Moisture damage is a prevalent problem for hot-mix asphalt (HMA) pavements all over the world and the use of moisture-resistant pavement materials is critical for ensuring durable pavements. There is thus a need for the development of an accurate method of identification of moisture-susceptible mixes during laboratory mix design. The objective of this study was to develop a system of identification of poor- and good-performance HMA mixes based on artificial intelligence. The work involved stiffness and strength testing and imaging of pre- and post-conditioned mix samples that were compacted from plant-produced loose mixes with known field performance. A deep convolutional neural network (CNN) was applied to classify the moisture damage potential of the mixes based on images. As the number of samples was small, transfer learning using a standard CNN architecture (Inception V3) was used, which was pre-trained on a large-scale object identification task. The predictions from the resulting model were 88% accurate, which is higher than the accuracy of statistical analyses of the results of mechanical tests and black pixel analysis. Implementation of the proposed method in laboratory mix design can help engineers in screening poor mixes quickly and with high accuracy.

中文翻译：

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人工智能在热拌沥青混合料湿损预测中的应用

湿损是全世界热拌沥青 (HMA) 路面普遍存在的问题，使用防潮路面材料对于确保耐用路面至关重要。因此，需要开发一种在实验室混合物设计过程中识别易受潮混合物的准确方法。本研究的目的是开发一种基于人工智能的识别性能不佳和性能良好的 HMA 混合物的系统。这项工作涉及刚度和强度测试以及预处理和后处理混合样品的成像，这些样品是从具有已知现场性能的植物生产的松散混合物中压实而成的。应用深度卷积神经网络 (CNN) 对基于图像的混合物的水分破坏潜力进行分类。由于样本数量少，使用标准 CNN 架构 (Inception V3) 进行迁移学习，该架构在大规模对象识别任务上进行了预训练。所得模型的预测准确率为 88%，高于机械测试结果的统计分析和黑色像素分析的准确率。在实验室混合设计中实施所建议的方法可以帮助工程师快速、准确地筛选不良混合。