Performance of ANN in Predicting Internal Bonding of Cement Particleboard Manufactured from Giant Reed and Bagasse

The present article investigates the microstructure of the cement matrices and the products of cement hydration by means of scanning electron microscopy, Fourier transform infrared spectroscopy and X-Ray diffraction. Then, the internal bonding strength (IB) is measured for the mixtures containing various amounts of nanosilica (NS), reed and bagasse particles. Finally, an Artificial Neural Network (ANN) is trained to reproduce these experimental results. The results show that the hardened cement paste including NS features the highest level of C-S-H. However, it has a lower level of C-S-H polymerization if reed or bagasse particles are applied. A relatively new dense microstructural degree is considered in the cement pastes containing NS, and a lower agglomeration is observed in the samples including reed or bagasse particles with NS. According to the microstructural analysis, the addition of NS to the samples containing reed or bagasse particles increases the unhydrated amount of C2S and C3S in the cement paste due to the decrease in the water needed for fully hydrated cement grains through portlandite (Ca(OH)2), C-S-H and ettringite increase. Besides, it is shown that the ANN prediction model is a useful, reliable and quite effective tool for modeling IB of cement-bonded particleboard (CBPB). It is indicated that the mean absolute percentage errors (MAPE) are 1.98 % and 1.45 % in the prediction of the IB values for the training and testing datasets, respectively. The determination coeffi cients (R2) of the training and testing data sets are 0.972 and 0.997 in the prediction of the bonding strength by ANN, respectively.