Optimization of surface free energy parameters for asphalt binder-aggregate system based on RBF neural network model
Optimization of surface free energy parameters for asphalt binder-aggregate system based on RBF neural network model
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DOI：
10.1016/j.conbuildmat.2022.129382
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发表时间：
2022-11
影响因子：
7.4
通讯作者：
Mengkai Sun;Bo Li;Zhao Yi;Kai Cao;A. Li;Yongning Wang
中科院分区：
文献类型：
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作者：
Mengkai Sun;Bo Li;Zhao Yi;Kai Cao;A. Li;Yongning Wang
沥青路面经常受到水损害，这降低了它提供的服务水平，并导致许多道路病害。表面自由能（SFE）理论已被证明是一种可靠的方法来评估沥青混合料的水损害。本研究的目的是使用RBF神经网络模型来优化沥青结合料-集料系统的SFE参数。本文首先计算了沥青结合料-集料体系的超临界流体参数。采用改进的沸水试验、浸水马歇尔试验和冻融劈裂试验对沥青混合料的粘结性能和水稳定性进行评价。在此基础上，采用熵权综合分析法对沥青混合料的抗水损害性能进行了全面评价。然后，以沥青混合料抗水损害的综合评价指标Wi作为RBF神经网络模型的输出层，对SFE参数进行优化。最后，通过熵权综合分析法建立优化后的SFE参数与沥青混合料抗水损害性能之间的联系，验证了RBF神经网络模型优化SFE参数的可行性。结果表明，在RBF神经网络模型优化的沥青胶凝材料-集料体系SFE参数中，集料的刘易斯碱组分和Lifshitz非极性组分对抗水损害能力起主导作用，其次是VDW粘附功和由货车范德华力形成的VDW脱粘功。在所有集料中，由石灰石组成的沥青结合料-集料体系具有最好的粘结性能。
Asphalt pavement is subject to water damage frequently, which reduces the level of service that it provides and contributes to many road diseases. The surface free energy (SFE) theory has proven to be a trustworthy approach to assess water damage in asphalt mixtures. The goal of this investigation was to optimize the SFE parameters of asphalt binder-aggregate systems using an RBF neural network model. In this research, the SFE parameters of asphalt binder-aggregate systems were calculated firstly. Then, the modified boiling water test and the immersion Marshall test, as well as the freeze–thaw splitting test were then used to assess the asphalt mixtures’ adhesive properties and water stability. Thereafter, the comprehensive analysis method of entropy weight was used to conduct a thorough evaluation of the asphalt mixtures’ resistance to water damage. Next, the comprehensive evaluation index,Wi, of the asphalt mixtures’ resistance to water damage was used as the output layer of the RBF neural network model to optimize the SFE parameters. Finally, the feasibility of the optimized the SFE parameters using RBF neural network model was verified by establishing the connection between the optimized SFE parameters and the asphalt mixtures’ resistance to water damage by the entropy weight comprehensive analysis method. The results showed that among the SFE parameters of asphalt binder-aggregate systems optimized using the RBF neural network model, the Lewis base component and the Lifshitz nonpolar component of the aggregate played a dominant role in the ability to resist water damage, followed by the VDW adhesion work and the VDW de-bonding work formed by van der Waals forces. Furthermore, the asphalt binder-aggregate system composed of limestone had the best adhesion properties among all aggregates.