Predicting Concrete Pavement Condition for Sustainable Management: Unveiling the Development of Distresses through Machine Learning

Donghyuk Jung, Jinhyuk Lee (), Cheolmin Baek, Deoksoon An and Sunglin Yang
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Donghyuk Jung: Department of Highway & Transportation Research, Korea Institute of Civil Engineering and Building Technology, Goyang-daero 283, Ilsanseo-gu, Goyang-si 10223, Republic of Korea
Jinhyuk Lee: Department of Structural Engineering Research, Korea Institute of Civil Engineering and Building Technology, Goyang-daero 283, Ilsanseo-gu, Goyang-si 10223, Republic of Korea
Cheolmin Baek: Department of Highway & Transportation Research, Korea Institute of Civil Engineering and Building Technology, Goyang-daero 283, Ilsanseo-gu, Goyang-si 10223, Republic of Korea
Deoksoon An: Department of Highway & Transportation Research, Korea Institute of Civil Engineering and Building Technology, Goyang-daero 283, Ilsanseo-gu, Goyang-si 10223, Republic of Korea
Sunglin Yang: Department of Highway & Transportation Research, Korea Institute of Civil Engineering and Building Technology, Goyang-daero 283, Ilsanseo-gu, Goyang-si 10223, Republic of Korea

Sustainability, 2024, vol. 16, issue 2, 1-14

Abstract: This study presents a machine learning model for predicting representative surface distresses (crack, durability, patching, joint spall) in concrete pavements, focusing on South Korean examples. It thoroughly analyzes specific distress types using time series data to understand their development over time, aiming to surpass traditional regression methods in forecasting pavement conditions. The research fills a gap by applying machine learning algorithms to detailed long-term data, enhancing the accuracy of distress progression predictions, which is crucial for efficient pavement management. A notable aspect of this study is the use of particle filtering, recognized for its effective resampling in analyzing time series data. To validate predictions, we compared the results from particle filtering with those from traditional regression models, long short-term memory (LSTM) networks, and Deep Neural Networks (DNNs). The accuracy varied significantly, with differences ranging from 3.32% to 23.64%, indicating particle filtering’s suitability for time-series-based pavement condition predictions. These findings are especially relevant in the context of current image-based machine learning and AI research in pavement distress detection and prediction. This research offers a comprehensive reference that is especially valuable due to the lack of studies using long-term usage data, thereby making a significant contribution to pavement management research and practice.

Keywords: pavement condition index; surface distress; time series analysis; concrete pavement; prediction of distress amount; particle filtering (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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