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

• Main Authors: Ekarizan, Shae, Putra Jaya, Ramadhansyah, Juraidah, Ahmad, Ahmad Kamil, Arshad, Mohd Afiq, Zihan, Fiona, Shiong
• Format: Article
• Language: English
• Published: Hindawi Limited 2022
• Subjects: TE Highway engineering. Roads and pavements
• Online Access: http://umpir.ump.edu.my/id/eprint/34664/1/Prediction%20Model%20of%20the%20Coring%20Asphalt%20Pavement.pdf; http://umpir.ump.edu.my/id/eprint/34664/; https://doi.org/10.1155/2022/6723396; https://doi.org/10.1155/2022/6723396
• Institution: Universiti Malaysia Pahang

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.