A Study on the Application of Machine Learning for Predicting Concrete Compressive Strength
1. School of Information Engineering, China Jiliang University, Hangzhou 310018, China
2. SINOMA Bonyear (Hangzhou) Intelligent Technology Co. Ltd., Hangzhou 310000, China
| Abstract: | The compressive strength of concrete is one of the main factors affecting the quality of construction, and it is also a key parameter for concrete design, so it is necessary to predict it. However, most of the early empirical formulas for predicting the compressive strength of concrete are for specific concretes, which can not be universally promoted, while other prediction methods such as physical testing method, finite element analysis method, etc. are cumbersome in terms of steps, and can not be formed into a generalized prediction model under the harsh conditions of applicability. In recent years, the concrete compressive strength prediction model based on machine learning has become a hot research topic at home and abroad. In this paper, firstly, by comparing the application of various machine learning methods, including regression model, support vector regression machine model, tree model, neural network and integrated learning in concrete compressive strength, it is concluded that the support vector regression machine model and integrated learning have higher effectiveness in the prediction of compressive strength. Finally, the article discusses the shortcomings of the widely used concrete compressive strength prediction methods and gives the future research direction of machine learning based concrete compressive strength prediction model. |
| Keywords: | Concrete Compressive Strength; Machine Learning; Ensemble Learning; Overfitting |
| DOI: | 10.57237/j.se.2023.05.003 |
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Received: 8 September 2023
Accepted: 26 October 2023
Published Online: 28 October 2023
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