Deep learning-based pavement performance modeling using multiple distress indicators and road work history

2026-05-03

AI Summary

This study proposes a deep learning model integrating convolutional neural networks (CNNs) and long short-term memory (LSTM) networks to enable high-accuracy prediction of pavement performance deterioration, thereby optimizing maintenance resource allocation. The approach uniquely combines 21 multidimensional pavement distress indicators with up to 18 years of maintenance history data, effectively capturing both spatial characteristics and temporal evolution patterns. Evaluated on a large-scale dataset encompassing over 100,000 road segments, the proposed model significantly outperforms conventional machine learning methods in predicting pavement condition indices. These results demonstrate the model’s practical utility and innovation in advancing intelligent infrastructure management and maintenance strategies.

Abstract

The deterioration of pavement is a complex and dynamic process determined by different factors including material, environment, design, and some other unobserved variables. Accurate predictions of pavement condition can help maximize the use of available resources for pavement management agencies through better coordinated preservation and maintenance activities. This paper uses deep neural networks such as the convolutional neural network (CNN) and the long short-term memory (LSTM) to model the pavement deterioration process. In this paper, pavement condition data and maintenance and rehabilitation history collected by the Texas Department of Transportation over the past 18 years were used. Twenty-one flexible pavement condition indicators, including cracking, rutting, raveling, and roughness, collected from more than 100,000 pavement sections were included in the proposed models. Promising preliminary results were obtained. Case study results show that the proposed CNN model outperforms standard machine learning models in predicting pavement condition values.

Problem

• pav日晚间 performance modeling
• pavement deterioration
• distress indicators
• road work history
• deep learning

Innovation

• deep learning
• convolutional neural network (CNN)
• long short-term memory (LSTM)
• pavement performance modeling
• distress indicators