Proceedings of the 2017 3rd International Forum on Energy, Environment Science and Materials (IFEESM 2017)

Viscoelastic Parameter Identification of Asphalt Mixture Based on 1stOpt Method

Authors
Fuyu Wang, Haibin Wei, Ziqi Li
Corresponding Author
Fuyu Wang
Available Online February 2018.
DOI
10.2991/ifeesm-17.2018.313How to use a DOI?
Keywords
asphalt mixture,1stOpt software, Burgers model.
Abstract

At present, commonly used methods to identify Burgers model parameters have subjectivity shortcomings. This paper adopts 1stOpt software with unique nonlinear fitting function. The stress-strain-time equation can be obtained according to constitutive equation of Burgers model. Then four parameters of the Burgers model can be obtained by fitting the experimental data directly. Compare with the parameters precision obtained by the subsection fitting method and Lsqcurvefit method and the results show that 1stOpt method has high recognition precision and accuracy.

Copyright
© 2018, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of the 2017 3rd International Forum on Energy, Environment Science and Materials (IFEESM 2017)
Series
Advances in Engineering Research
Publication Date
February 2018
ISBN
978-94-6252-453-8
ISSN
2352-5401
DOI
10.2991/ifeesm-17.2018.313How to use a DOI?
Copyright
© 2018, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Fuyu Wang
AU  - Haibin Wei
AU  - Ziqi Li
PY  - 2018/02
DA  - 2018/02
TI  - Viscoelastic Parameter Identification of Asphalt Mixture Based on 1stOpt Method
BT  - Proceedings of the 2017 3rd International Forum on Energy, Environment Science and Materials (IFEESM 2017)
PB  - Atlantis Press
SP  - 1721
EP  - 1726
SN  - 2352-5401
UR  - https://doi.org/10.2991/ifeesm-17.2018.313
DO  - 10.2991/ifeesm-17.2018.313
ID  - Wang2018/02
ER  -