This research targets a very low-cost Artificial intelligence (AI) based overheight vehicle warning system for bridges based on the use of cutting-edge camera technology, augmented reality and AI based height detection approach.
The purpose of this study is to develop and implement an analytical framework to calculate deterioration rates for bridges and large culverts based AASHTO-Element inspection data as well as NBI data and demonstrate the application of the approach through currently available inspection data. This analytical approach will be applied to generate deterioration rates for NYS bridges based on, but not limited to climate and/or geographical location, DOT Region, bridge ownership, material types, design types, and bridge types. The outcome of the research will be further implemented in the AASHTO BrM and the Agile Assets Structures Manager and Bridge Analyst.
This project focuses on the feasibility of the proposed EMEH to power sensors that are used to regularly monitor the structural integrity of materials and components of highway bridges such as acceleration and temperature sensors.