Comparison of Response Surface Methodology and Artificial Neural Network approach in predicting the performance and properties of palm oil clinker fine modified asphalt mixtures

Nura Shehu Aliyu Yaro, Muslich Hartadi Sutanto, Noor Zainab Habib, Madzlan Napiah, Aliyu Usman, Ashiru Muhammad

Research output: Contribution to journalArticlepeer-review

24 Citations (Scopus)
39 Downloads (Pure)


Recently with the increase in traffic loading, the traditional materials used for road construction deteriorate at a faster rate due to repetitive traffic loading which greatly necessitates bitumen modification to improve its quality. Amid an ever-increasing waste generation and disposal crisis, researchers came up with multiple ideas, however, the implementation was halted due to different practitioners' policies. Palm oil clinker (POC) waste is a prevalent waste dumped around the oil palm mill that pollutes the environment. To harness sustainability, this study utilizes varying dosages of POC fine (POCF) at 2%, 4%, 6%, and 8% to produce the POCF modified bitumen (POCF-MB). Also, the conventional and microstructure properties were evaluated. The objective of this study is to utilize response surface methodology (RSM) and artificial neural networks (ANN) to optimize and predict the stiffness modulus and rutting characteristic of asphalt mixtures prepared with POCF modified bitumen (POCF-MB). The conventional test results revealed that the incorporation of POCF improves the plain bitumen properties with enhanced stiffness and temperature susceptibility. Microstructural analysis highlighted that a new functional group Si-OH was formed because of the crystalline structure of Si-O that indicates bitumen properties enhancement with POCF inclusion. Two input and output variables were considered which are POCF dosage, test temperature, and stiffness modulus and rutting depth respectively. Results showed that all mixtures containing POCF-MB show better performance than the control mixture. Though, 6% POCF dosage shows improved performance compared to other mixtures increasing stiffness by 33.33% and 57.42% respectively at 25 °C and 40 °C, while rutting at 45 °C shows increased resistance by 25.91%. For both approaches, there was a high degree of agreement between the model-predicted values and actual. For the model statistical performance index, the RSM indicates that R2 for stiffness and rutting response were (99.700 and 99.668), RMSE (266.091 and 0.597), and MRE (68.793 and 3.841) respectively. The ANN R2 for stiffness and rutting response were (99.972 and 99.880), RMSE (61.605 and 0.280), and MRE (12.093 and 2.044) respectively. The ANN use 70% data for training, 15% data for testing, and 15% data for validation processes. The ANN model outperforms the RSM model for the prediction of POCF-MB asphalt mixtures' stiffness modulus and rutting properties.
Original languageEnglish
Article number126618
JournalConstruction and Building Materials
Early online date4 Feb 2022
Publication statusPublished - 21 Mar 2022


  • Artificial neural network
  • Asphalt mixtures
  • Palm oil clinker fine
  • Prediction
  • Response surface methodology
  • Rutting
  • Stiffness modulus

ASJC Scopus subject areas

  • Materials Science(all)
  • Building and Construction
  • Civil and Structural Engineering


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