Estimating the Concrete Ultimate Strength Using a Hybridized Neural Machine Learning

• Published in: Buildings (Basel) Vol. 13; no. 7; p. 1852
• Main Author: Zhang, Ziwei
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.07.2023
• Subjects: Accuracy; Algorithms; Analysis; Artificial neural networks; building material; Compressive strength; Concrete; Construction materials; Correlation coefficient; Correlation coefficients; Datasets; Error analysis; Estimation; Fire resistance; Food; Foraging behavior; Gas solubility; Heuristic methods; Laboratory tests; Learning algorithms; Machine learning; Mathematical models; Mold; neural network; Neural networks; Optimization; Optimization algorithms; Retrofitting; Root-mean-square errors; slime mold algorithm; Slime molds; Support vector machines; Ultimate tensile strength

Concrete is a highly regarded construction material due to many advantages such as versatility, durability, fire resistance, and strength. Hence, having a prediction of the compressive strength of concrete (CSC) can be highly beneficial. The new generation of machine learning models has provided cap...