Recent researches in literature have established that the overall life of mortar used in plastering works can be improved by the self-healing processes. Cracking of mortar plasters is a common hurdle due to the intrinsic brittleness of the material. This will result in severe loss of durability and water tightness. There are various methods to prevent this problem, such as adding glass fibers to the mortar mix which prevents shrinkage cracks is one among them and another way to do it is with the help of microorganisms that precipitates calcium carbonate which fills the cracks. But not all kinds of bacteria will precipitate calcium carbonate. Even if it does it should survive under extreme conditions present in mortar. The species called bacillus is a kind which fulfills those conditions is used in this project. The two bacteria namely “Bacillus Subtilis” and “Bacillus Megaterium” were isolated by taking 10g rhizosphere soil and they are cultured. The objective of this project is to compare the productivity of two bacteria. Along with this, a material called metakaolin is also used in combination with bacteria and its efficiency is also checked. The use of metakaolin (kaolinite) in this project increases compressive strength, decreases the heat of hydration which in turn increases the efficiency of calcium carbonate (CaCO3) precipitation along with bacteria. Mortar cubes are cast in different combinations to observe the compressive strength by Compression Test, healing capacity of mortar through Ultrasonic Pulse Velocity (UPV) Test and the precipitated amount of calcium carbonate via X-ray Diffraction Test. Also, the durability of mortar cubes has been identified using Water Absorption Test and Sorptivity Test.
Rheology is the science that concerns the flow of liquids, and the distortion of solids under an applied force. The study of the rheology of concrete determines the properties of fresh concrete. The rheological parameters are affected by temperature, stress conditions and several other factors. The main intention of this research is to model the rheological parameters of the fly ash incorporated cement with various types of superplasticizers exposed under different temperatures using an Artificial Neural Network. Test data were generated by performing rheological tests on cement paste at three distinct temperatures (15, 27, 35°C). Mixes were prepared using OPC, fly ash (15, 25, 35%) and superplasticizers of four different families. By conducting experiments, 252 data have been generated by modifying the combination of fly-ash, superplasticizer, and test temperature. Among the 252 data, 80% has been utilized for training and 20% is utilized for predicting the model’s accuracy. The input layer of the model consists of test temperature, the amount of fly ash replaced, cement and water content, and four different groups of superplasticizers. The cement paste’s yield stress was the output parameter of the model. The model generated data has been compared with the experimentally generated data to determine the accuracy of the model.Keywords: Rheology, Fly Ash, Superplasticizer, Temperature, ANN
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