Jul 2026· Asian Journal of Civil Engineering· Vol 27, pp. 4993 - 5011· 1 citation· 61 references
TL;DR
A robust machine learning framework integrating NDT methods rebound hammer (RH) and ultrasonic pulse velocity (UPV) with mix design parameters and curing age precise early strength prediction is developed, demonstrating promising strength prediction capability as early as 3 days under laboratory conditions.
Investigation of machine learning models for predicting the compressive strength of concrete using a publicly available experimental dataset reveals that curing age and cement content are the most influential parameters affecting compressive strength, followed by water content, which is consistent with established conc...
S. Rouabah· ITEGAM- Journal of Engineeri...· 0 citations
Compressive strength is the single most important design parameter governing the safety, serviceability, and economy of concrete structures, yet its determination through standard 7-, 14-, or 28-day destructive cylinder/cube testing is slow, costly, and unable to assess concrete already cast in place. This study develo...
M. Selvakumar, S. Geetha, P. K. Kumar et al.· International journal of com...· 0 citations
Ultra-high-performance concrete (UHPC) exhibits exceptional mechanical properties and durability. However, its compressive strength is highly dependent on complex mix design parameters. While traditional experimental techniques and regression-based models are commonly used to evaluate UHPC compressive strength, machine...
N. T. Nguyen, T. Nguyen, Tuan-Khoi Nguyen et al.· PLoS ONE· 0 citations
This study aims to evaluate the mechanical and durability performance of sustainable concrete incorporating Marble Fine Powder (MFP), Marble Coarse Powder (MCP) and Recycled Concrete Aggregate (RCA), and to develop machine learning (ML) models for accurate prediction of concrete properties.
A total of 250 ex...
Ravikant, P. Aggarwal, Mahesh Pal· Multidiscipline Modeling in...· 0 citations
This study provides a robust, interpretable, and generalizable ML framework for optimizing nano-silica concrete mix design and highlights the strong potential of ML, particularly ensemble models combined with explainable AI techniques, to improve prediction reliability, reduce trial-and-error experimentation, and suppo...
Yousif J. Bas, Jamal I. Kakrasul, Kamaran S. Ismail et al.· Engineering Research Express· 0 citations
Accurate prediction of concrete compressive strength is essential for mixture design, quality control, and the broader use of supplementary cementitious materials in low-carbon construction. Fly ash concrete is particularly challenging to model because its strength development is affected by nonlinear interactions amon...
