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Journal of Construction Engineering - Volume 2016 2016, Article ID 5089683, 8 pages -

Research Article

Institute of Research and Development, Faculty of Civil Engineering, Duy Tan University, P809-K7-25 Quang Trung, Danang 550000, Vietnam

Faculty of Project Management, the University of Danang-University of Science and Technology, 54 Nguyen Luong Bang, Danang 550000, Vietnam

Received 23 August 2016; Accepted 8 November 2016

Academic Editor: Khandaker Hossain

Copyright © 2016 Nhat-Duc Hoang and Anh-Duc Pham. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Concrete workability, quantified by concrete slump, is an important property of a concrete mixture. Concrete slump is generally known to affect the consistency, flowability, pumpability, compactibility, and harshness of a concrete mix. Hence, an accurate prediction of this property is a practical need of construction engineers. This research proposes a machine learning model for predicting concrete slump based on the Least Squares Support Vector Regression LS-SVR. LS-SVR is employed to model the nonlinear mapping between the mix components and slump values. Since the learning process of the LS-SVR necessitates two hyperparameters, the regularization and the kernel parameters, the grid search method is employed search for the most desirable set of hyperparameters. Furthermore, to construct the hybrid model, this research collected a dataset including actual concrete slump tests from a hydroelectric dam construction project in Vietnam. Experimental results show that the proposed model is capable of predicting concrete slump accurately.

Autor: Nhat-Duc Hoang and Anh-Duc Pham



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