Cargando…

Assessment of Artificial Intelligence Strategies to Estimate the Strength of Geopolymer Composites and Influence of Input Parameters

Geopolymers might be the superlative alternative to conventional cement because it is produced from aluminosilicate-rich waste sources to eliminate the issues associated with its manufacture and use. Geopolymer composites (GPCs) are gaining popularity, and their research is expanding. However, casti...

Descripción completa

Detalles Bibliográficos
Autores principales:	Khan, Kaffayatullah, Ahmad, Waqas, Amin, Muhammad Nasir, Ahmad, Ayaz, Nazar, Sohaib, Al-Faiad, Majdi Adel
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	MDPI 2022
Materias:	Article
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9231083/ https://www.ncbi.nlm.nih.gov/pubmed/35746085 http://dx.doi.org/10.3390/polym14122509

Ejemplares similares

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

Use of Artificial Intelligence for Predicting Parameters of Sustainable Concrete and Raw Ingredient Effects and Interactions
por: Amin, Muhammad Nasir, et al.
Publicado: (2022)

A Scientometric-Analysis-Based Review of the Research Development on Geopolymers
por: Khan, Kaffayatullah, et al.
Publicado: (2022)

Split Tensile Strength Prediction of Recycled Aggregate-Based Sustainable Concrete Using Artificial Intelligence Methods
por: Amin, Muhammad Nasir, et al.
Publicado: (2022)

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

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

Compressive Strength Estimation of Geopolymer Composites through Novel Computational Approaches
por: Amin, Muhammad Nasir, 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)

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)

Comparative Study of Experimental and Modeling of Fly Ash-Based Concrete
por: Khan, Kaffayatullah, et al.
Publicado: (2022)

Prediction Models for Estimating Compressive Strength of Concrete Made of Manufactured Sand Using Gene Expression Programming Model
por: Khan, Kaffayatullah, et al.
Publicado: (2022)

Nano-Silica-Modified Concrete: A Bibliographic Analysis and Comprehensive Review of Material Properties
por: Khan, Kaffayatullah, et al.
Publicado: (2022)

Predictive Modeling of Compression Strength of Waste PET/SCM Blended Cementitious Grout Using Gene Expression Programming
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)

Evaluating the Strength and Impact of Raw Ingredients of Cement Mortar Incorporating Waste Glass Powder Using Machine Learning and SHapley Additive ExPlanations (SHAP) Methods
por: Alkadhim, Hassan Ali, et al.
Publicado: (2022)

Modelling Compression Strength of Waste PET and SCM Blended Cementitious Grout Using Hybrid of LSSVM Models
por: Khan, Kaffayatullah, et al.
Publicado: (2022)

Compressive Strength Estimation of Fly Ash/Slag Based Green Concrete by Deploying Artificial Intelligence Models
por: Khan, Kaffayatullah, et al.
Publicado: (2022)

A Comprehensive Review of Types, Properties, Treatment Methods and Application of Plant Fibers in Construction and Building Materials
por: Amin, Muhammad Nasir, et al.
Publicado: (2022)

Steel Fiber-Reinforced Concrete: A Systematic Review of the Research Progress and Knowledge Mapping
por: Amin, Muhammad Nasir, et al.
Publicado: (2022)

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

Prediction of Geopolymer Concrete Compressive Strength Using Novel Machine Learning Algorithms
por: Ahmad, Ayaz, et al.
Publicado: (2021)

Knowledge Mapping of the Literature on Fiber-Reinforced Geopolymers: A Scientometric Review
por: Alkadhim, Hassan Ali, et al.
Publicado: (2022)

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

Evaluating the compressive strength of glass powder-based cement mortar subjected to the acidic environment using testing and modeling approaches
por: Alfaiad, Majdi Ameen, et al.
Publicado: (2023)

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

Effect of Fineness of Basaltic Volcanic Ash on Pozzolanic Reactivity, ASR Expansion and Drying Shrinkage of Blended Cement Mortars
por: Khan, Kaffayatullah, et al.
Publicado: (2019)

Data-Driven Techniques for Evaluating the Mechanical Strength and Raw Material Effects of Steel Fiber-Reinforced Concrete
por: Al-Hashem, Mohammed Najeeb, et al.
Publicado: (2022)

New SHapley Additive ExPlanations (SHAP) Approach to Evaluate the Raw Materials Interactions of Steel-Fiber-Reinforced Concrete
por: Anjum, Madiha, et al.
Publicado: (2022)

Predicting the Rheological Properties of Super-Plasticized Concrete Using Modeling Techniques
por: Amin, Muhammad Nasir, et al.
Publicado: (2022)

Experimental and machine learning approaches to investigate the effect of waste glass powder on the flexural strength of cement mortar
por: Amin, Muhammad Nasir, et al.
Publicado: (2023)

In-Depth Analysis of Cement-Based Material Incorporating Metakaolin Using Individual and Ensemble Machine Learning Approaches
por: Bulbul, Abdulrahman Mohamad Radwan, et al.
Publicado: (2022)

Strength Parameters of Foamed Geopolymer Reinforced with GFRP Mesh
por: Krzywoń, Rafał, et al.
Publicado: (2021)

Estimating Flexural Strength of FRP Reinforced Beam Using Artificial Neural Network and Random Forest Prediction Models
por: Khan, Kaffayatullah, et al.
Publicado: (2022)

Mapping Research Knowledge on Rice Husk Ash Application in Concrete: A Scientometric Review
por: Amin, Muhammad Nasir, et al.
Publicado: (2022)

Prediction of Rapid Chloride Penetration Resistance to Assess the Influence of Affecting Variables on Metakaolin-Based Concrete Using Gene Expression Programming
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)

Evaluating the effectiveness of waste glass powder for the compressive strength improvement of cement mortar using experimental and machine learning methods
por: Khan, Kaffayatullah, et al.
Publicado: (2023)

Mechanical Performance of High-Strength Sustainable Concrete under Fire Incorporating Locally Available Volcanic Ash in Central Harrat Rahat, Saudi Arabia
por: Amin, Muhammad Nasir, et al.
Publicado: (2020)

Use of Artificial Intelligence Methods for Predicting the Strength of Recycled Aggregate Concrete and the Influence of Raw Ingredients
por: Pan, Xinchen, 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)

Cannot write session to /tmp/vufind_sessions/sess_6gsu86qoch7tavs2r8au010uv6