Graduation Semester and Year
Summer 2025
Language
English
Document Type
Dissertation
Degree Name
Doctor of Philosophy in Civil Engineering
Department
Civil Engineering
First Advisor
Nur Yazdani
Second Advisor
Kate Hyun
Third Advisor
Suyun Paul Ham
Fourth Advisor
Mohammad Atiqul Islam
Abstract
The deterioration of bridge elements arises from multiple complex factors such as reinforcement corrosion, concrete degradation, creep, shrinkage, cracking, and fatigue. In the absence of mechanistic models that quantitatively account for these factors, along with environmental influences and maintenance constraints, transportation agencies rely on inspection data to assess rehabilitation, or replacement needs and prioritize maintenance activities. This Ph.D. research addresses these challenges by developing predictive models to forecast bridge deck condition ratings and load ratings using machine learning algorithms. The study leverages historical data from the National Bridge Inventory (NBI) and environmental datasets, incorporating climatic zone classifications from NOAA. Supervised machine learning models, including decision tree, k-nearest neighbors, support vector machines, random forest, Adaboost, CatBoost, and Bagged Tree, were applied to predict future deck condition ratings. Additional data from the Long-Term Bridge Performance (LTBP) program were combined with the bridge features to capture both structural and environmental impacts on deterioration. Since bridge records contain both temporal and spatial data, deep learning approaches such as vanilla and stacked Long Short-Term Memory (LSTM) networks (vLSTM, sLSTM) were employed to capture temporal deterioration trends. Additionally, a Random Forest model was developed to estimate bridge load ratings, integrating diagnostic load test data to improve accuracy beyond inspection-based methods. Results demonstrated high predictive performance across models, highlighting critical features influencing deterioration and enabling data-driven decision-making for bridge safety and maintenance prioritization.
Keywords
Predictive model, Machine learning, Artificial intelligence (AI), Deep learning, Neural networks, Load Rating
Disciplines
Structural Engineering
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Recommended Citation
Morshed, Syed Sadat, "MACHINE LEARNING APPROACH TO PREDICT BRIDGE DECK CONDITION RATING AND LOAD RATING CONSIDERING ENVIRONMENTAL FACTORS" (2025). Civil Engineering Dissertations. 524.
https://mavmatrix.uta.edu/civilengineering_dissertations/524