Machine Learning Models for Concrete Strength Predictions Based on Rebound Hammer Measurements
Received: 5 September 2025 | Revised: 10 October 2025 | Accepted: 18 October 2025 | Online: 8 December 2025
Corresponding author: Ali Al-Ataby
Abstract
This paper presents an improved machine learning approach to predict the compressive strength of concrete from nondestructive Rebound Hammer (RH) measurements and water-to-cement (W/C) ratio. Several regression models, including Linear Regression (LR), Decision Tree (DT), Support Vector Regression (SVR), Random Forest (RF), and Gradient Boosting (GB) were initially applied and experimented on a comprehensive experimental dataset. GB achieved good baseline accuracy with R² = 0.86 and MAE = 6.34 MPa. To improve performance, hyperparameter tuning using grid search was adopted, and the optimized GB model improved accuracy with R² = 0.8704 and MAE = 5.90 MPa. Other models such as XGBoost, deep neural networks, and an ensemble averaging model were then explored. Among these, XGBoost had the best overall performance of R² = 0.8735 and MAE = 5.85 MPa, with the tuned GB coming close. A second-order polynomial regression model was further derived from the XGBoost predictions to provide a reference equation. This polynomial equation presents a simple and comprehensible method for field engineers to estimate compressive strength from RH and W/C values alone, without the need for computers. To support practical deployment, a user-friendly application was developed using Streamlit, which enabled users to estimate concrete strength in a real-time interface. This app uses the XGBoost model and allows for fast, portable, and accurate predictions in the field. Overall, this work demonstrates the value of combining domain knowledge with modern data-driven techniques to improve the accuracy, interpretability, and usability of nondestructive testing in concrete evaluation. The proposed models and tools offer practical benefits for real-time, reliable estimation that bridge the gap between conventional field tests and intelligent predictive analytics.
Keywords:
machine learning, concrete, rebound hammer, compressive strength, nondestructive testing, regression models, artificial intelligenceDownloads
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Copyright (c) 2025 Fayez Moutassem, Ali Al-Ataby
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