8. Conclusion and Future Work

8. Conclusion and Future Work

In this project, a novel two-stage bridge deck inspection framework has been proposed. In the first stage, a vision-based fast inspection using UAV is proposed. Deep learning-based methods have been applied to develop an automated vision-based data interpretation system. In the second stage, multiple NDE techniques are proposed to conduct in-depth inspections. Statistical analysis for individual NDE data has been provided. A novel multi-resource NDE data fusion method has been developed. The feasibility of the proposed methods has been evaluated using data collected from the first full-scale bridge specimen, the BEAST. In addition, a UAV data collection strategy has been studied in this project. More importantly, the dissemination, integration, and interpretation of BEAST data has been conducted to correlate data from the BEAST with real bridges. Finally, the cost-benefit analysis of the proposed framework has been discussed.

This study was conducted based on data collected only from the BEAST. Therefore, more work needs to be done in the future to verify the feasibility of the proposed methods. Several future work directions are recommended:

1. For vision-based methods, more data needs to be collected from different bridges to ensure the generalization of developed models. 2. For the drone data collection strategy, more rounds of data collection need to be conducted to draw a more reliable conclusion. 3. For multi-resource data fusion, the proposed methods need to be applied to more data collected from different bridges to further verify the feasibility. 4. It is important to develop vision-based methods capable of crack width determination.

The proposed deep learning and image processing methods can be tuned later to extract crack pixels and a crack midline (i.e., crack skeleton). This can have additional cost for the state if results can be translated into element condition states.

In addition, the BEAST is built with black rebar and normal strength concrete. The results obtained in this project can only be references for bridges similar to the BEAST. In the future, more work should be done to verify the feasibility of the proposed methods on other types of bridges. Several future work directions are recommended: 1. Retrofit the BEAST with epoxy coated rebar and high strength concrete and collect more data. 2. Build a new specimen similar to the BEAST using current design standards to collect more data. 3. Deploy the proposed methods to a number of actual bridges to collect more data.

Acknowledgement

The research reported on in this paper was conducted under a project sponsored by the IRISE public/private research consortium. At the time of publication, the consortium included the Pennsylvania Department of Transportation, the Federal Highway Administration (ex officio), Allegheny County, the Pennsylvania Turnpike Commission, Golden Triangle Construction and Michael Baker International. IRISE was established in the Civil and Environmental Engineering Department in the University of Pittsburgh’s Swanson School of Engineering to study problems related to transportation infrastructure durability and resiliency. More information on IRISE can be found at: https://www.engineering.pitt.edu/irise/. Special thanks to Sun Ho Ro, the graduate student at University of Rutgers, for helping with data collection and processing.

Disclaimer

The contents of this report reflect the views of the authors who are responsible for the facts and the accuracy of the data presented herein. The contents do not necessarily reflect the official views or policies of any member of the IRISE research consortium at the time of publication. This report does not constitute a standard, specification, or regulation.