Bride Structural Inspections using Bridge Information Modeling (BrIM) and Unmanned Aerial Vehicles (UAVs)

Abstract

Bridge inspection is a critical task needed to monitor bridge quality and serviceability. In the U.S., 40 percent of bridges are more than 50 years old, while most bridges are typically designed for a lifespan of 50 years. Of the 614,387 bridges across the U.S., 9.1 percent are considered structurally deficient. These statistics reported in the literature emphasize the urgent need for more frequent and comprehensive bridge inspections. However, the current manual inspection routine is expensive, time-consuming, hazardous, and subjective. Moreover, current Bridge Management Systems (BMS) may not coordinate management of all four phases of the bridge life cycle. Also, dispersion of inspection data drastically reduces the effectiveness of these systems. Therefore, there is a need to find cost-efficient and productive ways to inspect and manage our bridges. The objective of this study was to develop a novel framework for performing bridge inspections and management. The framework implements Bridge Information Modeling (BrIM) and unmanned aerial system (UAS) technologies to solve the problems with current manual bridge inspection and management practices. The proposed framework was implemented with data collected from an existing bridge located in Eugene, Oregon. Different types of defects were identified from the digital images captured by the UAS, and cracks were detected automatically by applying computer vision algorithms to those images. The identified defects were assigned to individual BrIM elements. BrIM was used as the central database to store the 3D bridge model and all inspection data. The framework also enables bridge inspectors and decision makers to access the most up-to-date inspection data simultaneously by taking advantage of cloud computing technology. The proposed framework will (1) provide a systematic approach for collecting and accurately documenting structural condition assessment data, (2) reduce the number of site visits and eliminates potential errors resulting from data transcription, and (3) enable a more efficient, more cost-effective, and safer bridge inspection process.