Cargando…
Application of Machine Learning Techniques for Predicting Compressive, Splitting Tensile, and Flexural Strengths of Concrete with Metakaolin
The mechanical properties of concrete are the important parameters in a design code. The amount of laboratory trial batches and experiments required to produce useful design data can be decreased by using robust prediction models for the mechanical properties of concrete, which can save time and mon...
Autores principales: | Shah, Hammad Ahmed, Yuan, Qiang, Akmal, Usman, Shah, Sajjad Ahmad, Salmi, Abdelatif, Awad, Youssef Ahmed, Shah, Liaqat Ali, Iftikhar, Yusra, Javed, Muhammad Haris, Khan, Muhammad Imtiaz |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9369826/ https://www.ncbi.nlm.nih.gov/pubmed/35955370 http://dx.doi.org/10.3390/ma15155435 |
Ejemplares similares
-
Predicting Compressive and Splitting Tensile Strengths of Silica Fume Concrete Using M5P Model Tree Algorithm
por: Shah, Hammad Ahmed, et al.
Publicado: (2022) -
Splitting tensile strength prediction of Metakaolin concrete using machine learning techniques
por: Li, Qiang, et al.
Publicado: (2023) -
Machine Learning-Based Predictive Model for Tensile and Flexural Strength of 3D-Printed Concrete
por: Ali, Ammar, et al.
Publicado: (2023) -
Mechanical and Durability Evaluation of Metakaolin as Cement Replacement Material in Concrete
por: Al-Hashem, Mohammed Najeeb, et al.
Publicado: (2022) -
Flexural Tensile Strength of Concrete with Synthetic Fibers
por: Blazy, Julia, et al.
Publicado: (2021)