Machine Learning Models for Ecofriendly Optimum Design of Reinforced Concrete Columns

CO2 emission is one of the biggest environmental problems and contributes to global warming. The climatic changes due to the damage to nature is triggering a climate crisis globally. To prevent a possible climate crisis, this research proposes an engineering design solution to reduce CO2 emissions. This research proposes an optimization-machine learning pipeline and a set of models trained for the prediction of the design variables of an ecofriendly concrete column. In this research, the harmony search algorithm was used as the optimization algorithm, and different regression models were used as predictive models. Multioutput regression is applied to predict the design variables such as section width, height, and reinforcement area. The results indicated that the random forest algorithm performed better than all other machine learning algorithms that have also achieved high accuracy.

Automated summary

CO2 emissions are a major environmental problem contributing to global warming. In order to prevent a potential climate crisis, this research proposes an engineering design solution to reduce CO2 emissions. The proposed solution includes an optimization-machine learning pipeline and a set of models trained to predict the design variables of an eco-friendly concrete column. The results indicate that the random forest algorithm outperforms other machine learning algorithms in terms of accuracy.