Prediction of hydration properties of rice straw mineral composite slabs using expert systems

Abstract

Thickness swelling (TS) is a vital hydration property to test the suitableness of a material to be used in the built environment. These materials are made of randomly oriented natural or mineral fibers mixed with cementitious binders, such as magnesium oxide (MgO), gypsum, and Portland cement. Since they have high moisture absorption property due to the random orientation of fibers and are manufactured with different process parameters, it is essential to develop a prediction model to reduce the development cost and time. In this article, TS of rice straw-based MgO slabs was measured and predicted for three different fiber lengths, density, the thickness of the tiles, and the ratio of the mixture. Fully connected cascade (FCC) architecture of artificial neural network (ANN) and reduced error pruning tree (REPTree) algorithm of decision tree methods were used as expert systems for prediction. The best architectures were identified from the 109 experimental datasets of in-house developed slabs of commercial sizes. The proposed board absorbed more water than the commercial boards. However, the FCC and REPTree predicted the TS with an accuracy of 95% and 89%, respectively. The prediction model with FCC may be utilized as a reference for developing new building materials based on natural fibers with cementitious binders.