Model developments of long-term aged asphalt binders

NCUIR > Engineering College > Department of Civil Engineering > journal & Dissertation > Item 987654321/99728

Please use this identifier to cite or link to this item: https://ir.lib.ncu.edu.tw/handle/987654321/99728

Title:	Model developments of long-term aged asphalt binders
Authors:	莊長賢;Xiao, Feipeng;Amirkhanian, Serji N.;Juang, C. Hsein;Hu, Shaowei;Shen, Junan
Contributors:	工學院土木工程學系
Keywords:	Analysis;Artificial neural network;Asphalt cement;Gel permeation chromatography;HP-GPC;Important Index;m-Value;Mass loss;Mechanical properties;Neural networks;Penetration index;Pressurized aging vessel;Regression analysis;Stiffness
Date:	2012-12-01
Issue Date:	2026-04-21 13:31:25 (UTC+8)
Publisher:	Elsevier Ltd.;Elsevier Ltd
Abstract:	摘要： ► This study developed a series of models to simulate long-term aged asphalt binders. ► ANN models are more effective than regression models. ► And these ANN models were easily implemented in a spreadsheet. ► Aging temperature, duration and molecular sizes are the most important factors. Artificial neural networks (ANNs) are useful in place of conventional physical models for analyzing complex relationship involving multiple variables and have been successfully used in civil engineering applications. The objective of this study was to develop a series of ANN models to simulate the long-term aging of three asphalt binders (PG 64-22, crumb rubberized asphalt modifier, PG 76-22) regarding seven aging variables such as aging temperature and duration, m-value, mass loss of pressurized aging vessel (PAV) samples, percentages of large and small molecular sizes of high pressure-gel permeation chromatographic (GPC) testing, and binder stiffness. The results indicated that ANN-based models are more effective than the regression models and can easily be implemented in a spreadsheet, thus making it easy to apply. The results also show that the aging temperature, aging duration, percentage of large and small molecular sizes, and binder stiffness are the most important factors in the developed ANN models for prediction of penetration index after a long-term aging process. 出版者： Elsevier Ltd 出版日期： 2012-12 出處： Construction & building materials, 2012-12, Vol.37, p.248-256 版權： 2012 Elsevier Ltd 版權： COPYRIGHT 2012 Elsevier B.V. 識別號： ISSN: 0950-0618 識別號： EISSN: 1879-0526 識別號： DOI: 10.1016/j.conbuildmat.2012.07.047
Appears in Collections:	[Department of Civil Engineering] journal & Dissertation

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