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A soft computing approach to predict and evaluate asphalt mixture aging characteristics using asphaltene as a performance indicator

机译：使用沥青质作为性能指标的软计算方法来预测和评估沥青混合料的老化特性

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Prediction of long-term asphalt mixture aging using fundamental characteristics of asphalt binders and mixtures is a complex task. Asphaltene has been reported as one of the major chemical components of asphalt cement binder. Several research studies have established asphaltene content as the fundamental characteristic ingredient present in the asphalt binders required to understand aging-related performance. Dynamic complex modulus (|E*|) is recognized as the paramount performance response parameter for asphalt mixtures, routinely used in pavement design and evaluation exercises. Hence, there is a definitive need to develop mixture aging predictive models using asphaltene content as the fundamental parameter with its effect on the resulting |E*| performance of asphalt mixtures. The objective of this research study was to develop asphalt mixture aging predictive models with asphaltene content as a fundamental performance parameter, using soft computing techniques. Asphalt binders and corresponding asphalt mixtures were subjected to short- and long-term aging conditions. Asphaltene contents and rheological properties were measured for different asphalt binders. Volumetric properties and |E*| were conducted for corresponding asphalt mixtures. Artificial Neural Network (ANN) method was employed to develop a rational model for evaluating asphalt mixture aging behavior considering asphaltene content values from asphalt binders. A total of seven different dense-graded asphalt mixtures with virgin, polymer-, and rubber-modified binders with two different asphalt contents were produced for experimentation purposes. The results showed that the predictive model developed using the ANN approach provided a robust relationship with asphaltene aging indices, a fundamental asphalt property used to quantify asphalt mixture properties at various aging conditions.
机译：利用沥青粘合剂和混合物的基本特征预测沥青混合物的长期老化是一项复杂的任务。据报道沥青质是沥青水泥粘合剂的主要化学成分之一。多项研究已确定沥青质含量为了解老化相关性能所需的沥青粘合剂中的基本特征成分。动态复数模量（| E * |）被认为是沥青混合料的最重要性能响应参数，通常在路面设计和评估练习中使用。因此，迫切需要开发一种以沥青质含量为基本参数的混合物老化预测模型，并对其结果| E * |产生影响。沥青混合料的性能。这项研究的目的是使用软计算技术开发以沥青质含量为基本性能参数的沥青混合料老化预测模型。沥青粘合剂和相应的沥青混合物要经受短期和长期老化条件。测量了不同沥青粘合剂的沥青含量和流变性能。体积特性和| E * |对相应的沥青混合物进行了测试。人工神经网络（ANN）方法被用来建立一个合理的模型来评估沥青混合料的老化性能，其中要考虑沥青粘结剂中沥青质的含量值。为了实验目的，共生产了七种不同的高密度沥青混合料，其中含有两种不同沥青含量的原始，聚合物和橡胶改性的粘结剂。结果表明，使用ANN方法开发的预测模型与沥青质老化指数具有稳健的关系，沥青质老化指数是用于量化各种老化条件下沥青混合料性能的基本沥青性质。

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