Prediction Model of the Coring Asphalt Pavement Performance through Response Surface Methodology

Pavement evaluations provide crucial information regarding the performance and service life of asphalt concrete (HMA). They examine the structure of an existing pavement before deciding on different maintenance alternatives. The Klang Valley, as part of one of the developing areas in the state of Se...

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Main Authors: Ekarizan Shaffie, Ramadhansyah Putra Jaya, Juraidah Ahmad, Ahmad Kamil Arshad, Mohd Afiq Zihan, Fiona Shiong
Format: Article
Language:English
Published: Wiley 2022-01-01
Series:Advances in Materials Science and Engineering
Online Access:http://dx.doi.org/10.1155/2022/6723396
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author Ekarizan Shaffie
Ramadhansyah Putra Jaya
Juraidah Ahmad
Ahmad Kamil Arshad
Mohd Afiq Zihan
Fiona Shiong
author_facet Ekarizan Shaffie
Ramadhansyah Putra Jaya
Juraidah Ahmad
Ahmad Kamil Arshad
Mohd Afiq Zihan
Fiona Shiong
author_sort Ekarizan Shaffie
collection DOAJ
description Pavement evaluations provide crucial information regarding the performance and service life of asphalt concrete (HMA). They examine the structure of an existing pavement before deciding on different maintenance alternatives. The Klang Valley, as part of one of the developing areas in the state of Selangor, generates a high volume of traffic every day due to the increasing number of vehicles crossing the area. Every day, the impact of axle loads caused by vehicles has a negative impact on flexible pavement, resulting in road deterioration due to extreme distress. Pothole failures are one of the most common causes of distress. Five research areas in the Klang Valley area that have deteriorated owing to pothole failures were chosen as case studies. The objective of the study is to investigate the existing flexible pavement conditions by means of laboratory testing consisting of physical, volumetric, and performance tests using collected core samples. As a result, the data collected was compared to the Malaysian Public Work Department’s (PWD) standard. Data from laboratory tests was analyzed using Response Surface Methodology (RSM) to determine correlations with parameters influencing distress. Historical data design was carried out between test components and responses, which consisted of laboratory parameters. Axial strain, tensile strength ratio, and stability were the responses measured in the RSM. The created models between the independent variables and responses revealed a high level of correlation. The binder content, degree of compaction, and stiffness were the most significant operating parameters from the 3D plots. Optimized performance due to asphaltic pavement failure was observed at binder content (5.1%), degree of compaction (97%), and stiffness (3.1 kN/mm) to achieve ultimate axial strain (5000 microstrains), tensile strength ratio (80%), and stability (9.2 kN). The study showed that the response surface methodology (RSM) is an effective statistical method for providing an appropriate empirical model for relating parameters and predicting the best performance of an asphaltic mixture to reduce flexible pavement failure.
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spelling doaj-art-68f5ebe3598f41e090cda2993db2d9b02025-02-03T06:12:24ZengWileyAdvances in Materials Science and Engineering1687-84422022-01-01202210.1155/2022/6723396Prediction Model of the Coring Asphalt Pavement Performance through Response Surface MethodologyEkarizan Shaffie0Ramadhansyah Putra Jaya1Juraidah Ahmad2Ahmad Kamil Arshad3Mohd Afiq Zihan4Fiona Shiong5Institute for Infrastructure Engineering and Sustainable ManagementInstitute for Infrastructure Engineering and Sustainable ManagementSchool of Civil EngineeringInstitute for Infrastructure Engineering and Sustainable ManagementDetik Venture Sdn BhdSchool of Civil EngineeringPavement evaluations provide crucial information regarding the performance and service life of asphalt concrete (HMA). They examine the structure of an existing pavement before deciding on different maintenance alternatives. The Klang Valley, as part of one of the developing areas in the state of Selangor, generates a high volume of traffic every day due to the increasing number of vehicles crossing the area. Every day, the impact of axle loads caused by vehicles has a negative impact on flexible pavement, resulting in road deterioration due to extreme distress. Pothole failures are one of the most common causes of distress. Five research areas in the Klang Valley area that have deteriorated owing to pothole failures were chosen as case studies. The objective of the study is to investigate the existing flexible pavement conditions by means of laboratory testing consisting of physical, volumetric, and performance tests using collected core samples. As a result, the data collected was compared to the Malaysian Public Work Department’s (PWD) standard. Data from laboratory tests was analyzed using Response Surface Methodology (RSM) to determine correlations with parameters influencing distress. Historical data design was carried out between test components and responses, which consisted of laboratory parameters. Axial strain, tensile strength ratio, and stability were the responses measured in the RSM. The created models between the independent variables and responses revealed a high level of correlation. The binder content, degree of compaction, and stiffness were the most significant operating parameters from the 3D plots. Optimized performance due to asphaltic pavement failure was observed at binder content (5.1%), degree of compaction (97%), and stiffness (3.1 kN/mm) to achieve ultimate axial strain (5000 microstrains), tensile strength ratio (80%), and stability (9.2 kN). The study showed that the response surface methodology (RSM) is an effective statistical method for providing an appropriate empirical model for relating parameters and predicting the best performance of an asphaltic mixture to reduce flexible pavement failure.http://dx.doi.org/10.1155/2022/6723396
spellingShingle Ekarizan Shaffie
Ramadhansyah Putra Jaya
Juraidah Ahmad
Ahmad Kamil Arshad
Mohd Afiq Zihan
Fiona Shiong
Prediction Model of the Coring Asphalt Pavement Performance through Response Surface Methodology
Advances in Materials Science and Engineering
title Prediction Model of the Coring Asphalt Pavement Performance through Response Surface Methodology
title_full Prediction Model of the Coring Asphalt Pavement Performance through Response Surface Methodology
title_fullStr Prediction Model of the Coring Asphalt Pavement Performance through Response Surface Methodology
title_full_unstemmed Prediction Model of the Coring Asphalt Pavement Performance through Response Surface Methodology
title_short Prediction Model of the Coring Asphalt Pavement Performance through Response Surface Methodology
title_sort prediction model of the coring asphalt pavement performance through response surface methodology
url http://dx.doi.org/10.1155/2022/6723396
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