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Combined effect of glass and carbon fiber in asphalt concrete mix using computing techniques

  • Upadhya, Ankita (Department of Civil Engineering, Shoolini University) ;
  • Thakur, M.S. (Department of Civil Engineering, Shoolini University) ;
  • Sharma, Nitisha (Department of Civil Engineering, Shoolini University) ;
  • Almohammed, Fadi H. (Department of Civil Engineering, Shoolini University) ;
  • Sihag, Parveen (Department of Civil Engineering, Chandigarh University)
  • Received : 2021.10.14
  • Accepted : 2022.03.16
  • Published : 2022.07.25
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Abstract

This study investigated and predicted the Marshall stability of glass-fiber asphalt mix, carbon-fiber asphalt mix and glass-carbon-fiber asphalt (hybrid) mix by using machine learning techniques such as Artificial Neural Network (ANN), Support Vector Machine (SVM) and Random Forest(RF), The data was obtained from the experiments and the research articles. Assessment of results indicated that performance of the Artificial Neural Network (ANN) based model outperformed applied models in training and testing datasets with values of indices as; coefficient of correlation (CC) 0.8492 and 0.8234, mean absolute error (MAE) 2.0999 and 2.5408, root mean squared error (RMSE) 2.8541 and 3.3165, relative absolute error (RAE) 48.16% and 54.05%, relative squared error (RRSE) 53.14% and 57.39%, Willmott's index (WI) 0.7490 and 0.7011, Scattering index (SI) 0.4134 and 0.3702 and BIAS 0.3020 and 0.4300 for both training and testing stages respectively. The Taylor diagram also confirms that the ANN-based model outperforms the other models. Results of sensitivity analysis show that Carbon fiber has a major influence in predicting the Marshall stability. However, the carbon fiber (CF) followed by glass-carbon fiber (50GF:50CF) and the optimal combination CF + (50GF:50CF) are found to be most sensitive in predicting the Marshall stability of fibrous asphalt concrete.

Keywords

Acknowledgement

We, the authors, would like to express our gratitude to the researchers whose findings we have mentioned in this work.

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