This paper focuses on evaluating the effectiveness of an unmanned aerial vehicle (UAV) as a supplementary bridge damage quantification tool. For this study, a glued-laminated timber arch bridge in South Dakota was selected, and an UAV was utilized for the bridge damage quantification. A recommended four-stage UAV-enabled bridge damage quantification protocol involving image quality assessment and image-based damage quantification was developed. A field application using the UAV to measure crack lengths, thicknesses, and rust stain areas of the selected bridge was conducted following the recommended protocol. The image quality parameters, including sharpness and entropy, were used to determine the quality of the UAV-captured images. Pixel- and photogrammetry-based measurements using the high-quality images were obtained to quantify the bridge damage, and the damage was compared to that from actual field measurements. Once the damage information was gathered, the UAV image–based damage level classification was established based on the damage levels defined by current standards. The findings confirmed the accuracy of the recommended protocol, with results within 3.5, 7.9, and 14.9% difference for crack length, thickness, and rust stain area, respectively, when compared with the field measurements.
The field of Civil Engineering has lately gained increasing interest in Unmanned Aerial Vehicles (UAV), commonly referred to as drones. Due to an increase of deteriorating bridges according to the report released by the American Society of Civil Engineers (ASCE), a more efficient and cost-effective alternative for bridge inspection is required. The goal of this paper was to analyze the effectiveness of drones as supplemental bridge inspection tools. In pursuit of this goal, the selected bridge for inspection was a three-span gluedlaminated timber girder with a composite concrete deck located near the city of Keystone in the state of South Dakota (SD). A drone, a Dà-Jiang Innovations (DJI) Phantom 4, was utilized for this study. Also, an extensive literature review to gain knowledge on current bridge inspection techniques using drones was conducted. The findings from the literature review served as the basis for the development of a five-stage drone-enabled bridge inspection methodology. A field inspection utilizing the drone was performed following the stages of the methodology, and the findings were compared to current historical inspection reports provided by the SD Department of Transportation (SDDOT). Quantified data using the drone such as a spalled area of 0.18 m2, which is identical to the measurement provided by the SDDOT (0.3 m by 0.6 m), demonstrated the efficiency of the drone to inspect the bridge. This study detailed drone-enabled inspection principles and relevant considerations to obtain optimum data acquisition. The field investigation of the bridge demonstrated the image quality and damage identification capabilities of the drone to perform bridge inspection at a lower cost when compared to traditional methods.
Bridge inspection using a drone, also referred to as an unpiloted aircraft system, has gained more interest in recent years among bridge owners, researchers, and stakeholders because of its efficiency and effectiveness. In fact, numerous bridges classified as structurally deficient in the United States that require more attention and effort for maintenance activities can be inspected using drones in an efficient manner. The primary goal of this project was to evaluate drones as supplemental bridge inspection tools for bridges that present accessibility challenges for inspectors. To accomplish this goal, an extensive literature review and technical survey were initially conducted to gain knowledge of the state-of-the-art and practices and critical considerations that should be accounted for while conducting inspections. Also, analysis of the drones was conducted and the most suitable drone for bridge inspections was selected. To recognize the drone-enabled inspection efficiency, preliminary inspections were conducted for structural damage identification in three structures, including a reinforced masonry building and two pedestrian timber deck bridges. With the knowledge and techniques established during the preliminary inspections, a six-stage recommended bridge inspection protocol using the drone was proposed and applied to two in-service highway timber bridges, including a timber arch bridge and a three-span timber girder bridge in South Dakota. Through the acquisition and analysis of image and video data, the effectiveness of the drone platform was evaluated in terms of image quality, damage identification and quantification, and comparisons with results from traditional inspections conducted on the bridges. This study details drone-enabled inspection advantages and challenges and provides conclusions and recommendations for future work. A key finding demonstrated throughout this project was that different types of structural damage on the bridges were efficiently identified using the drone.
This research aims to develop a new bridge inspection approach using unmanned aerial vehicle (UAV) coupled with digital image correlation (DIC) system. The DIC system incorporating UAV images can measure displacements or strains by analyzing patterns of reference and deformed images. As part of this research, a commercially available UAV, DJI Matrice 210, was integrated with the DIC system using a 3D printed mounting plate, and the joint UAV-DIC system was utilized to inspect a timber bridge girder in the Structure Lab. Then, the UAV-DIC system inspected an existing timber slab bridge in Pipestone, Minnesota, but the system was not able to efficiently identify critical damage due to its instability caused by windy conditions. Therefore, only the UAV equipped with a gimbal camera was operated to perform the bridge inspection. A significant number of images from the UAV were used and analyzed through a conventional image analysis algorithm within ImageJ software for damage quantification. The major conclusion from this research was that the UAV-DIC system was only able to detect and quantify damage (i.e., crack) on the considered girder under almost zero ambient wind conditions, and the UAV integrated with the image analysis algorithm was capable of damage identification and quantification for the inspected bridge.