ArticleMaterials (Basel, Switzerland)2023
Ensemble Machine-Learning-Based Prediction Models for the Compressive Strength of Recycled Powder Mortar.
Article in Materials (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- The Impact of 3D Printing on Mortar Strength and Flexibility: A Comparative Analysis of Conventional and Additive Manufacturing Techniques.Materials (Basel, Switzerland) · 2026Article
- A TabPFN-based prediction system for refractive error and dry eye comorbidity: a retrospective study using large-scale real-world data.Frontiers in cell and developmental biology · 2026Article
- Compressive Strength-Based Classification of Eco-Friendly Concretes Using Machine Learning Models.Materials (Basel, Switzerland) · 2025Article
- A Comprehensive Review of Numerical and Machine Learning Approaches for Predicting Concrete Properties: From Fresh to Long-Term.Materials (Basel, Switzerland) · 2025Review
- Interpretable machine learning approaches to assess the compressive strength of metakaolin blended sustainable cement mortar.Scientific reports · 2025Article
- Real-world database evaluation of drug-associated vitreous opacities and machine learning for clinical interpretability.Frontiers in cell and developmental biology · 2025Article
- Uveal melanoma distant metastasis prediction system: A retrospective observational study based on machine learning.Cancer science · 2024Observational
- Prediction of Mortar Compressive Strength Based on Modern Minor-Destructive Tests.Materials (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Recycled powder (RP) serves as a potential and prospective substitute for cementitious materials in concrete. The compressive strength of RP mortar is a pivotal factor affecting the mechanical properties of RP concrete. The application of machine learning (ML) approaches in the engineering problems, particularly for predicting the mechanical properties of construction materials, leads to high prediction accuracy and low experimental costs. In this study, 204 groups of RP mortar compression experimental data are collected from the literature to establish a dataset for ML, including 163 groups in the training set and 41 groups in the test set. Four ensemble ML models, namely eXtreme Gradient-Boosting (XGBoost), Random Forest (RF), Light Gradient-Boosting Machine (LightGBM) and Adaptive Boosting (AdaBoost), were selected to predict the compressive strength of RP mortar. The comparative results demonstrate that XGBoost has the highest prediction accuracy when the a10-index, MAE, RMSE and 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.