Development of petroleum-derived polymeric additive to enhance the bituminous properties with the use of a machine-learning model

Mansi Awasthi , Vedant Joshi , Rakesh Upadhyay , Aruna Kukrety , Abhay Kumar Verma , Pradeep Kumar , Kamal Kumar
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Abstract

Study revealed the synthesis of petroleum-derived polymeric additive PS-co-PAO. The synthesis of the additives was confirmed by FT-IR and NMR spectroscopy. The synthesized polymeric additives was blended with VG 10 base bitumen to modifiy its physicochemical and rheological properties. Six modified bitumens SOMB1,SOMB2, SOMB3, SOMB4, SOMB5, and SOMB6 were prepared using different concentrations of polymeric additives at 150°C and 170°C. These modified bitumens were further analyzed for the physicochemical and rheological properties which revealed that the SOMB-6 modified bitumen was found most suitable. Additionally, a Random Forest regression model was developed to predict the rutting resistance based on the percentage of polymeric additive and temperature. The model demonstrated a high R-squared value of 0.986, indicating that it can effectively predict rutting resistance, enhancing the design and testing of polymer-modified bitumen. A study on multifunctional additives also revealed that the prepared modified bitumens marginally meet the properties of higher-grade VG and modified bitumen as per IS and IRC specifications.
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利用机器学习模型开发石油衍生聚合物添加剂以增强沥青的性能
研究了石油衍生聚合物添加剂PS-co-PAO的合成。通过红外光谱和核磁共振光谱对添加剂的合成进行了验证。将合成的聚合物添加剂与VG - 10基沥青混合,以改变其物理化学和流变性能。采用不同浓度的聚合物添加剂,在150℃和170℃下分别制备了SOMB1、SOMB2、SOMB3、SOMB4、SOMB5和SOMB6 6种改性沥青。对改性沥青的理化性能和流变性能进行了进一步的分析,结果表明SOMB-6改性沥青是最合适的。此外,建立了基于聚合物添加剂百分比和温度的随机森林回归模型来预测其抗车辙性能。该模型的r平方值为0.986,表明该模型可以有效地预测聚合物改性沥青的车辙阻力,为聚合物改性沥青的设计和试验提供了依据。对多功能添加剂的研究也表明,所制备的改性沥青基本符合IS和IRC规范的高级VG和改性沥青的性能。
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