Application of ANN for prediction of chloride penetration resistance and concrete compressive strength

Self-consolidating concrete is considered as one of the greatest developments in concrete industry. It has several advantages over conventional concrete such as minimized voids, ease of pour into congested formworks and complex shapes and absence of mechanical vibrations. One of the major problems with concrete structures is the corrosion of steel reinforcement due to penetration of chemicals like chloride. Most of the standard tests to determine chloride penetration levels are performed at 28-days. It is desired that some rapid means of determination of chloride ion diffusion in concrete are available for design and quality assurance purposes. In present study Artificial Neural Networks (ANNs) are developed for prediction of chloride penetration level and compressive strength of SCC mixes. ANN developed for prediction of chloride penetration used 294 data points for training and validation. This trained ANN was used to verify chloride penetration level of twenty experimentally evaluated SCC specimens of varied composition. Similarly, ANN developed for prediction of compressive strength used 1031 data points for training and validation of network. Compressive strength values predicted by ANN were compared with experimentally evaluated compressive strengths of thirty-three SCC specimens of varied composition. Both ANNs use relevant input parameters for training and validation of networks. A comparison between experimentally evaluated and ANN predicted values is presented in the paper for different combinations of learning rate and momentum of network. The trained ANNs were able to predict chloride iron penetration levels and compressive strength based on selected input parameters to an acceptable extent.