ArticleMaterials (Basel, Switzerland)2020
Investigation and Optimization of the C-ANN Structure in Predicting the Compressive Strength of Foamed Concrete.
Article in Materials (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed, 101 citations in OpenAlex.
- Optimizing the mechanical performance of sustainable industrial waste modified concrete using supervised machine learning modeling and feature importance analysis.Scientific reports · 2026Article
- Mathematical modeling and artificial neural network of textile fabric reinforced foamed concrete for enhanced composite material performance.Scientific reports · 2026Article
- A review on computer-aided diagnostic system to classify the disorders of the gastrointestinal tract.European journal of medical research · 2025Review
- Predicting High-Strength Concrete's Compressive Strength: A Comparative Study of Artificial Neural Networks, Adaptive Neuro-Fuzzy Inference System, and Response Surface Methodology.Materials (Basel, Switzerland) · 2024Article
- Comparative analysis of various machine learning algorithms to predict strength properties of sustainable green concrete containing waste foundry sand.Scientific reports · 2024Article
- Systematic literature review on the application of machine learning for the prediction of properties of different types of concrete.PeerJ. Computer science · 2024Article
- Estimating Compressive Strength of Concrete Using Neural Electromagnetic Field Optimization.Materials (Basel, Switzerland) · 2023Article
- Compressive Strength Prediction of Rice Husk Ash Concrete Using a Hybrid Artificial Neural Network Model.Materials (Basel, Switzerland) · 2023Article
- Comparison of various machine learning algorithms used for compressive strength prediction of steel fiber-reinforced concrete.Scientific reports · 2023Article
- Interpretable Machine Learning for Prediction of Post-Fire Self-Healing of Concrete.Materials (Basel, Switzerland) · 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
- Fatigue Performance Prediction of RC Beams Based on Optimized Machine Learning Technology.Materials (Basel, Switzerland) · 2022Article
- Study on Impermeability of Foamed Concrete Containing Municipal Solid Waste Incineration Powder.Materials (Basel, Switzerland) · 2022Article
- Prediction of Compressive Strength of Sustainable Foam Concrete Using Individual and Ensemble Machine Learning Approaches.Materials (Basel, Switzerland) · 2022Article
- Modeling of Mechanical Properties of Silica Fume-Based Green Concrete Using Machine Learning Techniques.Polymers · 2021Article
- Predicting the Ultimate Axial Capacity of Uniaxially Loaded CFST Columns Using Multiphysics Artificial Intelligence.Materials (Basel, Switzerland) · 2021Article
- Predictive Modeling of Mechanical Properties of Silica Fume-Based Green Concrete Using Artificial Intelligence Approaches: MLPNN, ANFIS, and GEP.Materials (Basel, Switzerland) · 2021Article
- Investigation of ANN architecture for predicting shear strength of fiber reinforcement bars concrete beams.PloS one · 2021Article
- A Novel Hybrid Model Based on a Feedforward Neural Network and One Step Secant Algorithm for Prediction of Load-Bearing Capacity of Rectangular Concrete-Filled Steel Tube Columns.Molecules (Basel, Switzerland) · 2020Article
- Cost-Effective Approaches Based on Machine Learning to Predict Dynamic Modulus of Warm Mix Asphalt with High Reclaimed Asphalt Pavement.Materials (Basel, Switzerland) · 2020Article
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Development of Foamed Concrete (FC) and incessant increases in fabrication technology have paved the way for many promising civil engineering applications. Nevertheless, the design of FC requires a large number of experiments to determine the appropriate Compressive Strength (CS). Employment of machine learning algorithms to take advantage of the existing experiments database has been attempted, but model performance can still be improved. In this study, the performance of an Artificial Neural Network (ANN) was fully analyzed to predict the 28 days CS of FC. Monte Carlo simulations (MCS) were used to statistically analyze the convergence of the modeled results under the effect of random sampling strategies and the network structures selected. Various statistical measures such as Coefficient of Determination (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.