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

• Published in: Materials Vol. 13; no. 24; p. 5738
• Main Authors: Ji, Bongjun, Lee, Soon-Jae, Mazumder, Mithil, Lee, Moon-Sup, Kim, Hyun Hwan
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 16-12-2020; MDPI
• Subjects: Additives; Asphalt; Asphalt pavements; Atomic force microscopy; Binders (materials); Deep learning; deep learning model; dynamic shear rheometer; Image analysis; Isoprene; Laboratories; Nanoparticles; Polymers; Properties (attributes); Regression analysis; regression architecture; Regression models; Rheological properties; Rheology; Styrenes; styrene–isoprene–styrene; Topography; Viscoelasticity; deep learning model; dynamic shear rheometer; styrene–isoprene–styrene; atomic force microscopy; regression architecture
• ISSN: 1996-1944; 1996-1944
• DOI: 10.3390/ma13245738

The 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
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.