# Additive Manufacturing as an Alternative to Core Sampling in Concrete Strength Assessment
## 1 Introduction
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Reference** & **Materials** & **Printing Method** & **Testing Method** & **Pinter Model** & **Key Findings/Limitations** \\ \hline \multirow{8}{*}{[10]} & \multirow{4}{*}{\begin{tabular}{c} Portland cement with \\ various mineral additives \\ (ground slag, dry ash, \\ limestone powder, \\ gramite dust, \\ alkali-activated binder) \\ \end{tabular} } & \multirow{4}{*}{\begin{tabular}{c} \begin{tabular}{c} Temperature strength \\ \(\mathrm{Testing}\) of concrete from \\ laboratory 3D printer \\ and cast beams \\ (\(160\times 40\times 40\) mm) made \\ using vibration table \\ \end{tabular} } & \multirow{4}{*}{\begin{tabular}{c} \begin{tabular}{c} Compressive strength \\ results reported for 28-day \\ cured concrete. Lower \\ strength observed in \\ 3D printed specimens; \\ method for testing \\ hardened printed concrete \\ not described in detail. \\ \end{tabular} } \\ \hline \multirow{8}{*}{[11]} & \multirow{4}{*}{\begin{tabular}{c} Cement-free concrete- \\ meta coalition, potassium \\ silicate solution, \\ volallonite, fiberglass. \\ \end{tabular} } & \multirow{4}{*}{\begin{tabular}{c} Manually extruded printed \\ mixture \\ \end{tabular} } & \multirow{4}{*}{\
\begin{tabular}{c} Laboratory beam \\ specimens (\(160\) mm \(\times 40\) mm \(\times 40\) mm) \\ were fabricated and tested \\ \end{tabular} } & \multirow{4}{*}{Not available} & \multirow{4}{*}{
\begin{tabular}{c} Only manually prepared \\ specimens were tested, not \\ directly printed concrete. \\ \end{tabular} } \\  & & & & \\ \cline{1-1} \cline{3-5}  & & & & \\ \cline{1-1} \cline{3-5}  & & & & \\ \cline{1-1} \cline{3-5}  & & & & \\ \cline{1-1} \cline{3-5}  & & & & \\ \cline{1-1} \cline{3-5}  & & & & \\ \cline{1-1} \cline{3-5}  & & & & \\ \cline{1-1} \cline{3-5}  & & & & \\ \cline{1-1} \cline{3-5}  & & & & \\ \cline{1-1} \cline{3-5}  & & & & \\ \cline{1-1} \cline{3-5}  & & & & \\ \cline{1-1} \cline{3-5}  & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._There also exists a non-destructive testing method that utilises a hammer imprint on the concrete surface. The strength is estimated by comparing the resulting indentation with a calibrated scale on a measuring ruler [28]. However, this method also has limitations (text in source incomplete). Another commonly used approach in construction is strength testing using standard-shaped specimens, such as cubes or cylinders, extracted from hardened concrete. These samples are then tested under compression in a laboratory. The main drawback of this method is the need to core or saw standard-shaped samples from the structure, which requires portable cutting equipment and subsequent transportation to the lab. This process can compromise the integrity of the structure and reduce its load-bearing capacity. A reliable prediction of concrete compressive strength is essential for the safe and durable use of structural members. Over the past decade, machine-learning techniques have attracted growing interest because they can detect relationships in large, heterogeneous datasets that are difficult to recognise using human reasoning alone [29]. A recent survey of industrial concretes confirmed an \"explosion\" of data-driven studies devoted to strength forecasting and mix optimisation [30]. Within the specific field of 3D printed concrete, [PERSON] et al. developed machine-learning models--artificial neural networks, decision trees, random forests, support-vector regression and linear regression--to predict both compressive and flexural strength from mixed experimental datasets; the random-forest model achieved correlation coefficients between 0.96 and 0.98, far surpassing traditional empirical formulas and thereby supporting more accurate deployment of 3D printing in construction [31]. [PERSON] et al.