Moisture Damage Modeling in Lime and Chemically Modified Asphalt at Nanolevel Using Ensemble Computational Intelligence
Joint Authors
Mamun, A. A.
Arifuzzaman, Md
Hassan, M. R.
Hossain, M. I.
Source
Computational Intelligence and Neuroscience
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-04-18
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Abstract EN
This paper measures the adhesion/cohesion force among asphalt molecules at nanoscale level using an Atomic Force Microscopy (AFM) and models the moisture damage by applying state-of-the-art Computational Intelligence (CI) techniques (e.g., artificial neural network (ANN), support vector regression (SVR), and an Adaptive Neuro Fuzzy Inference System (ANFIS)).
Various combinations of lime and chemicals as well as dry and wet environments are used to produce different asphalt samples.
The parameters that were varied to generate different asphalt samples and measure the corresponding adhesion/cohesion forces are percentage of antistripping agents (e.g., Lime and Unichem), AFM tips K values, and AFM tip types.
The CI methods are trained to model the adhesion/cohesion forces given the variation in values of the above parameters.
To achieve enhanced performance, the statistical methods such as average, weighted average, and regression of the outputs generated by the CI techniques are used.
The experimental results show that, of the three individual CI methods, ANN can model moisture damage to lime- and chemically modified asphalt better than the other two CI techniques for both wet and dry conditions.
Moreover, the ensemble of CI along with statistical measurement provides better accuracy than any of the individual CI techniques.
American Psychological Association (APA)
Hassan, M. R.& Mamun, A. A.& Hossain, M. I.& Arifuzzaman, Md. 2018. Moisture Damage Modeling in Lime and Chemically Modified Asphalt at Nanolevel Using Ensemble Computational Intelligence. Computational Intelligence and Neuroscience،Vol. 2018, no. 2018, pp.1-9.
https://search.emarefa.net/detail/BIM-1130834
Modern Language Association (MLA)
Hassan, M. R.…[et al.]. Moisture Damage Modeling in Lime and Chemically Modified Asphalt at Nanolevel Using Ensemble Computational Intelligence. Computational Intelligence and Neuroscience No. 2018 (2018), pp.1-9.
https://search.emarefa.net/detail/BIM-1130834
American Medical Association (AMA)
Hassan, M. R.& Mamun, A. A.& Hossain, M. I.& Arifuzzaman, Md. Moisture Damage Modeling in Lime and Chemically Modified Asphalt at Nanolevel Using Ensemble Computational Intelligence. Computational Intelligence and Neuroscience. 2018. Vol. 2018, no. 2018, pp.1-9.
https://search.emarefa.net/detail/BIM-1130834
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1130834