ArticleNanomaterials (Basel, Switzerland)2025
Multi-Objective Optimization for Nano-Silica-Modified Concrete Based on Explainable Machine Learning.
Article in Nanomaterials (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
4 citing papers in PubMed.
- Machine-Learning-Driven Performance Optimization of Nano-Modified Construction Materials: A Bibliometric Analysis and Systematic Review.Nanomaterials (Basel, Switzerland) · 2026Review
- Multi-Objective Optimization and Prediction of Mechanical Properties of Green Basalt Fiber-Reinforced Concrete Using Evolutionary ML Algorithms.Materials (Basel, Switzerland) · 2026Article
- Data-driven optimisation of sustainable high-performance concrete incorporating SCMs, biomass ash, and graphene nanoplatelets.Scientific reports · 2026Article
- Transforming Nanomaterials Development with Artificial Intelligence Techniques.Nanotechnology, science and applications · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
Nano-silica modified concrete (NSC) has been widely applied in engineering practice. However, conventional manual mix proportion design is both time-consuming and costly. In this study, four machine learning models-XGBoost, CatBoost, Random Forest, and AdaBoost-were trained to predict the compressive strength of NSC. Based on the best-performing model, the NSGA-II algorithm was employed to develop a multi-objective optimization framework, considering compressive strength, cost, and carbon emissions as objectives. The results indicated that XGBoost achieved the highest accuracy, with R
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.