Cargando…
Compressive Strength Estimation of Geopolymer Composites through Novel Computational Approaches
The application of artificial intelligence approaches like machine learning (ML) to forecast material properties is an effective strategy to reduce multiple trials during experimentation. This study performed ML modeling on 481 mixes of geopolymer concrete with nine input variables, including curing...
Autores principales: | Amin, Muhammad Nasir, Khan, Kaffayatullah, Ahmad, Waqas, Javed, Muhammad Faisal, Qureshi, Hisham Jahangir, Saleem, Muhammad Umair, Qadir, Muhammad Ghulam, Faraz, Muhammad Iftikhar |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9147713/ https://www.ncbi.nlm.nih.gov/pubmed/35632011 http://dx.doi.org/10.3390/polym14102128 |
Ejemplares similares
-
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) -
Forecasting Compressive Strength of RHA Based Concrete Using Multi-Expression Programming
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) -
Assessment of Artificial Intelligence Strategies to Estimate the Strength of Geopolymer Composites and Influence of Input Parameters
por: Khan, Kaffayatullah, 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)