Kernel machines and firefly algorithm based dynamic modulus prediction model for asphalt mixes considering aggregate morphology. (20th January 2018) - Singh, Dharamveer; Maheshwari, Saurabh; Zaman, Musharraf; Commuri, Sesh
Highlights: SVR proved successful in outperforming Witczak and ANN models for estimation of dynamic modulus of asphalt mixes. Aggregate shape parameters are considered in estimation of dynamic modulus. An approach for formulation of SVR-FA model equations for direct prediction of HMA stiffness is also discussed. SVR-FA algorithm is capable of successfully predicting dynamic modulus values using the aggregate shape parameters. Abstract: Artificial Intelligence algorithm support vector regression (SVR) has proved successful in outperforming conventional Witczak and ANN models for estimation of dynamic modulus (E ∗ ) of asphalt mixes. However, there were two issues related to the development of E ∗ prediction models that the present study addresses. Firstly, since aggregates occupy almost 95% by weight of HMA, it is quite possible that the morphology of these aggregates play an important role in influencing the E ∗ values. To address this issue, aggregate shape parameters, namely, angularity, sphericity, texture and form were used with aggregate gradation for stiffness estimation. Secondly, to fine tune the hyper-parameters firefly algorithm (FA) was coupled with SVR. E ∗ tests of 20 HMA mixes having different sources, sizes of aggregates, and volumetric properties were conducted at 4 temperatures and 6 frequencies. Aggregate shape parameters were measured using the automated aggregate image measurement system (AIMS). SVR-FA models were developed that predicted the E ∗ with an R 2 of 0.98. SVR-FA models were compared with SVR and ANN models for E ∗ prediction. Further, a sensitivity analysis was conducted to identify the important input parameters. Lastly, an approach for formulation of SVR-FA model equations for direct prediction of HMA stiffness is also discussed. FA proved instrumental in improving the efficiency of SVR by optimizing the hyper-parameters with lesser manual effort. Finally, it was concluded that SVR-FA algorithm is capable of successfully predicting the E ∗ values using the aggregate shape parameters.