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Prediction of concrete strength using MCMC and GPR methods
- 1 Tsinghua University
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Abstract
Concrete strength prediction is a complex nonlinear regression task that involves multiple ingredients and age as key factors. In order to achieve accurate predictions, the Markov Chain Monte Carlo (MCMC) and Gaussian Process Regression (GPR) techniques are employed. The dataset, sourced from Kaggle repositories, comprises a comprehensive collection of 1030 data points. Alongside the existing features (content of ingredients, age and strength), we introduce new ones, including water-cement ratio, sand ratio, and water-binder ratio, to enhance the model's credibility. To determine the optimal kernel function, the dataset is partitioned into training and testing subsets. Notably, the MCMC method yields an R2 of 0.41, while GPR demonstrates a significantly improved R2 of 0.89. Further investigation is warranted to refine the model's fit and optimize its predictive capacity.
Keywords
Concrete Strength, Prediction, Markov Chain Monte Carlo (MCMC), Gaussian Process Regression (GPR)
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Cite this article
Zhang,D. (2023). Prediction of concrete strength using MCMC and GPR methods. Theoretical and Natural Science,9,54-61.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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Volume title: Proceedings of the 3rd International Conference on Computing Innovation and Applied Physics
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