Cargando…

Prediction of Geopolymer Concrete Compressive Strength Using Novel Machine Learning Algorithms

The innovation of geopolymer concrete (GPC) plays a vital role not only in reducing the environmental threat but also as an exceptional material for sustainable development. The application of supervised machine learning (ML) algorithms to forecast the mechanical properties of concrete also has a si...

Descripción completa

Detalles Bibliográficos
Autores principales:	Ahmad, Ayaz, Ahmad, Waqas, Chaiyasarn, Krisada, Ostrowski, Krzysztof Adam, Aslam, Fahid, Zajdel, Paulina, Joyklad, Panuwat
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	MDPI 2021
Materias:	Article
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8512145/ https://www.ncbi.nlm.nih.gov/pubmed/34641204 http://dx.doi.org/10.3390/polym13193389

Ejemplares similares

Application of Advanced Machine Learning Approaches to Predict the Compressive Strength of Concrete Containing Supplementary Cementitious Materials
por: Ahmad, Waqas, et al.
Publicado: (2021)

Computation of High-Performance Concrete Compressive Strength Using Standalone and Ensembled Machine Learning Techniques
por: Xu, Yue, et al.
Publicado: (2021)

Predicting the Mechanical Properties of RCA-Based Concrete Using Supervised Machine Learning Algorithms
por: Shang, Meijun, et al.
Publicado: (2022)

Prediction of Compressive Strength of Fly Ash Based Concrete Using Individual and Ensemble Algorithm
por: Ahmad, Ayaz, et al.
Publicado: (2021)

Finite Element Analysis of Glass Fiber-Reinforced Polymer-(GFRP) Reinforced Continuous Concrete Beams
por: Ahmad, Hazem, et al.
Publicado: (2021)

Low-Cost Fiber Chopped Strand Mat Composites for Compressive Stress and Strain Enhancement of Concrete Made with Brick Waste Aggregates
por: Joyklad, Panuwat, et al.
Publicado: (2022)

Comparison of Prediction Models Based on Machine Learning for the Compressive Strength Estimation of Recycled Aggregate Concrete
por: Khan, Kaffayatullah, et al.
Publicado: (2022)

Application of Soft Computing Techniques to Predict the Strength of Geopolymer Composites
por: Wang, Qichen, et al.
Publicado: (2022)

Mechanical Experiments on Concrete with Hybrid Fiber Reinforcement for Structural Rehabilitation
por: Shahid, Muhammad Asharib, et al.
Publicado: (2022)

Comparative Study of Supervised Machine Learning Algorithms for Predicting the Compressive Strength of Concrete at High Temperature
por: Ahmad, Ayaz, et al.
Publicado: (2021)

Use of Natural and Synthetic Fiber-Reinforced Composites for Punching Shear of Flat Slabs: A Comparative Study
por: Joyklad, Panuwat, et al.
Publicado: (2022)

Structural Behavior of Large-Scale Hollow Section RC Beams and Strength Enhancement Using Carbon Fiber Reinforced Polymer (CFRP) Composites
por: Sirisonthi, Athasit, et al.
Publicado: (2021)

Evaluation of Artificial Intelligence Methods to Estimate the Compressive Strength of Geopolymers
por: Zou, Yong, et al.
Publicado: (2022)

Assessment of Soft Computing Techniques for the Prediction of Compressive Strength of Bacterial Concrete
por: Almohammed, Fadi, et al.
Publicado: (2022)

Effect of Coconut Fiber Length and Content on Properties of High Strength Concrete
por: Ahmad, Waqas, et al.
Publicado: (2020)

Compressive Strength Estimation of Steel-Fiber-Reinforced Concrete and Raw Material Interactions Using Advanced Algorithms
por: Khan, Kaffayatullah, et al.
Publicado: (2022)

A Comparative Study for the Prediction of the Compressive Strength of Self-Compacting Concrete Modified with Fly Ash
por: Farooq, Furqan, et al.
Publicado: (2021)

Analyzing the Compressive Strength of Ceramic Waste-Based Concrete Using Experiment and Artificial Neural Network (ANN) Approach
por: Song, Hongwei, et al.
Publicado: (2021)

