The steel bridge deck undergoes significant temperature changes during the high-temperature paving of asphalt pavement, and the non-uniform thermal field distribution herein affects the performance of pavement composite system. In this paper, the thermal field characteristic of the steel bridge deck during asphalt pavement paving was presented based on in-situ monitoring of the Tongling Yangtze River Bridge (TL Bridge) and numerical simulation. Firstly, the monitoring schemes were proposed to monitor the temperature changes of the steel bridge deck during the paving process, and the 3-D temperature distribution of the steel bridge deck was summarized. Secondly, a thermal field model of steel box girder was established according to the transient temperature field theory, and the element deletion method was used to simulate the process of paving construction. Subsequently, the thermal field model was validated by the monitoring results. Thirdly, the validated thermal field model was used to evaluate the sensitive factors that could affect the temperature distribution of the steel bridge deck during asphalt pavement paving. Finally, the critical temperature load formula was fitted, in which the sensitive factors were considered by introducing sensitive parameters.

Gussasphalt concrete (GAC) is increasingly used in long-span steel bridge deck pavements because of its impermeability and compliance. However, the paving temperature of GAC is very high, inducing a significant nonuniform temperature distribution in the bridge during paving, and this has an adverse effect on the serviceability of bridge. In this study, the temperature behavior and stability of orthotropic steel bridge deck (OSBD) during the GAC paving were investigated. First, the temperature distribution of a steel bridge during the GAC paving was studied using field monitoring and numerical simulation. Second, the temperature stress and deformation of the OSBD during GAC paving were calculated. Third, the effect of the temperature deformation of OSBD on pavement unevenness was examined. Last, a submodel and eigenbuckling analysis method was used to evaluate the stability of the U ribs during the GAC paving. The results show that uplift and expanded deformation occur in the steel bridge deck during the GAC paving, and this deformation could cause a nonuniform thickness distribution of pavement. The temperature load during paving could reduce the ultimate bearing capacity of the U ribs in the paving area, and there is a hidden danger of instability in the OSBD during the GAC paving.

The $\mathrm{EAC}+\mathrm{SMA}$ composite structure, made from epoxy asphalt concrete (EAC) with stone matrix asphalt (SMA) concrete on top, is a double-layered heterogeneous structure applied to steel bridge deck pavement (SBDP) in recent years. The interlayer bonding performance is key to the long-life application of $\mathrm{EAC}+\mathrm{SMA}$ . In this study, the objective is to investigate the interlayer bonding performance of $\mathrm{EAC}+\mathrm{SMA}$ composite structure to identify its best design and construction strategy. To achieve this goal, a series of pull-off tests and interlayer shear tests under different test conditions were performed on four commonly used $\mathrm{EAC}+\mathrm{SMA}$ composite structures. The effects of design parameters, the construction conditions, and the application environment of $\mathrm{EAC}+\mathrm{SMA}$ structure on its interlayer bonding performance were evaluated. The results showed that the $\mathrm{EAC}+\mathrm{SMA}$ composite structure with SMA13 (SMA mixture with the nominal maximum aggregate size of 13.2 mm) as the wearing layer material and epoxy resin–based bonding material possessed the best interlayer bonding performance among the four composite structures. It is suggested that the bonding layer and SMA layer should be constructed after the Marshall stability of the EAC layer reaches 15 kN, and the construction interface should be clean to achieve the best interlayer bonding performance for the $\mathrm{EAC}+\mathrm{SMA}$ composite structure. In addition, the mixed-mode cohesive zone model was developed to characterize the interlayer bonding behaviors in the mechanical analysis of $\mathrm{EAC}+\mathrm{SMA}$ composite structures, and the proposed numerical characterization method could be used to obtain more accurate mechanical results for the structure design compared with the traditional numerical characterization method under the interlayer continuous assumption.

We investigate the dynamics of droplet impacts on a ring-decorated solid surface, which is reported to reduce the integral of contact area over contact time by up to 80%. By using many-body dissipative particle dynamics (MDPD), a particle-based simulation method, we measure the temporal evolution of the shape and the impact force of two specific types of phenomena, overrun and ejection. The numerical model is first validated with experimental data on a plain surface from literature. Then, it is used to extract the impacting force of the ring and substrate separately, showing the ring does not provide the majority of vertical force to redirect the horizontal spreading. The impacting pressure in different concentric rings is also present as a function of time, showing pressure waves traveling from ring to center. The effect of the ring's height and radius on the impacting force is also discussed. To the best of our knowledge, this is the first MDPD study on droplets impacting on a solid surface with a validated force analysis.

Skid resistance is vital to road safety. The skid resistance of steel bridge deck pavement (SBDP) is particularly important due to the special operating conditions and unique structure features for bridges. This study aims to propose an effective method to predict the skid resistance of newly built SBDP at the design stage. First, the experiment was designed by Taguchi method to get the skid resistance-related data. Secondly, sensitivity analysis was conducted to evaluate the influence of material design and construction factors on the skid resistance. Finally, a prediction model based on GA-BP neural network (back propagation network optimised by genetic algorithm) was developed. Results show that the mixture design parameters have stronger influences on the skid resistance than the construction factors of SBDP. From this study, the 9-14-1 (Input neurons – Hidden neurons – Output neuron) GA-BP model with the population size of 60 could yield an acceptable prediction result for the skid resistance.