Machine Learning-Based Air Gap Prediction for Safe Vessel Passage Under Bridge

The Halifax MacKay Bridge is a critical passage for maritime traffic, requiring accurate air gap predictions to ensure safe vessel clearance. This project will develop a machine learning-based forecasting model that estimates air gap values at 30-minute, 90-minute, and 72–96-hour intervals. Using data from the Port of Halifax, including tide levels, weather conditions, and traffic loads, the project will create an AI model to assist port authorities in decision-making.

DeepSense will provide cloud infrastructure for model training, while OMC International and the Halifax Port Authority will facilitate an operational environment for deployment. The project’s conservative forecasting approach will prioritize safety by ensuring the predicted air gap is never overestimated. This research will enhance maritime safety, benefiting Canada’s shipping industry and the broader transportation sector.

Faculty Supervisor:

Christopher Whidden

Student:

Partner:

OMC International;Halifax Port Authority

Discipline:

Computer science

Sector:

Professional, scientific and technical services

University:

Dalhousie University

Program: