An effort has been made to develop concrete compressive strength prediction models with the help of two emerging data mining techniques, namely, Artificial Neural Networks (ANNs) and Genetic Programming (GP). The data for analysis and model development was collected at 28-, 56-, and 91-day curing periods through experiments conducted in the laboratory under standard controlled conditions. The developed models have also been tested on in situ concrete data taken from literature. A comparison of the prediction results obtained using both the models is presented and it can be inferred that the ANN model with the training function Levenberg-Marquardt (LM) for the prediction of concrete compressive strength is the best prediction tool.
The effect of an aggressive chemical environment on concrete prepared with ordinary Portland cement and silica fume, either as a binary combination or a ternary combination with fly ash, is investigated in the present study. The adverse environmental conditions are simulated by using either 1% sulfuric acid, 1% hydrochloric acid or 1% nitric acid. The corrosion process was monitored by measuring the mass loss and compressive strength for a period of one year. It was found that the course of action of acid attack is dependent on the type of acid and solubility of the calcium salt formed. The presence of mineral admixtures was found to lower the detrimental effect of all types of acids on concrete. Ternary mixes with OPC, silica fume and fly ash performed better than binary mixes containing only silica fume as supplementary cementitious material.
A comparative analysis for the prediction of compressive strength of concrete at the ages of 28, 56, and 91 days has been carried out using machine learning techniques via "R" software environment. R is digging out a strong foothold in the statistical realm and is becoming an indispensable tool for researchers. e dataset has been generated under controlled laboratory conditions. Using R miner, the most widely used data mining techniques decision tree (DT) model, random forest (RF) model, and neural network (NN) model have been used and compared with the help of coefficient of determination (R 2 ) and root-mean-square error (RMSE), and it is inferred that the NN model predicts with high accuracy for compressive strength of concrete.
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