TY - JOUR
T1 - Machine learning in concrete strength simulations
T2 - Multi-nation data analytics
AU - Chou, Jui Sheng
AU - Tsai, Chih Fong
AU - Pham, Anh Duc
AU - Lu, Yu Hsin
N1 - Publisher Copyright:
© 2014 Elsevier Ltd. All rights reserved.
PY - 2014/12/30
Y1 - 2014/12/30
N2 - 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.
AB - 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.
KW - Compressive strength
KW - Ensemble classifiers
KW - High performance concrete
KW - Machine learning
KW - Multi-nation data analysis
KW - Prediction
UR - http://www.scopus.com/inward/record.url?scp=84911423106&partnerID=8YFLogxK
U2 - 10.1016/j.conbuildmat.2014.09.054
DO - 10.1016/j.conbuildmat.2014.09.054
M3 - 期刊論文
AN - SCOPUS:84911423106
SN - 0950-0618
VL - 73
SP - 771
EP - 780
JO - Construction and Building Materials
JF - Construction and Building Materials
ER -