# Machine Learning Models for Ecofriendly Optimum Design of Reinforced Concrete Columns
## 4 Discussion  
Since RC columns are widely used, this increases CO\({}_{2}\) emissions. The optimum design of these structural elements must be done with a focus on minimizing the carbon footprint. In this study, we focus on the design optimization of RC concrete columns with the focus of minimizing the carbon footprint. A dataset of 4429 rows was produced by utilizing an HS optimization algorithm that generates optimal \(b\), \(h\), and \(A_{s}\) values of an RC rectangular column based on \(M\) and \(N\) values to conform to the optimal design objective. Next, we trained a set of ML algorithms by utilizing this dataset and derived ML models to predict optimal \(b\), \(h\), and \(A_{s}\) values based on input features \(M\) and \(N\). The last stage of the study was focused on a comparison of the prediction capability of ML models. Mostly, the coefficient of determination (R\({}^{2}\)) is used for the evaluation/comparison of ML models. In this sense, decision tree was found to be the most successful model (R\({}^{2}\): 0.996) among the foundational methods. Random forest was the most successful model among the ensemble methods (R\({}^{2}\): 0.9984). In addition, most boosting classifiers in the methods group performed well (R\({}^{2}\): 0.995-0.997). The highest accuracy (R\({}^{2}\) > 0.99) in different combination types tested for the voting technique was obtained in cases where ensemble methods (random forest, gradient boosting, and histogram-based gradient boosting) combination. This high accuracy is achieved as the result of the risk-reduction strategy applied by the voting approach. The highest accuracy (R\({}^{2}\): 0.96-0.99) in the stacking technique was obtained in cases where ensemble methods were combined.  
Among similar studies, [PERSON] et al. [87] aimed to predict the compressive strength and embodied carbon of cement replacement concrete by using machine learning algorithms (deep neural network (DNN), support vector regression (SVR), gradient boosting regression (GBR), random forest (RF), k-nearest neighbors (KNN), and decision tree regression (DTR)). GBR models achieved the best prediction of the compressive strength and embodied carbon. The R\({}^{2}\) of the GBR models for predicting the compressive strength and embodied carbon were 0.946 and 0.999, respectively. When compared with [87], our results are very similar in terms of performance metrics. Obtaining lower R\({}^{2}\) values in analyses with foundational methods confirmed that ensemble methods provide better results for this prediction process.  
In another similar effort, to estimate the environmental impacts of emissions from multiple construction activities, [PERSON] et al. [88] applied a random forest-based estimation method. The R\({}^{2}\) of the RF model used to estimate the construction phase carbon emissions during the early design phase was 0.605. Our results demonstrate a significant improvement in the accuracy of prediction (R\({}^{2}\) = 0.99), as a study with a similar focus.  
Since the main carbon emission from the concrete industry is due to the production of Portland cement, which is its main binder, finally in another similar study, [PERSON] et al. [89] predicted the compressive strength of environmentally friendly concrete by using hybrid machine learning based on 147 datasets. The coefficient of determination (R\({}^{2}\)) of their study's gradient boosting regressor model was 0.9528 while our study's coefficient of determination of the gradient boosting regressor model was 0.995, which indicates a significant improvement for a study with a similar focus.