ArticleMaterials (Basel, Switzerland)2021
Simulation of Depth of Wear of Eco-Friendly Concrete Using Machine Learning Based Computational Approaches.
Article in Materials (Basel, Switzerland), 2021. 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
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
- Modeling mechanical properties of rubberized concrete using gene expression programming (GEP) and random forest: a comparative study.Scientific reports · 2026Article
- Modeling the mechanical properties of lightweight high-strength concrete incorporating supplementary cementitious materials using multi-expression programming and random forest.Scientific reports · 2026Article
- Comparative performance evaluation of machine learning models for predicting the ultimate bearing capacity of shallow foundations on granular soils.Scientific reports · 2025Article
- Predicting the compressive strength of concrete incorporating waste powders exposed to elevated temperatures utilizing machine learning.Scientific reports · 2025Article
- Comparative analysis of various machine learning algorithms to predict strength properties of sustainable green concrete containing waste foundry sand.Scientific reports · 2024Article
- Article
- An efficient machine learning approach for predicting concrete chloride resistance using a comprehensive dataset.Scientific reports · 2023Article
- Prediction of Autogenous Shrinkage of Concrete Incorporating Super Absorbent Polymer and Waste Materials through Individual and Ensemble Machine Learning Approaches.Materials (Basel, Switzerland) · 2022Article
- Forecasting Compressive Strength of RHA Based Concrete Using Multi-Expression Programming.Materials (Basel, Switzerland) · 2022Article
- Prediction of Mechanical Properties of Fly-Ash/Slag-Based Geopolymer Concrete Using Ensemble and Non-Ensemble Machine-Learning Techniques.Materials (Basel, Switzerland) · 2022Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
To avoid time-consuming, costly, and laborious experimental tests that require skilled personnel, an effort has been made to formulate the depth of wear of fly-ash concrete using a comparative study of machine learning techniques, namely random forest regression (RFR) and gene expression programming (GEP). A widespread database comprising 216 experimental records was constructed from available research. The database includes depth of wear as a response parameter and nine different explanatory variables, i.e., cement content, fly ash, water content, fine and coarse aggregate, plasticizer, air-entraining agent, age of concrete, and time of testing. The performance of the models was judged via statistical metrics. The GEP model gives better performance with R
Indexed as
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.