Model developments of long-term aged asphalt binders
Model developments of long-term aged asphalt binders
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DOI：
10.1016/j.conbuildmat.2012.07.047
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发表时间：
2012-12
影响因子：
7.4
通讯作者：
F. Xiao;S. Amirkhanian;C. Juang;Shaowei Hu;Junan Shen
中科院分区：
文献类型：
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作者：
F. Xiao;S. Amirkhanian;C. Juang;Shaowei Hu;Junan Shen
人工神经网络（ANN）可代替传统的物理模型来分析涉及多个变量的复杂关系，并已成功应用于土木工程应用。本研究的目的是开发一系列 ANN 模型来模拟三种沥青粘合剂（PG 64-22、碎胶沥青改性剂、PG 76-22）的长期老化，涉及七个老化变量，例如老化温度和持续时间、m 值、加压老化容器 (PAV) 样品的质量损失、高压凝胶渗透色谱 (GPC) 测试的大分子和小分子尺寸的百分比以及粘合剂刚度。结果表明，基于 ANN 的模型比回归模型更有效，并且可以轻松地在电子表格中实现，从而易于应用。结果还表明，老化温度、老化持续时间、大分子和小分子尺寸的百分比以及粘合剂刚度是所开发的用于预测长期老化过程后针入度指数的 ANN 模型中最重要的因素。
Artificial neural networks (ANNs) are useful in place of conventional physical models for analyzing complex relationship involving multiple variables and have been successfully used in civil engineering applications. The objective of this study was to develop a series of ANN models to simulate the long-term aging of three asphalt binders (PG 64-22, crumb rubberized asphalt modifier, PG 76-22) regarding seven aging variables such as aging temperature and duration, m-value, mass loss of pressurized aging vessel (PAV) samples, percentages of large and small molecular sizes of high pressure-gel permeation chromatographic (GPC) testing, and binder stiffness. The results indicated that ANN-based models are more effective than the regression models and can easily be implemented in a spreadsheet, thus making it easy to apply. The results also show that the aging temperature, aging duration, percentage of large and small molecular sizes, and binder stiffness are the most important factors in the developed ANN models for prediction of penetration index after a long-term aging process.