Multi-objective Bayesian Optimization of Super hydrophobic Coatings on Asphalt Concrete Surfaces Nahvi, Ali Sassani, Alireza Arabzadeh, Ali Nahvi, Ali Sadoughi, Mohammad Kazem Arabzadeh, Ali Sassani, Alireza Hu, Chao Ceylan, Halil Ceylan, Halil Kim, Sunghwan
dc.contributor.department Civil, Construction and Environmental Engineering
dc.contributor.department Electrical and Computer Engineering
dc.contributor.department Institute for Transportation
dc.contributor.department Institute for Transportation 2019-10 2020-06-30T01:12:44Z 2020-06-30T01:12:44Z Mon Jan 01 00:00:00 UTC 2018 2019-10-01
dc.description.abstract Conventional snow removal strategies add direct and indirect expenses to the economy through profit lost due to passenger delays costs, pavement durability issues, contaminating the water runoff, and so on. The use of superhydrophobic (super-water-repellent) coating methods is an alternative to conventional snow and ice removal practices for alleviating snow removal operations issues. As an integrated experimental and analytical study, this work focused on optimizing superhydrophobicity and skid resistance of hydrophobic coatings on asphalt concrete surfaces. A layer-by-layer (LBL) method was utilized for spray depositing polytetrafluoroethylene (PTFE) on an asphalt concrete at different spray times and variable dosages of PTFE. Water contact angle and coefficient of friction at the microtexture level were measured to evaluate superhydrophobicity and skid resistance of the coated asphalt concrete. The optimum dosage and spay time that maximized hydrophobicity and skid resistance of flexible pavement while minimizing cost were estimated using a multi-objective Bayesian optimization (BO) method that replaced the more costly experimental procedure of pavement testing with a cheap-to-evaluate surrogate model constructed based on kriging. In this method, the surrogate model is iteratively updated with new experimental data measured at proper input settings. The result of proposed optimization method showed that the super water repellency and coefficient of friction were not uniformly increased for all the specimens by increasing spray time and dosage. In addition, use of the proposed multi-objective BO method resulted in hydrophobicity and skid resistance being maximally augmented by approximately 23% PTFE dosage at a spray time of 5.5 s.
dc.description.comments This article is published as Nahvi, Ali, Mohammad Kazem Sadoughi, Ali Arabzadeh, Alireza Sassani, Chao Hu, Halil Ceylan, and Sunghwan Kim. "Multi-objective Bayesian Optimization of Super hydrophobic Coatings on Asphalt Concrete Surfaces." Journal of Computational Design and Engineering 6, no. 4 (2019): 693-704. DOI: 10.1016/j.jcde.2018.11.005. Copyright 2018 Society for Computational Design and Engineering. This is an open access article under the CC BY-NC-ND license. Posted with permission.
dc.format.mimetype application/pdf
dc.identifier archive/
dc.identifier.articleid 1198
dc.identifier.contextkey 13458420
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath ccee_pubs/198
dc.language.iso en
dc.source.bitstream archive/|||Fri Jan 14 22:00:07 UTC 2022
dc.subject.disciplines Civil Engineering
dc.subject.disciplines Computational Engineering
dc.subject.keywords Sustainable airfield pavement
dc.subject.keywords Superhydrophobic coating
dc.subject.keywords Polytetrafluoroethylene
dc.subject.keywords Surrogate modeling
dc.subject.keywords Multi-objective Bayesian optimization
dc.title Multi-objective Bayesian Optimization of Super hydrophobic Coatings on Asphalt Concrete Surfaces
dc.type article
dc.type.genre article
dspace.entity.type Publication
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