You are encouraged to attend the defence. The details of the defence and attendance information is included below:  

Date:  September 26, 2025

Time:  9:00 AM - 11:00 AM (PT)

Defence mode: Hybrid

In-Person Attendance: Senate Chambers, UNBC Prince George Campus  

Virtual Attendance: via Zoom 

Please contact the Office of Graduate Administration for information regarding remote attendance for online defences. 

To ensure the defence proceeds with no interruptions, please mute your audio and video on entry and do not inadvertently share your screen. The meeting will be locked to entry 5 minutes after it begins: please ensure you are on time.  

Thesis/Dissertation entitled:  OPTIMIZATION OF PAVEMENT MAINTENANCE AND REHABILITATION USING PAVEMENT MANAGEMENT SYSTEM IN PRINCE GEORGE

Abstract: 

Pavement Management Systems (PMS) are essential for guiding cost-effective and sustainable road maintenance, particularly in municipalities operating within harsh climates and under financial constraints. This research examines the optimization of pavement maintenance strategies for the City of Prince George, British Columbia, by combining historical condition data, predictive modeling, and decision-support frameworks. The study utilizes pavement distress survey results from 2016, 2017, 2020, and 2023 to assess network-level deterioration, identify critical distress types, and establish performance baselines.

To forecast pavement performance, three modeling approaches—Random Forest (RF), Multiple Linear Regression (MLR), and Artificial Neural Networks (ANN)—were applied to predict the Pavement Distress Index (PDI). These models were evaluated using Mean Squared Error (MSE), Root Mean Square Error (RMSE), and coefficient of determination (R²). The Random Forest model achieved the highest predictive accuracy (R² = 0.9616, RMSE = 0.55), followed closely by the ANN (R² = 0.9511, RMSE = 0.48), while the MLR model demonstrated lower predictive capability (R² = 0.8165, RMSE = 0.92). Variable importance analysis identified transverse cracking, rutting, and surface roughness as the most influential predictors of deterioration.

The findings of this research provide a data-driven framework for proactive pavement maintenance planning in Prince George, enabling the prioritization of high-impact interventions and the optimization of rehabilitation budgets. By extending pavement service life and reducing long-term maintenance costs, the proposed methodology supports the creation of more resilient transportation infrastructure. The framework can be adapted for use in other municipalities facing similar environmental and operational conditions, strengthening the integration of advanced analytics into municipal asset management practices.

Defence Committee:  

Chair: Dr. Annie Booth, University of Northern British Columbia 

Supervisor: Dr. Mohab El-Hakim, University of Northern British Columbia 

Committee Member: Dr. Siraj Ul Islam, University of Northern British Columbia

Committee Member: Dr. Chichu Cherian, University of Northern British Columbia

External Examiner: Dr. Leila Hashemian, University of Alberta