School of Civil and Environmental Engineering, Oklahoma State University, Stillwater, OK 74078, USA.
Sensors (Basel). 2022 Oct 21;22(20):8038. doi: 10.3390/s22208038.
Traditionally, pavement safety performance in terms of texture, friction, and hydroplaning speed are measured separately via different devices with various limitations. This study explores the feasibility of using a novel 0.1 mm 3D Safety Sensor for pavement safety evaluation in a non-contact and continuous manner with a single hardware sensor. The 0.1 mm 3D images were collected for pavement safety measurement from 12 asphalt concrete (AC) and Portland cement concrete (PCC) field sites with various texture characteristics. The results indicate that the Safety Sensor was able to measure pavement texture data as traditional devices do with better repeatability. Moreover, pavement friction numbers can be estimated using 0.1 mm 3D data via the proposed 3D texture parameters with good accuracy using an artificial neural network, especially for asphalt pavement. Lastly, a case study of pavement hydroplaning speed prediction was performed using the Safety Sensor. The results demonstrate the potential of using ultra high-resolution 3D imaging to measure pavement safety, including texture, friction, and hydroplaning, in a non-contact, continuous, and accurate manner.
传统上，路面的安全性性能（如纹理、摩擦和水漂速度）是通过不同的设备分别进行测量的，这些设备具有不同的局限性。本研究探索了使用新型的 0.1 毫米 3D 安全传感器以非接触和连续的方式，利用单个硬件传感器对路面安全进行评估的可行性。该传感器从 12 个具有不同纹理特征的沥青混凝土（AC）和波特兰水泥混凝土（PCC）现场采集了 0.1 毫米 3D 图像，用于路面安全测量。结果表明，安全传感器能够像传统设备一样测量路面纹理数据，且具有更好的可重复性。此外，通过提出的 3D 纹理参数，可以使用 0.1 毫米 3D 数据来估计路面摩擦系数，利用人工神经网络的方法具有良好的准确性，特别是对于沥青路面。最后，使用安全传感器进行了路面水漂速度预测的案例研究。结果表明，使用超高分辨率 3D 成像技术以非接触、连续和准确的方式测量路面安全性（包括纹理、摩擦和水漂）具有潜力。
