Comparison of different machine learning algorithms to predict mechanical properties of concrete

Abstract

Concrete is the most widely used construction material throughout the world. Extensive experiments are conducted every year to measure various physical, mechanical, and chemical properties of concrete involving a hefty amount of money and time. This work focuses on the utilization of Machine Learning (ML) algorithms to predict a wide range of concrete properties and avoiding unnecessary experimentation. In this work, six mechanical properties of concrete namely Modulus of Rupture, Compression strength, Modulus of Elasticity, Poisson's ratio, Splitting tensile strength and Coefficient of thermal expansion were estimated by applying five different ML algorithms viz. Linear Regression, Support Vector Machine, Decision Tree, Random Forest, and Gradient Boosting models on the Wisconsin concrete mixes database. Further, these ML models were evaluated to identify the most suitable model that can reliably predict the mechanical properties of concrete. The approach followed in this research was verified using the 10-fold Cross- Validation technique to get rid of training and testing split bias. The Grid Search Cross Validation method was used to find the best hyperparameters for each algorithm. Root mean squared error (RMSE) and Nash and Suctcliffe Efficiency (NS) results showed that the Support Vector Machine outperformed all other models applied on the datasets. Support Vector Machine predicted the Modulus of Rupture at a curing age of 28 days with an NS score of 0.43 which is 34% and 26% better than the NS scores of Random Forest and Gradient Boosting advanced algorithms, respectively. This suggests that the Support Vector Machine algorithm with its NS score further improved can be used for predicting new data points at least for potentially similar systems.