# Augmented Data-Driven Approach towards 3D Printed Concrete Mix Prediction
## 1 Introduction
This phenomenon, often referred to as cold joint formation, has a greater influence over the anisotropy of printed elements [38] and arises from the limited interaction between subsequently deposited layers [39]. So, the layers are required to be flowable, which facilitates smooth extrusion and stronger interlayer bonding; however, this negatively affects buildability [30]. This crucial balance, keeping in view the time-dependent rheological properties of concrete, is the most difficult component of 3 DCP, which is further aggravated by the variable mechanics of the printer design [16]. Most of the printable mix designs used in commercial-scale printing are protected as intellectual properties, while those available to researchers rely on an iterative approach for their formulation [40; 41; 42; 43; 44]. This way of developing the mixed design of printable concrete is costly and generates a huge amount  
Figure 1: (a) 3D printed “House Zero” in Austin (Reprinted from [35]) (b) Two-story 3D printed house under construction in Europe (Reprinted from [36]) (c) Earthquake-proof 3D printed house in Guatema (Reprinted from [37]). of waste, negating its inherent benefits and eventually restricting the widespread implementation of this technology in the construction sector [5; 45; 46; 47; 48]. A generalized interaction of concrete printing devices and rheological properties of concrete for 3 DCP is shown in Figure 2.  
The mixing technique is critical for 3D printable mixes to ensure uniform material distribution, optimal rheological properties, and consistent mechanical strength. Proper mixing enhances workability, prevents segregation, and ensures strong layer adhesion, leading to high-quality prints with reliable structural integrity [30]. Especially when adding supplementary cementitious materials (SCMs) for optimizing 3D printable mixes, crucial attention must be given to the mixing processes to ensure proper dispersion and distribution. Numerous researchers have explored innovative methods for mixing concrete specifically for 3D printing applications. Chemical additives that improve the buildability of concrete can cause poor pumpability; to address this, special nozzle-based mixing techniques can be implemented [49]. [PERSON] et al. [50] explored the feasibility of integrating an accelerating admixture at the print head using a static mixer and reported that this approach could produce printable mixtures with improved buildability. Similarly, for foaming concrete, a special foam concrete mixer can be designed on the printhead [51]. Mixing time is also crucial in terms of formulating a printable concrete with repeated properties. [PERSON] et al. achieved superior fiber dispersion for optimal material and printing performance through careful mix proportioning and utilizing component-specific mixing speeds [52]. Hence, innovations in 3 DCP mix design are crucial, as they address the issues of mix formulation and material consistency to enhance the overall quality and reliability of printed structures. Artificial intelligence (AI) in the construction sector has opened new possibilities; for example, AI can optimize project scheduling, identify potential risks before they occur, and use cameras with computer vision to ensure quality control by detecting defects in real-time [53; 54]. It can also predict the mix constituents required for the desired concrete properties [55; 56]. AI algorithms can analyze vast datasets encompassing material properties, environmental conditions, and desired performance metrics [57]. This enables them to predict the ideal concrete mix composition for a specific application, resulting in superior material performance, enhanced durability, and reduced environmental impact [58]. Like human brains--to learn new information, machine learning (ML) models analyze large amounts of data to understand its interrelationship. [PERSON] et al. used an artificial neural network (ANN) to effectively predict high-performance concrete's compressive and tensile  
Figure 2: Interaction of key 3 DCP parameters.