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

TL;DR

This study employs deep neural networks, specifically CNN and LSTM, to predict pavement deterioration using extensive condition and maintenance data, achieving promising results in accuracy.

Contribution

It introduces a deep learning approach combining multiple distress indicators and historical data for pavement condition prediction, outperforming traditional models.

Findings

CNN outperforms standard machine learning models in prediction accuracy.

Using 21 distress indicators improves model comprehensiveness.

Promising preliminary results demonstrate potential for pavement management.

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.