Asphalt overlays have been widely employed in airport runway maintenance in recent years due to their ability to minimize traffic disruption. However, they continue to face the challenge of reflective cracks originating from the expansion joints of the underlying cement concrete runway. To better understand the cracking behavior of asphalt overlays under the combined temperature variations and aircraft loads, this study developed a finite element (FE) model. with the model incorporate two types of landing methods and typical temperature conditions. Simulation results indicate that critical loading positions are located at the edges of original cement concrete slabs, where shear stress is identified as the primary driver of crack evolution, with the peak stress coinciding with the arrival of aircraft load. Furthermore, findings suggest that the use of asphalt overlays significantly reduces the stress intensity in crack-prone areas, particularly under rough landing conditions. Reflective cracks predominantly manifest as type II shear cracks, While aircraft loading and initial crack length exert a relatively limited impact on crack propagation compared to temperature effects, the horizontal location of the initial crack substantially influences both the direction and speed of crack propagation. To mitigate crack propagation, increasing the linear shrinkage coefficient of the overlay material and the thickness of the asphalt overlay are effective strategies for enhancing the cracking resistance of airport runways with asphalt overlays. The methodologies and findings of this study provide valuable insights for engineering practices involving similar structural configurations and materials.

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Understanding pedestrian wayfinding behavior is crucial for traffic management and building design. The use of virtual reality technology presents an efficient approach for investigating pedestrian wayfinding behavior in large public spaces, offering numerous advantages for data collection. However, the impact of different scenario dimensions on pedestrian wayfinding behavior in large public spaces remains unclear. Additionally, the selection of virtual experiment scenario dimensions currently relies primarily on researchers’ experience and practical conditions, lacking sufficient evidence to support their rational. Another challenge is the limited focus on spatial knowledge’s effect on wayfinding behavior, with insufficient analysis of the utility of pedestrian visual information and a lack of precise methods to quantify visual field information accurately. This study addresses these gaps by incorporating spatial knowledge at multiple scales and pedestrian visual field information as influencing factors in the analysis of wayfinding behavior. Furthermore, it distinguishes between three-dimensional and two-dimensional scenarios to compare the impact of dimensional differences on pedestrian wayfinding behavior. By analyzing behavior data from non-immersive wayfinding experiments, this research employs statistical analysis methods and a deep learning framework to derive results regarding the factors influencing wayfinding behavior. The findings demonstrate that considering both spatial and visual field information effectively enhances the predictive ability of the wayfinding model. Additionally, dimensional differences significantly influence the pedestrian wayfinding process. These results offer empirical evidence to guide researchers in selecting experimental scenarios of pedestrian behavior and provide insights for public space layout, signage design, and improving pedestrian efficiency.@en