# Machine Learning Models for Ecofriendly Optimum Design of Reinforced Concrete Columns
## 1 Introduction
In the study, columns under uniaxial bending and axial load were examined, and the bending axis has been taken as similar in all cases for comparison purposes. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**County** & **2015** & **2016** & **2017** & **2018** & **2019** & **2020** \\ \hline China & 2350 & 2403 & 2320 & 2370 & 2300 & 2200 \\ India & 270 & 290 & 290 & 330 & 320 & 290 \\ USA & 83.4 & 84.7 & 86.1 & 87.8 & 88.6 & 89 \\ Brazil & 72 & 57.8 & 54 & 53.5 & 53.4 & 60 \\ Turkey & 71.4 & 75.4 & 80.6 & 72.5 & 57 & 72.3 \\ Russia & 69 & 56 & 54.7 & 53.7 & 54.1 & 56 \\ Indonesia & 65 & 61.3 & 68 & 70.8 & 64.2 & 64.8 \\ South Korea & 63 & 56.7 & 57.9 & 55 & 56.4 & 50 \\ Japan & 55 & 3.4 & 55.5 & 55.3 & 55.2 & 52.1 \\ Saudi Arabia & 55 & 55.9 & 47.1 & 42.2 & 52.2 & 53.4 \\ Germany & 31.1 & 32.7 & 34 & 33.7 & 34.2 & 35.5 \\ Italy & 20.8 & 19.3 & 19.3 & 19.3 & 19.2 & 18.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Cement production by countries (million tons) [14]. Recently, machine learning (ML) has been used in the field of civil engineering. [PERSON] et al. [16] intended to create machine learning-based regression models to predict self-compacting concrete compressive strength. The results showed that the random forest model forecasts concrete's compressive strength accurately. [PERSON] and [PERSON] [17] introduced an ML model for calculating the ultimate torsion strength of concrete beams strengthened by using externally bonded fiber-reinforced polymer. The model showed improved agreement and consistency with experimental results compared to existing models in the literature. [PERSON] et al. [18] presented the application of popular ML algorithms in the prediction of the shear resistance of the steel channel sections. The results indicated that the implemented ML models exceed the prediction accuracy of the available design equations in estimating the shear capacity of the steel channel section. [PERSON] et al. [19] developed a new model for predicting bearing capacity by using an extreme gradient boosting (XGBoost) algorithm. The results showed that the XGBoost algorithm has the most accurate predictions for all models developed. [PERSON] et al. [20] aimed to explore new ML methods to automatically classify soil for minimizing the time and cost of the classification process. The results indicated that tree-based foundational methods/classifiers, such as the decision tree classifier, and gradient boosting-based ensemble methods provide very good performance. The damage to nature due to global warming has forced researchers to think of an environmentally friendly design. As indicated in the literature, various studies have been carried out for reducing CO2 emissions of reinforced concrete system design by using different datasets and various optimization and ML methods. [PERSON] et al. [21] investigated the sustainable design (minimum cost and CO2 emissions) of the RC retaining walls by using the harmony search (HS) algorithm. Their proposed approach performs well with regard to economic and ecological results. [PERSON] et al. [22] showed that variations in the span and load of RC slabs can change their environmental sustainability. The research results indicated that composite slabs were widely recommended in engineering applications from the view of environmental sustainability. [PERSON] et al. [23] investigated the concrete manufacturing process's potential effect on global warming. According to the results, while under different functional units, the environmental performance of composite and cast-in-situ floors varies. [PERSON] and [PERSON] [24] investigated the effect of using a void deck slab (VDS) system instead of an ordinary reinforced concrete slab on CO2 emissions. The results revealed the total CO2 emissions of an ordinary RC slab are more than those of the voided slab system.