Week 0: Introduction

Bridges are essential to the transportation systems we rely on every day, yet thousands across the United States are aging and require continuous monitoring to remain safe. There are over 623,000 bridges throughout the U.S. and 6.8% of those are currently rated as “poor”. That means over 40,000 bridges are not safe enough for too much traffic, but there are 178 million trips over those bridges every day. Much of the problem is attributable to their age: over 40% of the nation’s bridges have gone over 50 years without proper renovations (ASCE Infrastructure Report Card). Most were not constructed to last that long; our expectation that a bridge will provide service for 50 to 70 years is based on their typical longevity, and yet the age of America’s bridges continues to increase.

As we continue to struggle to keep up with increasing inspection and maintenance demands, funding for bridges poses a serious problem as well. Currently, the rehabilitation costs of bridge systems are estimated at $191 billion, but even with additional federal funds, the number of bridges rated in “fair” condition exceeds the number rated in “good” condition (ASCE Infrastructure Report Card). Previously unnoticed structural deterioration could escalate from requiring routine maintenance to becoming a serious public safety concern (such as the collapse of the Fern Hollow Bridge in Pittsburgh). The National Transportation Safety Board determined that serious gaps in maintenance and oversight ultimately resulted in the bridge collapsing, even though the bridge remained open for a substantial amount of time after inspectors determined it was “safe for travel.”

As AI continues to evolve, it may play an important role in addressing this challenge sooner in the process. A number of studies have demonstrated this and have reported promising results. One study found a machine learning model to predict with 94.23% accuracy from bridge inventory data and reduced the number of bridges stated to require scheduling for inspection by nearly 30% (MDPI). This level of increased efficiency has the potential to result in transformative benefits on a larger scale.

My research will explore the application of machine learning to large scale infrastructure data and whether it will identify patterns of deterioration and/or potential structural issues before they reach critical status better than out current models. I will analyze existing data to review trends in bridge condition ratings, identify common patterns of deterioration, and look at their maintenance history so predictive models can identify structures that may need maintenance sooner than would typical engineering practices.

In addition to analyzing the data, I will also gain experience through a hands-on internship with an administrative engineer at the NYC Department of Transportation. In this role, I will witness firsthand how infrastructure inspections are conducted, and I will become familiar with how bridge data is collected and used to inform real-world maintenance decisions. This information will be critical to ensure my research is applicable to how these systems actually work in practice.

Department of Transportation: https://www.nyc.gov/html/dot/html/home/home.shtml