# Augmented Data-Driven Approach towards 3D Printed Concrete Mix Prediction
## 1 Introduction
[PERSON] et al. investigated the impact of data augmentation on the performance of various machine learning regression models by introducing Gaussian noise to the existing dataset and found that the average error decreases by 2.7%, 3%, and 0.8% [83]. [PERSON] et al. showed that, after 250,000 generations of training data set through the generative adversarial network (GAN) model, the classification accuracy improved by 82% [84]. For concrete printing, the dataset available is very small. [PERSON] et al. used 299 data points to predict the anisotropic compressive strength of 3D-printed fiber-reinforced concrete [85]. [PERSON] et al. developed support vector machine (SVM), decision tree regression (DTR), extreme gradient boosting (XGBoost), and Gaussian process regressor (GPR) models by using 77 mix designs for anisotropic flexural strength and 49 mix designs for tensile strength of 3D printed fiber-reinforced concrete [86]. [PERSON] et al. proposed ML models for the prediction of the yield stress of concrete printing with R\({}^{2}\) scores of 0.99 and 0.98 for light gradient boosting machines (LightGBM) and XGBoost [87]. So, the limited data on concrete printing and its complex material interactions necessitate the integration of advanced AI models for data augmentation with predictive models. This will provide the models with larger datasets, allowing for more accurate forecasts and ultimately facilitating better mix design prediction to cater to various printer designs and printing requirements. In this study, the dataset used to formulate the mix design of printable concrete was extracted from the literature. This dataset included mix constituents as input features, while the output features were slump flow (mm), cast compressive strength (MPa), and anisotropic compressive strength (MPa). The slump flow values indicate the printability of the concrete so that the predicted mix will have optimal compressive strength and printability. The dataset comprised 18 input features and five output features. Subsequently, data augmentation techniques were applied, and statistical and correlation analyses were performed to verify the compatibility of the augmented data with the original data. Three different machine-learning models were then trained on the augmented dataset and tested on the original data. Finally, the model's performance was evaluated using various performance metrics. This research proposes a novel approach to overcome the issue of limited data availability for 3 DCP through the use of machine learning models trained on augmented datasets. The data augmentation allowed for enhancing the predictive ability of the models on an already limited data set further limited by the inclusion of both fresh and hardened-state properties. The ability to predict both fresh and hardened-state properties enables precise, efficient concrete mix designs that meet strength and printability requirements, reducing iterations and saving resources. Including fresh-state properties ensures compatibility across various 3 DCP setups, facilitating wide-scale implementation and suitability for large-scale 3 DPC construction projects. This research holds significant value for advancing the field of printable concrete by offering an efficient and accurate method for mix design development, ultimately facilitating broader adoption of 3 DCP technology in the construction industry.