Performance evaluation of hot mix asphaltic concrete incorporating cow bone ash (CBA) as partial replacement for filler

Modupe, A. E. and Olayanju, T.M.A. and Atoyebi, O. D. and Aladegboye, S. J. and Awolusi, T. F. and Busari, A. and Aderemi, P. O. and Modupe, O. C. (2019) Performance evaluation of hot mix asphaltic concrete incorporating cow bone ash (CBA) as partial replacement for filler. Materials Science and Engineering, 640.

Text Modupe_2019_IOP_Conf._Ser.__Mater._Sci._Eng._640_012082.pdf - Published Version Download (1MB)

Abstract

Given the current realities of incessant pavement distresses frequently experienced on Nigerian highways due to axle loads from heavy-duty vehicles, coupled with the menace of grave environmental pollution from abattoir solid wastes such as Cow-bones, the mechanistic properties of Cow Bone Ash (CBA) as partial replacement for filler in the production of asphaltic concrete via the Marshall Mix Design Method and Artificial Neural Networks (ANN) were investigated in this research. The conventional filler was partially replaced with CBA at 2.5%, 5%, 7.5%, 10%, 20%, 30%, 40% and 50% respectively, in the total mix. Sequel to the production of the bituminous concrete at the various proportions, the samples were submerged in water, in a water bath for 30 minutes at a temperature of 105°C before conducting Marshall Stability and flow tests. Results revealed that the stability and flow of asphaltic concrete containing the CBA were greater than that of the concrete containing the conventional filler. Furthermore, the physical and volumetric properties of the mix also improved as CBA was observed to be finer than the conventional quarry dust, hence reduced the voids present in the mix and stiffened the bitumen film on the aggregate particles. The results obtained further showed that the conventional filler (quarry dust) can be replaced partially with CBA up to 50%. The ANN Model employed was trained and tested by quick propagation (QP) algorithm amongst others such as the Incremental Back Propagation (IBP), Batch Back Propagation (BBP), Levenberg Marquardt (quasi Newton) and genetic algorithm (GA). QP gave the least Root Mean Square Error (RMSE) at the shortest time. The statistical values obtained showed that the ANN model was able to efficiently study and predict the experimental data

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