# Research Progress on Machine Learning Prediction of Compressive Strength of Nano-Modified Concrete
## 1 Introduction  
Concrete strength is key to evaluating the stability of reinforced concrete structures. Pressure testing is a standard method used to determine this strength. It depends on cement ratios, aggregates, and moisture content. However, testing is expensive. ML algorithms are now used for predicting strength based on data [1, 2, 3, 4, 5]. ML uses statistical methods to learn from existing data and produce accurate predictions [6]. Traditional mix designs rely on regression models. But these are less accurate for modern cement composites. As a result, ML models are being introduced to improve prediction accuracy. In recent years, with the development of Big Data, artificial intelligence (AI), and other technologies, ML prediction of some properties of concrete has gradually emerged in the construction industry [7, 8, 9, 10]. As the material of choice for infrastructure construction, concrete's mechanical properties are a primary evaluation index. Prediction models based on ML have been widely investigated to evaluate concrete's compressive strength and other related properties [11, 12]. The high cost of mechanical property testing and lack of reliable prediction models for nano-modified concrete constitute a critical research challenge in construction materials science. Traditional concrete strength evaluation relies on standardized pressure testing based on cement-aggregate ratios and moisture content. However, testing nano-modifiedconcrete involves additional complexities including nanoparticle dispersion uniformity, dosage optimization (e.g., 0-1.5% nano-silica content [13]), and multi-phase interface effects, leading to exponentially increased experimental costs. This creates an urgent need for accurate predictive models to guide material design and application. Nanomaterials are materials with at least one dimension under 100 nanometers. They have high surface energy, reactivity, and strength due to their size. These features improve composite materials. For example, [PERSON] et al. [14] reviewed nanotechnology in fiber-reinforced polymer composites. Nanomaterials help concrete in three main ways. First, their chemical makeup matters. For example, nano-silica reacts with calcium hydroxide forming an extra C-S-H gel, which makes the concrete stronger. Second, their tiny size lets them fill in pores, making the concrete denser and stronger. Third, the shape of the particles also matters. Round particles fill gaps, and long or thin particles can connect cracks and improve toughness. The use of nanomaterials to improve the typical properties of concrete has garnered a lot of interest. Nanomaterials possess properties such as high surface activity due to their small particle size but the large specific surface area, and ordinary concrete needs to be compounded with other materials due to its natural defects. In terms of enhancing the durability and mechanical properties of cementitious composites, the report of RILEM Technical Committee 197-Nanotechnology in Construction Materials (TC 197-NCM) [15] explores related applications. The application of nanoscience and nanotechnology to cementitious composites was reported as early as the late 1980s and research in this area has been active for almost two decades [16]. Nano-metallic and non-metallic oxide cementitious composites were first used to modify or enhance the properties of cementitious materials [17]. After recognizing the nanoscale structure and discovering that substituting nano-silica (NS) for cement reduces the cost of concrete and reduces the carbon footprint of the concrete product [13], work was initiated on silica nanoparticles and nanofibers to improve the strength of cementitious materials, etc. [18]. NS can increase the hydration reaction rate and improve the hydration effect of cement, and nanoscale particle filling can improve the performance of composites. It was found that the compressive strength of concrete increased significantly when the NS dosage ranged from 0 to 1.5%, and gradually increased with the increase in NS dosage.