ArticleScientific reports2023
Unboxing machine learning models for concrete strength prediction using XAI.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 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
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
11 citing papers in PubMed, 69 citations in OpenAlex.
- Concrete strength prediction using domain-driven feature engineering and explainable LightGBM.Scientific reports · 2026Article
- Performance evaluation of fly-ash and silica-fume based geopolymer concrete at different alkaline molarities with machine learning-based strength prediction.Scientific reports · 2026Article
- Compressive strength of mortar incorporating recycled nonmetallic printed circuit board waste: experimental and interpretable machine learning analysis.Scientific reports · 2026Article
- Evaluating the predictive accuracy of supervised machine learning models to explore the mechanical strength of blast furnace slag incorporated concrete.Scientific reports · 2026Article
- Compressive strength prediction of carbonated recycled aggregate concrete using regression based machine learning models.Scientific reports · 2026Article
- Performance Analysis of Boosting-Based Machine Learning Models for Predicting the Compressive Strength of Biochar-Cementitious Composites.Materials (Basel, Switzerland) · 2026Article
- Machine learning-driven optimization of compressive and tensile strength in concrete with GGBS, eggshell powder, and waste glass powder.Scientific reports · 2025Article
- Sustainable Concrete with Waste Tire Rubber and Recycled Steel Fibers: Experimental Insights and Hybrid PINN-CatBoost Prediction.Polymers · 2025Article
- Prediction of ultimate strength and strain in FRP wrapped oval shaped concrete columns using machine learning.Scientific reports · 2025Article
- Numerical and machine learning modeling of GFRP confined concrete-steel hollow elliptical columns.Scientific reports · 2024Article
- Machine learning and interactive GUI for concrete compressive strength prediction.Scientific reports · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Concrete is a cost-effective construction material widely used in various building infrastructure projects. High-performance concrete, characterized by strength and durability, is crucial for structures that must withstand heavy loads and extreme weather conditions. Accurate prediction of concrete strength under different mixtures and loading conditions is essential for optimizing performance, reducing costs, and enhancing safety. Recent advancements in machine learning offer solutions to challenges in structural engineering, including concrete strength prediction. This paper evaluated the performance of eight popular machine learning models, encompassing regression methods such as Linear, Ridge, and LASSO, as well as tree-based models like Decision Trees, Random Forests, XGBoost, SVM, and ANN. The assessment was conducted using a standard dataset comprising 1030 concrete samples. Our experimental results demonstrated that ensemble learning techniques, notably XGBoost, outperformed other algorithms with an R-Square (R
Identifiers
What OpenQuestion holds
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.