Azam Amirさんが土木学会全国大会第78回年次学術講演会にて優秀講演者を受賞

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.

【研究内容】

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.

 【今後の展望】

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.