Prediction of moisture resistance of asphalt mastics modified by liquid anti stripping based on support vector regression, artificial neural network and Kernel-based support vector regression methods
Commonly referred to as stripping, moisture sensitivity is described as the stripping of bitumen from the surface of aggregates. This phenomenon occurs when adhesion bonds between asphalt mastics and the surface of aggregates are fractured mainly due to cohesion failures in the asphalt cement. For evaluating the effect of bitumen-aggregates adhesion and also asphalt mastic cohesion, the bitumen bonding strength (BBS) test on wet and dry conditions was implemented, with regard to a few studies in this field. The experimental program for this study consists of using one binder source Flint Hills PG 64-22, three liquid anti stripping additives (ASA), and two aggregate substrate (limestone and granite). This research aimed to examine the effect of various ASA and mineral fillers on moisture failure resistance in asphalt mastic by the use of BBS experiment. The findings indicated that in dry condition, samples with no additive and containing limestone filler had higher pull-off tensile strength (POTS) than those with granite. While, by addition of ASA, all specimens containing granite had higher POTS compared to those with limestone. In addition, the failure of most of the asphalt mastics was cohesive, except asphalt mastics containing granite filler and the one with limestone filler and additive C.
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/issn/09500618
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Supplemental Notes:
- © 2022 Elsevier Ltd. All rights reserved. Abstract reprinted with permission of Elsevier.
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Authors:
- Babagoli, Rezvan
- Rezaei, Mohsen
- Publication Date: 2022-6-13
Language
- English
Media Info
- Media Type: Web
- Features: Figures; Photos; References; Tables;
- Pagination: 127480
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Serial:
- Construction and Building Materials
- Volume: 335
- Issue Number: 0
- Publisher: Elsevier
- ISSN: 0950-0618
- Serial URL: http://www.sciencedirect.com/science/journal/09500618?sdc=1
Subject/Index Terms
- TRT Terms: Antistrip additives; Mastic asphalt; Moisture damage; Neural networks; Regression analysis; Vector analysis
- Subject Areas: Highways; Materials; Pavements;
Filing Info
- Accession Number: 01845362
- Record Type: Publication
- Files: TRIS
- Created Date: May 17 2022 10:47AM