CARBON FOOTPRINT OPTIMIZED DESIGN OF SUSTAINABLE REINFORCED CONCRETE COLUMNS USING DEEP LEARNING

Abstract

The objective of this paper is to present a neural network (NN) model for the sustainable structural design optimization of rectangular reinforced concrete columns under biaxial bending and axial loading to minimize the embodied carbon from concrete and steel. Using the loading combination, height, and concrete class, the model predicts the section geometry and reinforcement ratio. To train the NN, a dataset of 195 million designs was generated with the OpenSees library following Eurocode. The dataset spans six concrete classes and five different column heights. Using the estimates of the embodied carbon for concrete and steel, the designs were evaluated and filtered before training. To illustrate the performance of our model, 30 columns for different loads, heights, and concrete classes were manually designed and compared with the NN output. The results showed a 24% average reduction of embodied carbon for the NN predicted designs. In addition, the manual process took approximately six minutes per design, while it took the NN 1.2 seconds for the same task.