# Research Progress on Machine Learning Prediction of Compressive Strength of Nano-Modified Concrete
## 1 Introduction
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Nano Additive** & **Typical Dosage** & **Main Effects** & **Notes/Findings** & **Key References** \\ \hline Nano-silica (NS) & 0–1.5\% & \begin{tabular}{c} Fills pores, accelerates \\ hydration, improves \\ compressive strength \\ \end{tabular} & \begin{tabular}{c} Most studied; optimum \\ \(\sim\)1\%; improves C–S–H \\ formation \\ \end{tabular} & [13; 18; 19; 22] \\ \hline \begin{tabular}{c} Carbon \\ nanotubes (CNTs) \\ \end{tabular} & 0.05–0.2\% & \begin{tabular}{c} Bridges micro-cracks, \\ enhances toughness \\ \end{tabular} & \begin{tabular}{c} High strength; dispersion \\ is challenging \\ \end{tabular} & [23; 24] \\ \hline Nano-alumina & 0.5–2\% & \begin{tabular}{c} Accelerates early \\ hydration, improves early \\ strength \\ \end{tabular} & \begin{tabular}{c} Works well with \\ supplementary \\ cementitious materials \\ (SCMs) \\ \end{tabular} & [17; 25] \\ \hline Nano-clay & \(\sim\)1\% & \begin{tabular}{c} Refines pore structure, \\ reduces permeability \\ \end{tabular} & \begin{tabular}{c} Synergistic effects with \\ NS \\ \end{tabular} & [14; 20] \\ \hline Nano-CaCO\({}_{3}\) & 1–3\% & \begin{tabular}{c} Provides nucleation sites, \\ enhances early hydration \\ and strength \\ \end{tabular} & \begin{tabular}{c} Good for early-age \\ performance; improves \\ workability \\ \end{tabular} & [20; 26] \\ \hline Nano-Fe\({}_{2}\)O\({}_{3}\) & 0.5–1\% & \begin{tabular}{c} Reduces pores, enhances \\ chloride resistance \\ \end{tabular} & \begin{tabular}{c} Less commonly used in \\ strength prediction \\ \end{tabular} & [17; 20] \\ \hline Nano-TiO\({}_{2}\) & 0.5–2\% & \
\begin{tabular}{c} Enhances compressive \\ strength and durability; \\ provides photocatalytic \\ properties \\ \end{tabular} &
\begin{tabular}{c} Often used in \\ combination with ML \\ models; effective under \\ UV exposure \\ \end{tabular} & [21; 22; 27] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Overview of nanomaterials used for concrete modification. which shows a significant increase in ML publications related to nano-modified concrete, and further, as shown in Figure 2, it is seen that the most relevant topics for nano-modified concrete in the last 10 years are DT, eXtreme Gradient Boosting (XGB), and ANN; the ML, nanomaterials, and machine learning part of the model are shown by visualization, which shows that the diverse applications of ML in nano-modified concrete. To support this review, a structured literature search was conducted. Sources were mainly retrieved from Web of Science. We used general combinations of keywords such as (\"nano\"OR \"nano concrete\") AND \"machine learning\". The publication years were limited to 2014-2024. Only English peer-reviewed journal articles and relevant conference papers were considered. The search results were manually screened based on titles and abstracts to exclude duplicates and unrelated studies. This article first outlines the steps and common algorithms for developing ML models for concrete strength prediction. It then examines the advancements made in applying ML to the prediction of nano-modified concrete strength and provides an outlook for the future direction of ML in nano-modified concrete. Figure 1: Machine learning publications related to nanomodified concrete in the last decade. Figure 2: Machine learning and nano-modified concrete related topics.