Performance Prediction of Waste Tire Metal Fiber-Modified Asphalt Mixes Using a Decision Tree Machine Learning Technique

Arsalaan Khan Yousafzai, Muslich Hartadi Sutanto, Nasir Khan, Mohamed Mubarak Abdul Wahab, Muhammad Imran Khan, Adamu Sani Abubakar, Rania Al-Nawasir

Abstract

The Marshall stability and flow of asphalt mixes are important performance indicators of their durability and suitability for application in the pavement industry. The mix design for achieving the optimum levels of bitumen and volumetric properties is critical and depends on the properties of the ingredients used. Recycling waste materials from asphalt is also a key factor in achieving environmental sustainability. The development of machine learning models is crucial for the performance prediction of asphalt mixes. This study investigates the use of a machine learning approach to predict the performance of waste tire metal fiber-modified asphalt mixes. A dataset of 75 experimental data of various mix proportions was compiled to test and train the model. 60/70 penetration grade bitumen was used in conjunction with five modified mixes, each containing varying amounts of waste tire metal fiber at 0%, 0.375%, 0.75%, 1.125%, and 1.5%. Decision tree regression was applied to establish an effective relationship between the input variables. R2, adjusted R2, and mean absolute error were used to assess the predictive ability of each model. The input parameters were fiber content, bitumen content, aggregate percentage, and porosity. The study of input variables revealed that the stability decreased while the flow increased with increasing fiber and bitumen contents. With R2 recorded as 0.901 for training and 0.937 for testing, decision tree regression was found to be an effective model for predicting the performance of the modified mixes. This study fills a gap in machine learning applications by predicting the stability and flow performance of modified asphalt mixes using decision tree algorithms. Using waste tires and promoting recycling also enhances environmental sustainability in the pavement industry.

 

Keywords: Marshall stability, Marshall flow, metal fiber, asphalt mixes, decision tree.

 

https://doi.org/10.55463/issn.1674-2974.51.7.3


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