Journal of Construction Engineering

Volume 2016, Article ID 5089683, 8 pages

http://dx.doi.org/10.1155/2016/5089683

## Estimating Concrete Workability Based on Slump Test with Least Squares Support Vector Regression

^{1}Institute of Research and Development, Faculty of Civil Engineering, Duy Tan University, P809-K7/25 Quang Trung, Danang 550000, Vietnam^{2}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.

#### 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.

#### 1. Introduction

Concrete workability is defined as the effort required to manipulate a freshly mixed quantity of concrete with minimum loss of homogeneity [1]. This property of concrete is generally known to affect the consistency, flowability, pumpability, compactibility, and harshness of a concrete mix. Thus, concrete workability is a very crucial factor that must be considered in order to produce high quality concrete [2–4].

The slump test is the most common method for assessing the flow properties of fresh concrete; the slump provides a measure of workability [5]. Using this test, the slump can be derived by measuring the drop from the top of the slumped fresh concrete. In the task of concrete mixture design, the prediction of concrete flowability is critical for on-site construction. As the complexity of concrete construction escalates, there is an increasing pressure on material engineers to achieve high workability as well as to maintain the necessary mechanical properties to meet design specifications.

Concrete has been increasingly utilized in high-rise building and infrastructure development projects and special ingredients are often employed to make the material satisfy a specific set of performance requirements [6]. Superplasticizers are often included to enhance the concrete workability [7–9]. This situation makes the concrete mixes to be highly complex materials and modeling their properties becomes a very challenging task. There are complex and nonlinear relationships between the characteristics and the components that constitute the concrete mixes [8, 10, 11].

Due to the importance of the research topic, various studies have been dedicated to concrete slump prediction. Traditional statistical models and machine learning are prevailing approaches to tackle the problem at hand. Öztaş et al. [2], Yeh [1, 3], Chine et al. [12], and Bilgil [13] employed the regression analysis and Artificial Neural Network (ANN) models to estimate concrete slump; the common finding is that ANN is an effective nonlinear modeling method and its results are more accurate than the models based on the traditional regression analysis approach.

Baykasoğlu et al. [14] utilized the gene expression programming (GEP) to model high-strength concrete slump. Chen et al. [15] constructed a parallel hypercubic GEP to forecast the slump of high-performance concrete; this research showed that the improved method is better than the GEP and similar to the performance of ANN. Chandwani et al. [16] proposed a Genetic Algorithm assisted ANN; the study showed that the integrated approach can enhance the convergence speed of ANN and its prediction accuracy.

Due to the popularity of concrete in the construction industry, better alternatives for concrete slump prediction are of practical need for construction engineers in concrete mix design. This research contributes to the body of knowledge by proposing a new approach for improving the accuracy of concrete slump prediction which is based on the Least Squares Support Vector Regression (LS-SVR). LS-SVR is an advanced machine learning method which is designed for nonlinear modeling [17]; the superiority of the approach has been illustrated in recent applications [18–22].

Furthermore, a dataset that contains slump test records, collected from a hydroelectric dam construction project in central Vietnam, is used to establish and verify the proposed approach. The rest of the article is organized as follows: the second section presents the research method. The proposed slump prediction model is described in the third section. The next section reports the experimental results. The conclusion of this study is stated in the final section.

#### 2. Research Method

##### 2.1. The Concrete Slump Test Dataset

This research recorded testing results of 95 concrete mixes during the construction progress of the Song Bung 2 hydroelectric dam construction project in central Vietnam (http://www.sb2.vn/). The test is in conformity with the Vietnamese standard (TCVN-3106) for slump test which is equivalent to the ASTM-C-143. The equipment for the slump test includes a hollow frustum of a cone and a ruler as the measuring device (see Figure 1). The height of the cone is 30 cm. The diameter of the top and bottom of the cone is 10 cm and 20 cm, respectively. The cone is filled with fresh concrete and then lifted vertically. The height difference between the concrete and the cone is the slump value.