Estimating Concrete Workability Based on Slump Test with Least Squares Support Vector Regression. (6th December 2016)

• Record Type: Journal Article
• Authors: Hoang, Nhat-Duc; Pham, Anh-Duc
• Other Names: Hossain Khandaker Academic Editor.
• Is Part Of: Journal of construction engineering. Volume 2016(2016)
• Publication Date: 2016-12-06
• Subjects: Construction projects -- Periodicals; Civil engineering -- Periodicals; Engineering -- Periodicals; Construction industry -- Periodicals; Civil engineering; Construction industry; Construction projects; Engineering; Electronic journals; Periodicals; 624
• DOI: 10.1155/2016/5089683
• Languages: English
• ISSNs: 2356-7295

Abstract

Abstract : 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.