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dc.contributor.authorJi, Bongjun ( )
dc.contributor.authorLee, Soon-Jae ( Orcid Icon 0000-0003-4185-6983 )
dc.contributor.authorMazumder, Mithil ( )
dc.contributor.authorLee, Moon-Sup ( Orcid Icon 0000-0002-2543-6981 )
dc.contributor.authorKim, Hyun Hwan ( Orcid Icon 0000-0001-5605-003X )
dc.date.accessioned2021-07-26T14:21:13Z
dc.date.available2021-07-26T14:21:13Z
dc.date.issued2020-12-16
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.en_US
dc.identifier.issn1996-1944
dc.identifier.urihttps://digital.library.txstate.edu/handle/10877/14071
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.en_US
dc.formatText
dc.format.extent17 pages
dc.format.medium1 file (.pdf)
dc.language.isoenen_US
dc.publisherMultidisciplinary Digital Publishing Institute
dc.sourceMaterials, 2020, Vol. 13, No. 24, Article 5738.
dc.subjectDeep learning modelen_US
dc.subjectRegression architectureen_US
dc.subjectAtomic force microscopyen_US
dc.subjectStyrene-isoprene-styreneen_US
dc.subjectDynamic shear rheometeren_US
dc.titleDeep Regression Prediction of Rheological Properties of SIS-Modified Asphalt Bindersen_US
dc.typepublishedVersion
txstate.documenttypeArticle
dc.rights.holder© 2020 The Authors.
dc.identifier.doihttps://doi.org/10.3390/ma13245738
dc.rights.licenseCreative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
dc.description.departmentEngineering Technology


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