Vinay Chawla, and Carol Massarra, PhD.
East Carolina University
Greeneville, North Carolina
Husam Sadek, Ph.D.
Louisiana State University
Baton Rouge, Lousiana
Collecting condition data of pavement structures after hurricane events are extremely essential in any effort to reduce future economic losses from natural hazards and improve the structural reliability and resilience. These data are used as a record of infrastructure condition/performance and as a major component to assess their functionality and structural integrity. It is essential to obtain rapid information of the conditions post the disaster event to take necessary measures for mitigation and recovery operations. However, it is a challenging task because of the accessibility restrictions and safety hazards. Therefore, with evolving technology like Unmanned Aerial Vehicles (UAVs) and Laser Scanners (LiDAR), high resolution aerial and 3D images can be obtained aiding in the rapid damage assessment of the pavements.
The objective of this research is to collect the pavement condition data on surface distress and defects (i.e., cracks, potholes, and rutting) using the Unmanned Aerial Vehicle (UAV), e.g., DJI Matrice 210 with X4S, DJI Zenmuse Z30 and MicaSense Altum Cameras and LiDAR technology. The research methodology mainly consists of obtaining high resolution images of pavements. The aerial images were obtained by flying the drone over the test damaged pavement, which was set on an automated flight path and the cameras were attached to it to capture images along the way. To set the drone on an automated flight path, the application “Pix4D Capture” was used. These images will be stitched together in post-processing and comprehensive information about the pavement defects will be gathered. The laser scanner was used to scan a damaged pavement, it was mounted on a tripod and levelled with its ground position in order to acquire accurate and detailed information of the defects. Two sets of observations were taken, first a primary 360º scan was done to get a low resolution scan of the area. Afterwards, the pavement areas of interest were selected in the primary scan to get high resolution 3D scans which will aid in determining the dimensions of the defects. Pavement selection was based on 1) location (pavement on a bridge affected by hurricane, and 2) properties (pavement consists of rigid and flexible pavements).
The resulting high resolution images and scans from the UAV and Laser Scanner will enable us to identify the type of the defect through visual interpretation and observation. The dimensions of the surface cracks and distress (i.e. length, width and depth) will be determined during the post-processing using the applications “Pix4D” and Cloud Compare. In addition, the location of these damages will be identified by the built-in GPS and Inertial Measurement Unit (IMU) in the UAV and Laser Scanners. The multispectral aerial imagery obtained through the UAV will also assist in seeking out temperature variations in the pavement corresponding to these defects and distress.
Data needed for effective infrastructure data collection and damage assessment will be identified and while most of the existing data collection practices focused on collecting data for structures highly affected by hurricane such as residential buildings, more data regarding the infrastructures and pavement sections are needed to improve our understanding of pavement performance after hurricane events. The data will serve as the base for developing enhanced performance-based design standards, which will lead to more reliable performance of infrastructures. It will also provide an efficient, rapid and economical technique for investigating pavements after natural hazards.
Keywords: Hurricane, Drones, Pavement