Investigation of the Gaussian Process with Various Kernel Functions for the Prediction of the Compressive Strength of Concrete
Received: 28 September 2024 | Revised: 15 November 2024 | Accepted: 27 November 2024 | Online: 23 December 2024
Corresponding author: Binh Thai Pham
Abstract
The Compressive Strength of Concrete (CSC) is a critical parameter for evaluating the quality of concrete used in various construction projects, including buildings, bridges, and roads. The primary objective of this study is to examine the efficacy of a Gaussian Process (GP) Machine Learning (ML) model employing two kernel functions: Radial Basis Function (RBF) and Polynomial (POL), for predicting the CSC, considering readily quantifiable parameters. Based on these kernel functions, two models were created for this prediction, GP-RBF and GP-POL. The modeling process employed a total of 369 concrete sample data, including compressive strength values and eleven other physico-mechanical properties, collected from the Cua Luc bridge project in Vietnam. This dataset was partitioned into a training set (70%) and a testing set (30%) for model training and validation. Various validation metrics, including R2, Root Mean Square Error (RMSE), and Mean Absolute Error (MAE), were used to evaluate and compare the models. The findings of this study demonstrated that both models GP-RBF and GP-POL exhibited strong performance in predicting CSC, with GP-POL demonstrating marginal superiority over GP-RBF. Consequently, it can be concluded that POL is more efficacious than RBF in training the GP model for CSC prediction.
Keywords:
machine learning, concrete, Gaussian process, compressive strength, kernel functionDownloads
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Copyright (c) 2024 Hoang Ha, Hieu Vu Trong, Trang Le Huyen, Dam Duc Nguyen, Indra Prakash, Binh Thai Pham
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