Assessment of strength characteristics for experimental based workable self compacting concrete using artificial neural network

Abstract

Self compacting concrete (SCC) is the concrete which can flow through reinforcement without the requirement of vibration. The proportioning of materials for workable SCC requires clear understanding of material properties and their behavior with one on one to apply it in construction site. Hence, an extensive experimentation is conducted based on empirical way and produced 123 workable SCC mixtures resulting for measuring the strength characteristics such as compressive strength (fc) and splitting tensile strength (fst). The workable SCC mixture is considered for strength characteristics only after passing the requirements of workability tests of SCC namely slump flow test, L-box test, V-funnel test and sieve segregation resistance test. The 123 workable SCC mixtures experimentally obtained fc ranging from 26.6 N/mm2 to 62.1 N/mm2 and fst ranging from 2.9 N/mm2 to 6.9 N/mm2. In this study, artificial neural network (ANN) had been used which was speedy and efficient for predicting output strength characteristics based on extensive experimental data investigated in laboratory. ANN is data modeling tool where relationship between inputs and output can be found out. In this work, after investigating all parameters for producing SCC, only important inputs considered in ANN model. Marquardt back propagation training algorithm had been used with inputs containing cement content, water cement ratio, type of mineral admixture, percentage of mineral admixture and type of chemical admixture. In this study, satisfactory mean square error and correlation coefficient had been obtained during training, validation and testing of ANN for predicting two outputs fc and fst separately.