Tendon corrosion of grouted internal tendons in precast segmental bridges is reviewed, with an emphasis on the role of grouting in the corrosion protection of posttensioning. An analysis and compilation of the available information on the subject was conducted as part of an NCHRP research study (NCHRP Project 20-7, Task 92) with emphasis on those structures located in the United States and the United Kingdom. It was determined that the corrosion problems with precast segmental and posttensioned bridges in the United Kingdom principally were due to poor design choices and poor quality construction, not a significant intrinsic susceptibility to corrosion. The study found no evidence of corrosion or other durability problems with precast segmental bridges in the United States, although it must be recognized that this conclusion is based on reported information primarily gathered by visual inspection. Similar to the experience in the United Kingdom, the few reported problems with grouted posttensioned structures in the United States were due to poor quality construction. Additionally, laboratory research indicates that properly designed and constructed precast segmental and posttensioned structures are corrosion resistant. To ensure good future performance of precast segmental bridges, high-quality design details and proper construction practices must be continued, especially when using internal tendons. It is important to adopt more stringent grouting requirements and inspection procedures in the United States, similar to what has been done in the United Kingdom, to ensure that the tendons are completely encapsulated in a high-quality grout.
The purpose of a routine bridge inspection is to assess the physical and functional condition of a bridge according to a regularly scheduled interval. The Federal Highway Administration (FHWA) requires these inspections to be conducted at least every 2 years. Inspectors use simple tools and visual inspection techniques to determine the conditions of both the elements of the bridge structure and the bridge overall. While in the field, the data is collected in the form of images and notes; after the field work is complete, inspectors need to generate a report based on these data to document their findings. The report generation process includes several tasks: (1) evaluating the condition rating of each bridge element according to FHWA Recording and Coding Guide for Structure Inventory and Appraisal of the Nation’s Bridges; and (2) updating and organizing the bridge inspection images for the report. Both of tasks are time-consuming. This study focuses on assisting with the latter task by developing an artificial intelligence (AI)-based method to rapidly organize bridge inspection images and generate a report. In this paper, an image organization schema based on the FHWA Recording and Coding Guide for the Structure Inventory and Appraisal of the Nation’s Bridges and the Manual for Bridge Element Inspection is described, and several convolutional neural network-based classifiers are trained with real inspection images collected in the field. Additionally, exchangeable image file (EXIF) information is automatically extracted to organize inspection images according to their time stamp. Finally, the Automated Bridge Image Reporting Tool (ABIRT) is described as a browser-based system built on the trained classifiers. Inspectors can directly upload images to this tool and rapidly obtain organized images and associated inspection report with the support of a computer which has an internet connection. The authors provide recommendations to inspectors for gathering future images to make the best use of this tool.
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