用深度学习预测路面病害，结合历史维修数据提升养护效率
Deep learning-based pavement performance modeling using multiple distress indicators and road work history
- 融合21类病害指标与18年维修历史，用CNN和LSTM建模路面退化
- 在超10万段路面数据上验证，CNN模型预测精度优于传统机器学习
- 适合道路管理机构做养护决策，尤其关注长期性能预测的场景
路面老化是一个受材料、环境、设计及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公路管理部门更高效地配置资源，协调预防性养护与维修工作。本文采用卷积神经网络（CNN）和长短期记忆网络（LSTM）等深度神经网络模型，基于德克萨斯州交通部过去18年收集的路面状况数据与养护修复历史进行建模。模型纳入了超过10万个路面路段的数据，涵盖21种柔性路面状况指标，包括裂缝、车辙、剥落和不平顺度等。初步结果表明，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。
原文摘要 · Abstract (English)
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.
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