Thông tin chung

  English

  Đề tài NC khoa học
  Bài báo, báo cáo khoa học
  Hướng dẫn Sau đại học
  Sách và giáo trình
  Các học phần và môn giảng dạy
  Giải thưởng khoa học, Phát minh, sáng chế
  Khen thưởng
  Thông tin khác

  Tài liệu tham khảo

  Hiệu chỉnh

 
Số người truy cập: 31,026,829

 Machine learning in concrete strength simulations: Multi-nation data analytics
Tác giả hoặc Nhóm tác giả: Jui-Sheng Chou, Chih-Fong Tsai, Anh-Duc Pham, Yu-Hsin Lu
Nơi đăng: Construction and Building Materials (SCIE); Số: 73;Từ->đến trang: 771–780;Năm: 2014
Lĩnh vực: Kỹ thuật; Loại: Bài báo khoa học; Thể loại: Quốc tế
TÓM TẮT
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.
ABSTRACT
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.
© Đại học Đà Nẵng
 
 
Địa chỉ: 41 Lê Duẩn Thành phố Đà Nẵng
Điện thoại: (84) 0511 3822 041 ; Email: dhdn@ac.udn.vn