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Neural network approach for GO-modified asphalt properties estimation

Thi Hoang University of Transport Technology, Hanoi, 100000, Viet Nam|
Hai-Bang (55992422600) | Hoang-Long (57304749800); Ly | Thuy-Anh (57216703469); Nguyen | Huong-Giang (57953061300); Nguyen |

Case Studies in Construction Materials Số , năm 2022 (Tập 17, trang -)

ISSN: 22145095

ISSN: 22145095

DOI: 10.1016/j.cscm.2022.e01617

Tài liệu thuộc danh mục:



Từ khóa: Asphalt; Ductility; Graphene; Intelligent systems; Monte Carlo methods; Neural networks; Viscosity; Artificial neural network modeling; Asphalt property; Development process; Graphene oxides; Input variables; Modified asphalts; Neural-networks; Property; Property estimation; Softening points; Sensitivity analysis
Tóm tắt tiếng anh
This paper presents an innovative development process of Artificial Neural Network (ANN) to predict four properties of Graphene Oxide (GO) modified asphalt, including penetration, softening point, ductility, and viscosity. To this goal, a GO-modified asphalt database is carefully constructed and divided into 4 subsets, using input variables related to GO characteristics, mixing procedure, aging type, and properties of the initial asphalt before being modified. The model training and selection process is then conducted with random sampling techniques via Monte Carlo simulation to ensure the models’ reliability and generalizability. The results show that the selected ANN models have high performance and accuracy, with a coefficient of determination (R2) = 0.994, 0.996, 0.999, and 0.983, for penetration, softening point, ductility, and viscosity dataset, respectively. In addition, sensitivity analysis is used to evaluate the influence of input variables on the 4 properties. The findings, in good agreement with experimental results, reveal that 2 input variables, namely aging type and corresponding properties of the initial asphalt, have the most influence on the predictability of ANN models. Overall, with verified sensitivity analysis and high prediction accuracy, the proposed models could be used by material engineers to avoid costly and time-consuming experiments. © 2022 The Authors

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