Deep Regression Prediction of Rheological Properties of SIS-Modified Asphalt Binders

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dc.contributor.authorJi, Bongjun
dc.contributor.authorLee, Soon-Jae
dc.contributor.authorMazumder, Mithil
dc.contributor.authorLee, Moon-Sup
dc.contributor.authorKim, Hyun Hwan
dc.date.accessioned2021-07-26T14:21:13Z
dc.date.available2021-07-26T14:21:13Z
dc.date.issued2020-12-16
dc.description.abstractThe engineering properties of asphalt binders depend on the types and amounts of additives. However, measuring engineering properties is time-consuming, requires technical expertise, specialized equipment, and effort. This study develops a deep regression model for predicting the engineering property of asphalt binders based on analysis of atomic force microscopy (AFM) image analysis to test the feasibility of replacing traditional measuring estimate techniques. The base asphalt binder PG 64-22 and styrene–isoprene–styrene (SIS) modifier were blended with four different polymer additive contents (0%, 5%, 10%, and 15%) and then tested with a dynamic shear rheometer (DSR) to evaluate the rheological data, which indicate the rutting properties of the asphalt binders. Different deep regression models are trained for predicting engineering property using AFM images of SIS binders. The mean absolute percentage error is decisive for the selection of the best deep regression architecture. This study’s results indicate the deep regression architecture is found to be effective in predicting the G*/sin δ value after the training and validation process. The deep regression model can be an alternative way to measure the asphalt binder’s engineering property quickly. This study would encourage applying a deep regression model for predicting the engineering properties of the asphalt binder.
dc.description.departmentEngineering Technology
dc.formatText
dc.format.extent17 pages
dc.format.medium1 file (.pdf)
dc.identifier.citationJi, B., Lee, S. J., Mazumder, M., Lee, M. S., & Kim, H. H. (2020). Deep regression prediction of rheological properties of SIS-modified asphalt binders. Materials, 13(24), 5738.
dc.identifier.doihttps://doi.org/10.3390/ma13245738
dc.identifier.issn1996-1944
dc.identifier.urihttps://hdl.handle.net/10877/14071
dc.language.isoen
dc.publisherMultidisciplinary Digital Publishing Institute
dc.rights.holder© 2020 The Authors.
dc.rights.licenseThis work is licensed under a Creative Commons Attribution 4.0 International License.
dc.sourceMaterials, 2020, Vol. 13, No. 24, Article 5738.
dc.subjectdeep learning model
dc.subjectregression architecture
dc.subjectatomic force microscopy
dc.subjectstyrene-isoprene-styrene
dc.subjectdynamic shear rheometer
dc.subjectEngineering Technology
dc.titleDeep Regression Prediction of Rheological Properties of SIS-Modified Asphalt Binders
dc.typeArticle

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