Prediction of shotcrete compressive strength using Intelligent Methods; Neural Network and Support Vector Regression

Compressive strength is one of the most important mechanical properties of concrete. 28-day compressive strength test is the acceptance measure of concrete or shotcrete, which is highly affected by the mix design. Some parameters like water/cement ratio, amount of fine and coarse aggregates in mix, admixtures and so on affect shotcrete strength. Due to the large number of such parameters, it is very difficult to predict the shotcrete strength. Today, owing to intelligent methods, modeling has a particular role in engineering sciences and predicting material behavior. Therefore, this paper examines different mix designs of shotcrete containing microsilica and records their 28-day compressive strength. Regarding shotcrete mix design parameters as inputs, ANN and SVR models were used to predict compressive strength of shotcretes. The correlation coefficient (R), mean absolute percentage error (MAPE) and the root mean square error (RMSE) statics are used for performance evaluation of proposed predictive models. All of the results showed that the accuracy of the proposed soft computing methods is quite satisfactory as compared to experimental results. The finding of this study indicated that the both ANN and SVM models are sufficient tools for estimating the compressive strength of shotcrete.