Abstract
Abstract. Bridge inspection is a critical task in infrastructure management and is facing unprecedented challenges after a series of bridge failures. The prevailing visual inspection was insufficient in providing reliable and quantitative bridge information although a systematic quality management framework was built to ensure visual bridge inspection data quality to minimize errors during the inspection process. The LiDAR based remote sensing is recommended as an effective tool in overcoming some of the disadvantages of visual inspection. In order to evaluate the potential of applying this technology in bridge inspection, some of the error sources in LiDAR based bridge inspection are analysed. The scanning angle variance in field data collection and the different algorithm design in scanning data processing are the found factors that will introduce errors into inspection results. Besides studying the errors sources, advanced considerations should be placed on improving the inspection data quality, and statistical analysis might be employed to evaluate inspection operation process that contains a series of uncertain factors in the future. Overall, the development of a reliable bridge inspection system requires not only the improvement of data processing algorithms, but also systematic considerations to mitigate possible errors in the entire inspection workflow. If LiDAR or some other technology can be accepted as a supplement for visual inspection, the current quality management framework will be modified or redesigned, and this would be as urgent as the refine of inspection techniques.
Highlights
Visual inspection is currently the prevailing method performed for highway bridge inspection even since a series of bridge failures which challenged the current bridge safety monitoring program (Brinckerhoff, 1993 and Subramanian, 2008)
The inspection team needs to walk to the site and observe the bridge conditions and find out bridge defects following the federal or state bridge inspection procedures, and input the inspection data into the bridge management software (BMS)
The primary error sources are assessed through designed experiments, and the purpose of the analysis is to understand the issues that affect bridge inspection data quality in both field bridge inspection and computer assisted data processing for LiDAR bridge inspection
Summary
Visual inspection is currently the prevailing method performed for highway bridge inspection even since a series of bridge failures which challenged the current bridge safety monitoring program (Brinckerhoff, 1993 and Subramanian, 2008). Improving bridge inspection data quality is important, because it is the foundation of all effective bridge management operations (Dietrich et al, 2005 and Moore, et al, 2001). Bridge inspection data is essential in determining how to perform bridge maintenance, repairs, rehabilitations and replacement of a bridge. FHWA has adopted systematic quality management framework to ensure visual bridge inspection data quality though minimizing errors during data generation (FHWA, 2005). The framework recommends documenting the entire quality control (QC) and quality assurance (QA) program, and developing the recommended bridge inspection manual . The specific directions for QC/QA operations of bridge inspection are designed to guarantee data quality within the framework. 1 shows the recommendation for data quality management within the FHWA framework
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
