Application of Artificial Intelligence Method for Predicting of Compressive Strength and Materials Required for Self-Compacting Concrete

The compressive strength of concrete is a major parameter to assess the overall quality of concrete as other mechanical prosperities are directly related to the compressive strength. It can be determined using the non-destructive testing (NDT) method which is carried out without destroying the concrete specimen. Whereas the NDT methods like the rebound (Schmitz) hammer and Ultrasonic Pulse velocity (UPV) are most popular because they are much quicker; their values are more of an approximation than exact compressive strength values. The newly developed soft computing techniques like ANN, Fuzzy logic, Genetic programming etc. may be used to prepare a better numerical model correlating NDT results. Therefore, the present work uses the ANN tool to predict the required compressive strength and materials. The performance of the proposed model was evaluated using a dataset of 80 SCC specimens which are collected from journals. The proposed approach provides a reliable and accurate means for predicting the compressive strength and materials required for SCC. It is a time-consuming process and difficult to determine the compression strength of concrete in the laboratory. In this project, we predict the material quantities required for the different mixes and we cast cubes of 150 mm with those quantities and did NDT and compression tests after 28 days of curing, and with those quantities using ANN, we predict the compression strength of the SCC. After that, we just compare the results which we are getting from NDT, compression test and ANN predicted values.