Week 7
May 15, 2026
The most significant accomplishment of this week would have to be developing a full outline for the research paper after meeting with my placement mentor. The outline of the research has made me realize that I would like to expand my focus just beyond structural monitoring. There is tremendous potential for AI in bridge maintenance, beyond just crack and corrosion detection; areas such as data organization and management of maintenance records, inspection scheduling, and formatting and disseminating information to the appropriate parties. I will also explore all of these areas in the research paper.
In addition to the initial project, I am going to do a second case study of the Pulaski Bridge’s Jackson Avenue approach span (B.I.N. 2240639) in Queens, which connects 11th Street (in Queens) to McGuinness Boulevard (in Brooklyn) over Newtown Creek. The bridge has six travel lanes, as well as a pedestrian walkway; it terminates at a traffic light at Jackson Avenue (in Queens), subsequently continuing as 11th Street. This section of the bridge (Jackson Avenue) consists of fixed, non-movable approach span structures, meaning that there are no drawbridge mechanisms (e.g., counterweights or hoists) involved in its design/operation. Essentially, this structure is about as simple as a bridge can be built structurally. The NYC Department of Transportation (DOT) has rated this section of the Pulaski Bridge a fair rating of 5.2 NBI-W according to the 2024 Annual Condition Report.
For this reason, I am establishing it in my report. The Third Street Bridge is a classic example of a bridge that has a long history and complex mechanical elements (the bridge is over 120 years old and is located in a Superfund environment). This offers a more complex and potentially more environmentally impactful situation to study. In contrast, the Jackson Avenue Bridge is a high-volume, fixed-span, standard bridge that has few mechanical variables, making it a much simpler example from which to demonstrate how AI-based deterioration modeling can be performed easily and consistently. This combination of case studies allows the resulting research to have significant variability and produces a final model more generally useful than the individual case studies on their own.
Work Cited:
East Coast Roads. “Pulaski Bridge.” East Coast Roads, www.eastcoastroads.com/states/ny/nyc/pulaski.
Michael Minn. “The Gowanus Canal: Third Street.” michaelminn.net, michaelminn.net/newyork/mobility/gowanus-canal/third-street/index.html.
New York City Department of Transportation. Bridges and Tunnels Annual Condition Report, 2024 Edition. NYCDOT Division of Bridges, 2024.
NYC DOT. “The Bridges of Newtown Creek.” NYC.gov, www.nyc.gov/html/dot/html/infrastructure/bridges-newtown.shtml.
Wikipedia. “Pulaski Bridge.” Wikipedia, en.wikipedia.org/wiki/Pulaski_Bridge.
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Pairing the Third Street Bridge with something as straightforward as the Jackson Avenue fixed span is a really smart move, Philip. I think it gives your model range that is going to make it applicable to cases beyond what you’re looking at. Expanding the scope from crack and corrosion detection into inspection scheduling and maintenance is also a good move, it will increase the roles AI could fill with regards to bridge infrastructure. Great work!