Experimental study and theoretical prediction of mechanical properties of ultra-high-performance concrete incorporated with nanorice husk ash burning at different temperature treatments

Abstract

This research aimed to investigate the effect of nanorice husk ash (NRHA) prepared using different thermal treatment methods on ultra-high-performance concrete (UHPC) behaviour. NRHA was prepared by two methods: (1) burning for 3 h at 300, 500, 700 and 900 °C and (2) burning for different durations (9, 7, 5 and 3 h) at 300, 500, 700 and 900 °C. NRHA was added to UHPC to make 25 mixtures with three dosages (1%, 3% and 5%). Density, compressive strength, tensile strength, flexure strength and ultrasonic pulse velocity tests were performed at the experimental level. Moreover, full microstructure analysis, including X-ray diffractometry, Brunauer-Emmett-Teller surface area analysis, thermogravimetric analysis, scanning electron microscopy and energy-dispersive X-ray spectroscopy, was performed. The best performances in in the first method (constant duration, different temperatures) were obtained by 1% NRHA burned at 900 °C with 12.5% compressive strength and 1% NRHA burned at 700 °C with increased ratio (10%). Moreover, the best performance in the second method (different burning durations and temperatures) was obtained by 3% NRHA with a ratio of 22.5% at 700 °C for 5 h. Burning rice husk ash improved the compressive strength. It also remarkably improved the splitting tensile strength and flexure strength by 32% and 47%, respectively, at 3% NRHA treated at 700 °C for 3 h. The microstructural analysis showed the efficient role of NRHA in the compactness of concrete sections. It improved the formation of new calcium silicate hydrate gel; decreased the cracks, voids, CaCO₃ and Ca(OH)₂; and increased the Ca/Si composition. The obtained experimental results were used to build an artificial neural network (ANN) to predict UHPC properties. The ANN model was used as a validation tool to determine the correlation between results. Results showed a remarkable improvement in the mechanical properties of UHPC incorporating NRHA for all mixtures. The ANN model indicated a reliable correlation between input and output variables. The R² values for the training, validation and testing steps were all 0.99.

Keywords: Artificial neural networks; Mechanical properties; Nanorice husk; Prediction; Temperature treatment; Ultra-high-performance concrete.