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AI and Deterioration Modeling
AI is coming – are we ready? With the MAP-21/FAST Act legislation, and the renewed emphasis on Transportation Asset Management Plans (TAMPs), projections made by management systems will come under increasing scrutiny as agency executive leadership is asked to make large scale funding decisions based on these projections. This scrutiny as well as the inherent complications in predictive modeling of asset deterioration, presents an opportunity for the use of Artificial Intelligence (AI) in this type of analysis.
AI is becoming ubiquitous in the realm of automation and pattern recognition and shows promise in improving predictive modeling for infrastructure managed by highway agencies. Because data collected over time is especially valuable for deterioration modeling, it is very important for agencies to start collecting the right data, and putting in place the right quality control, as early as possible so that this data is ready for immediate use as more research into AI techniques for predictive modeling is conducted.
Literature Search Summary
This research project would aim to develop a Primer or Guidance document to help agencies tasked with managing infrastructure (including pavement and bridges) to assess their current data, data collection processes, and data needs to best position them to be able to take advantage of burgeoning artificial intelligence techniques to develop increasingly accurate predictive models regarding their infrastructure.