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https://ir.lib.ncu.edu.tw/handle/987654321/106838
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| 題名: | Machine learning in concrete strength simulations: Multi-nation data analytics |
| 作者: | 蔡志豐;Chou, Jui-Sheng;Tsai, Chih-Fong;Pham, Anh-Duc;Lu, Yu-Hsin |
| 貢獻者: | 管理學院資訊管理學系 |
| 關鍵詞: | Analysis;Compressive strength;Concrete;Ensemble classifiers;High performance concrete;Machine learning;Mechanical properties;Multi-nation data analysis;Neural networks;Prediction |
| 日期: | 2014-12-30 |
| 上傳時間: | 2026-04-23 13:46:17 (UTC+8) |
| 出版者: | Elsevier Ltd.;Elsevier Ltd |
| 摘要: | 摘要： •This comprehensive study used advanced machine learning techniques to predict concrete compressive strength.•Model performance is evaluated through multi-nation data simulation experiments.•The prediction accuracy of ensemble technique is superior to that of single learning models.•This study developed advanced learning approaches for solving civil engineering problems.•The approach also has potential applications in material sciences. Machine learning (ML) techniques are increasingly used to simulate the behavior of concrete materials and have become an important research area. The compressive strength of high performance concrete (HPC) is a major civil engineering problem. However, the validity of reported relationships between concrete ingredients and mechanical strength is questionable. This paper provides a comprehensive study using advanced ML techniques to predict the compressive strength of HPC. Specifically, individual and ensemble learning classifiers are constructed from four different base learners, including multilayer perceptron (MLP) neural network, support vector machine (SVM), classification and regression tree (CART), and linear regression (LR). For ensemble models that integrate multiple classifiers, the voting, bagging, and stacking combination methods are considered. The behavior simulation capabilities of these techniques are investigated using concrete data from several countries. The comparison results show that ensemble learning techniques are better than learning techniques used individually to predict HPC compressive strength. Although the two single best learning models are SVM and MLP, the stacking-based ensemble model composed of MLP/CART, SVM, and LR in the first level and SVM in the second level often achieves the best performance measures. This study validates the applicability of ML, voting, bagging, and stacking techniques for simple and efficient simulations of concrete compressive strength.
出版者： Elsevier Ltd
出版日期： 2014-12-30
出處： Construction & building materials, 2014-12, Vol.73, p.771-780
版權： 2014 Elsevier Ltd
版權： COPYRIGHT 2014 Elsevier B.V.
識別號： ISSN: 0950-0618
識別號： DOI: 10.1016/j.conbuildmat.2014.09.054 |
| 顯示於類別: | [資訊管理學系] 期刊論文
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