Flexural Behavior of Natural Hybrid FRP-Strengthened RC Beams and Strain Measurements Using BOTDA
por: Chaiyasarn, Krisada, et al.
Publicado: (2021)

Artificial Intelligence Approaches for Prediction of Compressive Strength of Geopolymer Concrete
por: Dao, Dong Van, et al.
Publicado: (2019)

Application of Ensemble Machine Learning Methods to Estimate the Compressive Strength of Fiber-Reinforced Nano-Silica Modified Concrete
por: Anjum, Madiha, et al.
Publicado: (2022)

Using Machine Learning Algorithms to Estimate the Compressive Property of High Strength Fiber Reinforced Concrete
por: Dai, Li, et al.
Publicado: (2022)

Application of Gene Expression Programming (GEP) for the Prediction of Compressive Strength of Geopolymer Concrete
por: Khan, Mohsin Ali, et al.
Publicado: (2021)

Soft computing models to predict the compressive strength of GGBS/FA- geopolymer concrete
por: Ahmed, Hemn U., et al.
Publicado: (2022)

Application of Soft-Computing Methods to Evaluate the Compressive Strength of Self-Compacting Concrete
por: Amin, Muhammad Nasir, et al.
Publicado: (2022)

Compressive Strength Estimation of Geopolymer Composites through Novel Computational Approaches
por: Amin, Muhammad Nasir, et al.
Publicado: (2022)

Prediction of Mechanical Properties of Fly-Ash/Slag-Based Geopolymer Concrete Using Ensemble and Non-Ensemble Machine-Learning Techniques
por: Amin, Muhammad Nasir, et al.
Publicado: (2022)

Application of Machine Learning Approaches to Predict the Strength Property of Geopolymer Concrete
por: Cao, Rongchuan, et al.
Publicado: (2022)

Machine Learning Prediction Models to Evaluate the Strength of Recycled Aggregate Concrete
por: Yuan, Xiongzhou, et al.
Publicado: (2022)

The Effect of Different Parameters on the Development of Compressive Strength of Oil Palm Shell Geopolymer Concrete
por: Kupaei, Ramin Hosseini, et al.
Publicado: (2014)

Prediction of the Compressive Strength of Fly Ash Geopolymer Concrete by an Optimised Neural Network Model
por: Khalaf, Ali Abdulhasan, et al.
Publicado: (2022)

Assessment of Artificial Intelligence Strategies to Estimate the Strength of Geopolymer Composites and Influence of Input Parameters
por: Khan, Kaffayatullah, et al.
Publicado: (2022)

Axial Compressive Behavior of Geopolymer Recycled Lump Concrete
por: Zhang, Haiyan, et al.
Publicado: (2020)

Compressive Strength and Chloride Resistance of Slag/Metakaolin-Based Ultra-High-Performance Geopolymer Concrete
por: Zhang, Yufei, et al.
Publicado: (2022)

Exploring the Use of Waste Marble Powder in Concrete and Predicting Its Strength with Different Advanced Algorithms
por: Khan, Kaffayatullah, et al.
Publicado: (2022)

Optimization of Alkaline Activator on the Strength Properties of Geopolymer Concrete
por: Shilar, Fatheali A., et al.
Publicado: (2022)

Simulation of Depth of Wear of Eco-Friendly Concrete Using Machine Learning Based Computational Approaches
por: Khan, Mohsin Ali, et al.
Publicado: (2021)

Axial Compressive Strength Models of Eccentrically-Loaded Rectangular Reinforced Concrete Columns Confined with FRP
por: Isleem, Haytham F., et al.
Publicado: (2021)

A Systematic Review of the Research Development on the Application of Machine Learning for Concrete
por: Khan, Kaffayatullah, et al.
Publicado: (2022)

Statistical Methods for Modeling the Compressive Strength of Geopolymer Mortar
por: Ahmed, Hemn Unis, et al.
Publicado: (2022)

Cannot write session to /tmp/vufind_sessions/sess_cuc92p7oprfufp7t103btmg890