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