Pavement demolition debris is one of the world’s major waste problems. Each year the United States produces about 100 million tons of reclaimed asphalt pavement (RAP), out of which more than 60% ends up in landfills or asphalt plants. Recent studies have shown that RAP can be considered a viable alternative to natural base course aggregates to resolve the problem of waste accumulation. In this study efforts have been made to develop an artificial neural network (ANN)-based performance predicting model for base course aggregates blended with RAP. Repeated load triaxial (RLT) tests have been employed in this study to evaluate the performance of base course aggregate. Two different RAP samples were blended with virgin aggregates (VA) in proportions of 20%, 40% and 60% and RLT tests were performed on the RAP-VA blends at three different stress conditions. The data from the laboratory test results were used to model the response of the RAP-VA blends in terms of accumulated permanent deformation against loading cycles. The ANN-based model developed in this study predicted the response of the material with an average coefficient of determination of 0.98. The results indicate that the developed ANN-based model is accurate in comparison to previously published regression models, which do not have the room to accommodate complex material properties as in the case of RAP and other recycled materials.

路面拆除碎片是世界上主要的废物问题之一。美国每年生产约 1 亿吨再生沥青路面 (RAP)，其中 60% 以上最终进入垃圾填埋场或沥青厂。最近的研究表明，RAP 可以被认为是解决废物堆积问题的天然基层集料的可行替代品。在这项研究中，已经努力开发一种基于人工神经网络 (ANN) 的性能预测模型，用于与 RAP 混合的基础课程聚合。本研究采用重复载荷三轴 (RLT) 试验来评估基层骨料的性能。两种不同的 RAP 样品与原始骨料 (VA) 以 20% 的比例混合，在三种不同的应力条件下对 RAP-VA 混合物进行了 40% 和 60% 以及 RLT 测试。来自实验室测试结果的数据用于模拟 RAP-VA 混合物在累积永久变形对加载循环的响应。本研究中开发的基于 ANN 的模型预测材料的响应，平均确定系数为 0.98。结果表明，与之前发布的回归模型相比，开发的基于 ANN 的模型是准确的，后者没有空间容纳复杂的材料特性，如 RAP 和其他回收材料的情况。本研究中开发的基于 ANN 的模型预测材料的响应，平均确定系数为 0.98。结果表明，与之前发布的回归模型相比，开发的基于 ANN 的模型是准确的，后者没有空间容纳复杂的材料特性，如 RAP 和其他回收材料的情况。本研究中开发的基于 ANN 的模型预测材料的响应，平均确定系数为 0.98。结果表明，与之前发布的回归模型相比，开发的基于 ANN 的模型是准确的，后者没有空间容纳复杂的材料特性，如 RAP 和其他回收材料的情况。