Comparison of different machine learning algorithms to predict mechanical properties of concrete
Date
2021-01-19
Authors
Koya, Bhanu Prakash
Journal Title
Journal ISSN
Volume Title
Publisher
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.
Description
Keywords
Concrete Properties, Machine Learning, Modulus of Rupture, Support Vector Machine, Decision Tree, Ensemble methods