequently, focusing on temperature as a key factor, tests on temperature sweep, and multiple stress creep recovery (MSCR) were implemented to delve into the deformation resistance and creep recovery behavior of the modified asphalt under high-temperature conditions. In addition, bending beam rheometer (BBR) experiments were introduced to attain stiffness modulus and creep rate indices, which were applied to appraise the low-temperature rheological performance. Aside from Scanning Electron Microscopy (SEM), Fourier Transform Infrared Spectroscopy (FTIR) was utilized to explore the mechanism by which the ZM modifier influences the asphalt’s functional group composition and microstructure. Our findings reveal that the ZM modifier significantly increases the asphalt’s softening point and penetration index, reduces penetration and temperature sensitivity, and enhances high-temperature stability. Under high-temperature conditions, the ZM modifier adjusts the viscoelastic balance of asphalt, hence enhancing its resistance to flow deformation and its capacity for creep recovery. In low-temperature environments, the modifier increases the stiffness modulus of asphalt and improves its crack resistance. FTIR analyses reveal that the ZM modifier does not introduce new functional groups, indicating a physical modification process. However, by enhancing the cross-linked structure and increasing the hydrocarbon content within the asphalt, it strengthens the adhesion between the asphalt and aggregates. Overall, the asphalt’s performance improvement positively relates to the dosage of the ZM modifier, providing both theoretical basis and experimental support for its application in road engineering.

Large-scale engineering projects frequently involve pit excavation and wetland landfill operations, resulting in significant silt accumulation that occupies land and adversely affects the environment. Curing technology offers a solution for reusing this waste silt. In this study, straw ash and calcium carbide slag are proposed as effective curing agents for silt soil. Various indoor tests were conducted to evaluate the mechanical properties of the cured silt soil, while X-ray diffraction (XRD) and scanning electron microscopy (SEM) were used to analyze its mineral composition and micro-morphology. The results showed that increasing the curing agent dosage significantly improved soil strength. Specifically, at a 10% dosage, the California bearing ratio (CBR) value increased to 18.7%, which is 13.4 times higher than untreated silt soil and exceeds road specifications by 8%. At a 20% dosage, the unconfined compressive strength (UCS) value reached 1.38 MPa, meeting the ≥0.8 MPa requirement for roadbeds. Based on economic considerations, a 20% dosage of straw ash–calcium carbide slag was selected as optimal. Microscopic analysis revealed that the addition of these agents promoted the formation of hydrated calcium silicate, filling pores and enhancing the mechanical properties of the cured soil, resulting in a more dense and stable structure.

An interchange is a pivotal traffic facility that connects highways and controls access. It is necessary to study their dynamic response characteristics to analyze the operational safety of ramp bridges on interchanges. Based on the numerical simulation results of the finite element model of the Fuxing Interchange Bridge, non-destructive measurement techniques were used to conduct field dynamic load tests on the bridge, including ramp strain testing and acceleration testing. These tests aimed to study the dynamic response characteristics of the ramp bridge under moving loads. Due to the design speed limitation of the ramp bridge, the grey prediction GM(1, 1) model was used to predict the maximum dynamic deflection, maximum dynamic strain, and vibration acceleration when the vehicle speed was 60 km/h. Subsequently, finite element software was used to simulate the dynamic deflection under vehicle speeds ranging from 30 to 60 km/h. The simulated value was compared with the predicted value, and the difference between the simulated value and the predicted value was slight. This model can evaluate the operational safety performance of off-ramps at different speeds.

The importance of bridge technical condition assessment is not only to ensure traffic safety but also to ensure the project’s sustainable development. Therefore, problems exist in the traditional highway bridge technical condition evaluation standard, such as fixed weight value, cumbersome calculation, and intense subjectivity. A total of 146 bridge inspection reports in Heilongjiang Province were collected in this paper. Using Pearson correlation analysis for bridge components and bridge age and length, the features with strong correlation are identified as the basis for modeling, and a machine learning model is introduced to evaluate the technical condition of the bridge. The application effects of the BP neural network, support vector machine (SVM), random forest (RF), and particle swarm optimization-support vector machine (PSO-SVM) in bridge evaluation and classification are compared and analyzed. The results show that the essential parameters penalty factor c and kernel function g in support vector machine optimized by particle swarm optimization overcome the shortcomings of the SVM model, improving the accuracy of the assessment. It can be used as an effective means to evaluate the technical condition of bridges and provide scientific decision-making reference for the maintenance of bridges.