# Augmented Data-Driven Approach towards 3D Printed Concrete Mix Prediction
## 1 Introduction
strength [59]. [PERSON] et al. predicted the compressive strength of the concrete using a dataset containing the results of the rebound hammer test with root mean square error (RMSE) and mean absolute error (MAE) of 6.58 MPa and 5.27 MPa [60]. ML has also shown its widespread application in the 3D printing of filaments. Fused filament fabrication (FFF) is susceptible to inconsistencies in print quality and repeatability. However, the timely detection of anomalies by implementing ML algorithms can potentially mitigate these challenges [61]. Similarly, the advancements in metallurgy and materials science have relied heavily on trial-and-error methods using established scientific principles. However, the future lies in predictive capabilities anticipating and optimizing material properties, processing parameters, and final product characteristics before trial formulations [62]. Using ML to predict the properties of printable concrete before printing the elements can prove to be beneficial; however, a technology that is still in its developing phase poses many challenges when integrating with machine learning [63; 64; 65]. One major challenge is the lack of open-source data available for training of models [66]. As this technology is still new, the dataset available is limited in size and extent, restricting the learning capability of models [67; 68]. The second challenge is the highly complex nature of the mix designs of printable concrete [30]. The printability of concrete is dependent on many mix constituents, which are to be used as input parameters for model training [4]. The complexity is further compounded by taking into consideration the mechanical design of the concrete printer [69]. A higher workability is required for facilitating the smooth extrusion which can be achieved through the following: by increasing the water-to-cement ratio [26], the addition of superplasticizers [70] and aggregates [71], etc. Increasing the cement content facilitates even extrusion with suitable shape retention; however, this approach is costly and tends to have a higher rate of structuration, ultimately leading to clogging problems [72]. The addition of supplementary cementitious materials can result in decreased cement content, but they decrease the mechanical properties, such as anisotropic compressive strength and interlayer bonding [73]. Similarly, different mix constituents are printable at different extrusion and print speeds, although a balance between them is of vital importance [74]. Similarly, various researchers have also incorporated aggregates into printable mix designs with a size restriction per the nozzle area and the extrusion setup [75]. A higher content of fine aggregate densifies the mix, leading to a smooth extrusion. However, a higher content reduces the interlayer bonding with decreased compressive and flexural strength [76]. The tensile and flexural strength of 3D printed elements can be increased substantially by the addition of fibers; however, they tend to negatively affect the printability of concrete [41]. All these factors, along with their complex interdependency and requirements of printable concrete, need a wide and diverse database for models to learn effectively and provide accurate predictions [67; 77]. One possible approach in literature for overcoming the issue of limited data availability is data augmentation [78]. This technique has been used to improve the performance of machine learning models in various fields, such as image recognition, natural language processing, medical imaging, speech recognition, and autonomous driving [79; 80]. It creates new training data from existing data through various augmentation techniques like adding artificial noise or systemically extending the original data points [78]. The number of data points is increased while ensuring that data statistics between the real and synthetic data are comparable. Several studies have explored the application of generative models to address this challenge of limited data availability in concrete science [81; 82].