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.

翻译：路面劣化是一个受材料、环境、设计及其他未观测变量共同影响的复杂动态过程。准确预测路面状况有助于路面管理机构通过更协调的预防性养护和维修活动，最大化利用可用资源。本文采用卷积神经网络(CNN)和长短期记忆网络(LSTM)等深度神经网络对路面劣化过程进行建模。研究使用了美国德克萨斯州交通部过去18年收集的路面状况数据及养护与修复记录，模型包含从超过10万个路面段采集的裂缝、车辙、松散和粗糙度等21项柔性路面状况指标。初步结果令人鼓舞。案例研究表明，所提出的CNN模型在预测路面状况值方面优于标准机器学习模型。