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© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.

Abstract

A good asphalt mixture is very important to maintain the triangle of sustainability. Many accidents occur due to pavement damage such as permanent deformation caused by the external loads induced by heavy traffic. Stone Mastic Asphalt (SMA) has a low resistance to moisture and other performances. Many researchers have conducted on SMA using various types of fiber. However, not much research has been done using steel fiber in the SMA mixture and has analyzed the result obtained using a statistical approach. The objective of this research was to identify the optimum amount of steel fiber in a modified asphalt mixture and characterize the performance of steel fiber in the SMA mixture using the statistical approach of Response Surface Methodology (RSM) in Design Expert Software. In this study, various steel fiber proportions of 0 percent, 0.3 percent, 0.5 percent, and 0.7 percent by the total weight of the SMA mixture were used. The Marshall stability and flow test, dynamic creep and moisture susceptibility test, and ultimately, RSM analysis were used to evaluate the properties and performance of the steel fiber-modified SMA, which contained 6.2 percent of PEN 60/70 asphalt binder content. The testing findings unmistakably demonstrated that the addition of steel fiber greatly improves the SMA mixture’s resistance to moisture and permanent deformation. An amount of 0.3 percent was found to be the most optimum steel fiber content from the optimization by using Response Surface Methodology, thus proven with additional steel fiber in the SMA mixture enhancing the performance of the mixture. As a result, it can be determined that the addition of steel fiber to SMA asphalt mixtures has improved the properties and performance in the construction of asphalt pavements, and the RSM method is an efficient statistical method for producing an appropriate empirical model for relating parameters and predicting the best performance of an asphaltic mixture.

Details

Title
Statistical Approach Model to Evaluate Permanent Deformation of Steel Fiber Modified Asphalt Mixtures
Author
Ekarizan Shaffie 1 ; Alma Aina Mohd Nasir 2 ; Jaya, Ramadhansyah Putra 3   VIAFID ORCID Logo  ; Ahmad Kamil Arshad 1 ; Nuryantizpura Mohamad Rais 4 ; Zaid Hazim Al-Saffar 5 

 Institute for Infrastructure Engineering and Sustainable Management, Universiti Teknologi MARA, Shah Alam 40450, Malaysia; School of Civil Engineering, College of Engineering, Universiti Teknologi Mara, Shah Alam 40450, Malaysia 
 School of Civil Engineering, College of Engineering, Universiti Teknologi Mara, Shah Alam 40450, Malaysia 
 Institute for Infrastructure Engineering and Sustainable Management, Universiti Teknologi MARA, Shah Alam 40450, Malaysia; Faculty of Civil Engineering Technology, Universiti Malaysia Pahang, Kuantan 26300, Malaysia 
 School of Civil Engineering, College of Engineering, Universiti Teknologi Mara, Shah Alam 40450, Malaysia; Malaysia Institute of Transport (MITRANS), Universiti Teknologi MARA, Shah Alam 40450, Malaysia 
 Department of Construction Engineering and Projects Management, Al-Noor University College, Nineveh 41012, Iraq; Building and Construction Engineering Department, Technical College of Mosul, Northern Technical University, Mosul 41002, Iraq 
First page
3476
Publication year
2023
Publication date
2023
Publisher
MDPI AG
e-ISSN
20711050
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2779560296
Copyright
© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.