Moisture Damage Modeling in Lime and Chemically Modified Asphalt at Nanolevel Using Ensemble Computational Intelligence

Computational Intelligence and Neuroscience
M R HassanM Arifuzzaman

Abstract

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 alon...Continue Reading

References

Mar 3, 1986·Physical Review Letters·G BinnigC Gerber
Nov 18, 2005·Journal of Colloid and Interface Science·Ashish Vaidya, Manoj K Chaudhury
Jan 28, 2006·Journal of Microscopy·J-F MassonJ Margeson
Feb 7, 2014·Journal of Microscopy·R A Tarefder, S Ahsan

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Citations

Sep 13, 2019·Computational Intelligence and Neuroscience·Md ArifuzzamanAbdullah Al Mamun

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Methods Mentioned

BETA
Atomic Force Microscopy
AFM

Software Mentioned

Unichem

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