The permeability coefficient (PCOEF) is an important indicator to characterize the performance of asphalt concrete pavement. The dynamic PCOEF is more helpful to understand the permeability performance of the asphalt concrete in comparison to the traditional average PCOEF. A method for dynamically testing the permeability coefficient of asphalt concrete is proposed. In this method, the camera is placed at a suitable position to ensure continuously imaging the water level. The images that record the water level changes are saved and processed in the runtime. Then, by mapping the water level positions to the capacities and recording the corresponding time, the real-time PCOEF can be calculated at each point-in-time. The experimental results show that the dynamic PCOEF for a certain specimen with a PCOEF of 531mL/min by traditional method can vary between 443-630 mL/min. The results also demonstrate that the proposed method is useful for the research of dynamic performance of asphalt concrete under rainy conditions.
Road crack detection is seriously affected by many factors in actual applications, such as some shadows, road signs, oil
stains, high frequency noise and so on. Due to these factors, the current crack detection methods can not distinguish the
cracks in complex scenes. In order to solve this problem, a novel method based on infrared laser pavement imaging is
proposed. Firstly, single sensor laser pavement imaging system is adopted to obtain pavement images, high power laser
line projector is well used to resist various shadows. Secondly, the crack extraction algorithm which has merged multiple
features intelligently is proposed to extract crack information. In this step, the non-negative feature and contrast feature
are used to extract the basic crack information, and circular projection based on linearity feature is applied to enhance the
crack area and eliminate noise. A series of experiments have been performed to test the proposed method, which shows
that the proposed automatic extraction method is effective and advanced.
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