Home
Giới thiệu
Tài khoản
Đăng nhập
Quên mật khẩu
Đổi mật khẩu
Đăng ký tạo tài khoản
Liệt kê
Công trình khoa học
Bài báo trong nước
Bài báo quốc tế
Sách và giáo trình
Thống kê
Công trình khoa học
Bài báo khoa học
Sách và giáo trình
Giáo sư
Phó giáo sư
Tiến sĩ
Thạc sĩ
Lĩnh vực nghiên cứu
Tìm kiếm
Cá nhân
Nội dung
Góp ý
Hiệu chỉnh lý lịch
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: 107,045,439
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);
S
ố:
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) 0236 3822 041 ; Email: dhdn@ac.udn.vn