An Experimental Study of Strengthing of Concrete by Bacterial Mineral Precipitation

A new technique in remediating cracks and fissures in concrete by utilizing microbiologically induced Calcite (CaCo3) precipitation is discussed. Microbiologically induced calcite precipitation (MICP) is a technique that comes under a broader category of science called Bio Mineralization. It is a process by which living organisms form inorganic solids. Bacillus subtilis, a common soil bacterium can induce the precipitation of calcite. The objective of the present investigation is to study the potential application of bacterial species i.e. Bacillus subtilis to improve the strength of cement concrete. Here we have made an attempt to incorporate dormant but viable bacteria in the concrete matrix which will contribute to the strength of the concrete. In this project, bacterial concrete is prepared under grade of concrete M30.The design mix proportioning also carried under IS code provision. Testing of specimens are carried at 7 days, 14 days and 28 days of curing by Compression Testing Machine and Universal Testing Machine for corresponding specimens.

Predicting the Compressive Strength of Concrete Using an RBF-ANN Model

In this study, a radial basis function (RBF) artificial neural network (ANN) model for predicting the 28-day compressive strength of concrete is established. The database used in this study is the expansion by adding data from other works to the one used in the author’s previous work. The stochastic gradient approach presented in the textbook is employed for determining the centers of RBFs and their shape parameters. With an extremely large number of training iterations and just a few RBFs in the ANN, all the RBF-ANNs have converged to the solutions of global minimum error. So, the only consideration of whether the ANN can work in practical uses is just the issue of over-fitting. The ANN with only three RBFs is finally chosen. The results of verification imply that the present RBF-ANN model outperforms the BP-ANN model in the author’s previous work. The centers of the RBFs, their shape parameters, their weights, and the threshold are all listed in this article. With these numbers and using the formulae expressed in this article, anyone can predict the 28-day compressive strength of concrete according to the concrete mix proportioning on his/her own.

PREDICTION OF CONCRETE MIX DESIGN USING DATA MINING TECHNIQUES - A REVIEW

Concrete mix proportioning is one of the critical process and it involves a lot of precautionary measures to arrive at the right proportions of ingredients like cement, aggregate, water, and admixtures. Even though there are technical specifications that are managed mix proportionating, the procedure is not totally in the realm of science. Due to imprecise codal provisions, impreciseness, and fuzziness involved in the various stages of mix proportioning. This paper reviews the various data mining and machine learning techniques developed by the researchers for making concrete mix design for various codal provisions more realistic and scientific.

An ANN Model for Predicting the Compressive Strength of Concrete

An artificial neural network (ANN) model for predicting the compressive strength of concrete is established in this study. The Back Propagation (BP) network with one hidden layer is chosen as the structure of the ANN. The database of real concrete mix proportioning listed in earlier research by another author is used for training and testing the ANN. The proper number of neurons in the hidden layer is determined by checking the features of over-fitting while the synaptic weights and the thresholds are finalized by checking the features of over-training. After that, we use experimental data from other papers to verify and validate our ANN model. The final result of the synaptic weights and the thresholds in the ANN are all listed. Therefore, with them, and using the formulae expressed in this article, anyone can predict the compressive strength of concrete according to the mix proportioning on his/her own.