Azam Amir (Regional Environment Systems Course／Doctoral course, 3rd Year）
Professor Michael Henry (Dept. of Civil Engineering)
2023 Japan Society of Civil Engineers Annual Meeting
Excellent Presentation Award
【Title of Paper】
Utilization of ANN and Markov Chain-based pavement performance models for network-level maintenance management
Purpose of the Research
This research focused on the theme of data-driven maintenance management, aiming to enhance effective maintenance strategies. Two widely used approaches, the Markov chain method and the Artificial Neural Network, were employed to develop pavement condition prediction models. Subsequently, the implications and limitations of both models were discussed from a network-level management perspective.
The research aimed to enhance maintenance strategies through data-driven methods, utilizing the Markov chain and Artificial Neural Network approaches to develop pavement condition prediction models. The study discussed the implications and limitations of these models from a network-level management perspective, providing insights for effective maintenance decision-making.
The research aimed to assist pavement engineers in deciding the most suitable approach based on their specific objectives. Given the common use of the Markov chain method and Artificial Neural Network in pavement condition prediction modeling, the research's utilization, implications, and limitations section is particularly designed to help pavement engineers understand when to employ a specific approach.