Please use this identifier to cite or link to this item: https://scholarhub.balamand.edu.lb/handle/uob/6416
Title: Machine learning prediction of concrete compressive strength using rebound hammer test
Authors: El-Mir, Abdulkader 
El-Zahab, Samer
Sbartaï, Zoubir Mehdi
Homsi, Farah
Saliba, Jacqueline
El-Hassan, Hilal
Affiliations: Department of Civil and Environmental Engineering 
Keywords: Compressive strength
Concrete mix
Machine-learning
Non-destructive test
Rebound hammer
Issue Date: 2023
Publisher: Elsevier
Part of: Journal of Building Engineering
Volume: 64
Abstract: 
Machine learning has become a key branch in artificial intelligence by providing unique predictive modeling solutions. Predicting the compressive strength of concrete determined using non-destructive test techniques (NDT) includes high levels of uncertainty. This uncertainty directly depends on the repeatability of the measurement and the variability of concrete properties. This study aims to evaluate the effect of mixture composition and age of concrete on the coefficient of variation (CV) of the rebound hammer index applied to various types of concrete. Several supervised machine learning models, including multivariate multiple regression (MMR), support vector machine (SVM), Gaussian process regression (GPR), and Regression tree (RT) were utilized to predict the compressive strength of concrete. A large dataset of 468 cubic concrete specimens was sorted into four categories and employed for simulation. Regardless of the selected dataset, it was concluded that GPR/SVM and RT yielded the most accurate model prediction metrics of compressive strength when using rebound hammer records over MMR model. The results of the adopted models were remarkably better when mixture proportion and age of concrete features (i.e., age and w/p) were considered in the simulation.
URI: https://scholarhub.balamand.edu.lb/handle/uob/6416
DOI: 10.1016/j.jobe.2022.105538
Ezproxy URL: Link to full text
Type: Journal Article
Appears in Collections:Department of Civil and Environmental Engineering

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