# Research Progress on Machine Learning Prediction of Compressive Strength of Nano-Modified Concrete
## 1 Introduction
Researchers used NS and nanofibers to improve the strength, durability, and processability of cement composites [19]. Nano calcium carbonate can improve the early hydration reaction of cement and enhance the early strength, and the synergistic use of nano-concrete (NC) to improve the resistance to chloride penetration with polypropylene fibers (PPFs) can provide better durability performance for recycled aggregate concrete (RAC) [20]. Nano titanium dioxide not only improves the mechanical properties of concrete, but also improves durability, especially compressive strength, and the gradient lifting model more accurately predicts the compressive strength of nano titanium-based concrete [21]. Other than nano-silica, other nanomaterials are also been used to improve concrete. Carbon nanotubes (CNTs) can bridge cracks and improve toughness. Nano-alumina speeds up cement hydration and increases early strength. Nano-iron oxide helps reduce pores and improves resistance to chloride. Nano-clay makes the mix denser and more waterproof. Nano-calcium carbonate works as a nucleation site and helps early C-S-H formation. These materials improve concrete by filling pores, enhancing hydration, or strengthening the transition zone. Using different nano-additives together can further boost strength and durability, as shown in Table 1.  
The cost of testing nano-modified concrete is relatively high. At present, the design of mechanical properties is mainly based on experience and specifications, and there is no widely used prediction model. Machine learning has been used in many other engineering fields. In structural health monitoring, ML helps detect cracks and predict damage. In geotechnical engineering, it is used to forecast soil behavior and slope stability. ML also supports material design by predicting strength, durability, and thermal properties. These successful cases show that ML can handle complex, multi-factor problems. This makes it suitable for predicting the compressive strength of nano-modified concrete. With the rise in artificial intelligence in recent years, it has become a common practice to use ML methods in engineering problems [28; 29; 30; 31; 32]. ML is a subfield of AI that has many applications beyond simple data analysis. The regression function in ML has been used in multiple instances, one of which is to predict the compressive strength of concrete. ML uses algorithms that learn from input data and produce highly accurate output data compared to more traditional regression techniques [33]. Artificial Neural Networks (ANNs) and Support Vector Machines (SVMs) are the most popular machine-learning techniques for predicting the compressive strength of concrete. [PERSON] et al. [34] used an artificial neural network to predict the compressive strength of construction and demolition waste (CDW) concrete. [PERSON] et al. [35; 36] explored several ML methods including ANNs and SVMs for predicting the compressive strength of concrete. [PERSON] and [PERSON] [37] applied a weighted SVM-based prediction method to a more complex problem to predict the unconfined compressive strength of high-performance concrete (HPC). [PERSON] et al. [38] improved the least squares SVM with meta-heuristic optimization. [PERSON] et al. [7] used ANN to predict the environmental strength of concrete. [PERSON] et al. [39] predicted the strength of fiber concretes and ultrasonic pulse velocity based on heuristic regression methods. The analysis of the Web Of Science data from 2014 to 2024 is shown in Figure 1,