Concrete compressive strength prediction using an explainable boosting machine model

The mixing ratio of the raw materials has a significant impact on concrete compressive strength. Although the compressive strength of concrete can be inferred from the mix ratio, it is frequently challenging to determine how each mix ratio parameter affects the results. In this study, an Explainable Boosting Machine (EBM) is applied to predict the concrete compressive strength and explain the contribution of mix ratio factors on the compressive strength. Meanwhile, the com-bined algorithm selection and hyperparameter optimization problem in machine learning is implemented by employing a Bayesian optimization technique. A dataset consisting of 1030 concrete compressive strength test data has been used for model building. The results show that the Bayesian optimization algorithm iteratively constructs the algorithmic/hyperparametric model and identifies the optimal point in the space, significantly reducing the time consumption in the ML model building process. In terms of model prediction performance, the EBM algorithm shows excellent results with R2 = 0.93, RMSE = 4.33, and MAE = 3.10. The EBM algorithm allows one to fully interpret the contribution of individual features to the prediction results from both global and local aspects, by which the impact of each mix ratio parameter on the compression strength of the concrete can be further determined.

Automated summary

The mixing ratio of raw materials significantly affects the compressive strength of concrete. An Explainable Boosting Machine (EBM) is used to predict the compressive strength and explain the contribution of mix ratio factors. Bayesian optimization is employed for algorithm selection and hyperparameter optimization. The EBM algorithm shows excellent prediction performance with R2 = 0.93, RMSE = 4.33, and MAE = 3.10, allowing for the interpretation of individual feature contributions and determination of concrete compressive strength.