The infl uence of Nano-SiO 2 (NS) content and lignocellulosic material addition on hydration behavior of cement paste was studied through measurement of hydration temperature, initial and fi nal setting time of cement paste and compressive strength of hardened cement paste. Besides, the amount of NS, particle size of reed and bagasse as lignocellulosic materials and bagasse to reed particles weight ratio were selected as manufacturing variables for cement-bonded particleboard (CBPB) each at fi ve levels. The relationships between independent parameters and output variables (modulus of rupture (MOR), modulus of elasticity (MOE) and internal bonding (IB)) were modeled using response surface methodology (RSM) based on mathematical model equations (second-order multiple linear regression model) by computer simulation programming. The results indicated that cement pastes containing 3 wt.% Nano-SiO 2 content mixed with milled reed or bagasse particles enhanced maximum hydration temperature; however, the time of reaching the main rate peak shortened. Besides, the increase of SiO 2 replacement shortened the setting time. On the other hand, using reed particles, initial and fi nal setting times of cement prolonged, while bagasse particles shortened initial and fi nal setting times. Analysis of variance (ANOVA) was performed to determine the adequacy of the mathematical model and its respective variables. The interaction effect curves of the independent variables obtained from simulations showed a good agreement between the measured MOR, MOE and IB of CBPB and predicted values obtained by the developed models, and hence, the proposed concept was verifi ed.

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.