Research Progress on Machine Learning Prediction of Compressive Strength of Nano-Modified Concrete
Source abstract
Abstract
Nano-modified concrete has attracted wide attention due to its improved mechanical properties. Among them, compressive strength is the most critical indicator. However, testing nano-concrete is costly and complex because it requires control over many factors, such as nanoparticle content and dispersion. Machine learning offers a data-driven way to predict compressive strength more efficiently. It reduces trial-and-error efforts and supports mix design optimization. Currently, machine learning is more adept at handling complicated datasets than experimental and traditional statistical models. In this article, the development of machine learning research in predicting the strength of concrete enhanced by nanoparticles is reviewed. First, we systematically outline a three-phase ML framework encompassing data curation, model development, and validation protocols; next, popular algorithms and their uses in predicting the strength of nano-modified concrete are evaluated, such as Artificial Neural Networks, K-Nearest Neighbor, Random Forest, etc. Ultimately, the article offers a forward-looking perspective on how future machine learning advancements can foster and accelerate the development of nano-modified concrete.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Innovative concrete reinforcement materials
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
College of Civil and Transportation Engineering, Hohai University, Nanjing 210098, China
College of Materials Science and Engineering, Hohai University, Changzhou 213000, China
Department of Ocean Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China
Funding
National Natural Science Foundation of China · Natural Science Foun-dation of Jiangsu Province · Guangdong Basic and Applied Basic Research Foundation · Shenzhen Science and Technology Program
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