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Concrete Slump Test Data Set
Download: Data Folder, Data Set Description

Abstract: Concrete is a highly complex material. The slump flow of concrete is not only determined by the water content, but that is also influenced by other concrete ingredients.

Data Set Characteristics:  

Multivariate

Number of Instances:

103

Area:

Computer

Attribute Characteristics:

Real

Number of Attributes:

10

Date Donated

2009-04-30

Associated Tasks:

Regression

Missing Values?

N/A

Number of Web Hits:

112320


Source:

Donor: I-Cheng Yeh
Email: icyeh '@' chu.edu.tw
Institution: Department of Information Management, Chung-Hua University (Republic of China)
Other contact information: Department of Information Management, Chung-Hua University, Hsin Chu, Taiwan 30067, R.O.C.


Data Set Information:

The data set includes 103 data points. There are 7 input variables, and 3 output variables in the data set.
The initial data set included 78 data. After several years, we got 25 new data points.


Attribute Information:

Input variables (7)(component kg in one M^3 concrete):
Cement
Slag
Fly ash
Water
SP
Coarse Aggr.
Fine Aggr.

Output variables (3):
SLUMP (cm)
FLOW (cm)
28-day Compressive Strength (Mpa)


Relevant Papers:

1. Yeh, I-Cheng, "Modeling slump of concrete with fly ash and superplasticizer," Computers and Concrete, Vol.5, No.6, 559-572, 2008.

2. Yeh, I-Cheng, "Simulation of concrete slump using neural networks," Construction Materials,Vol.162, No.1, 11-18, 2009.

3. Yeh, I-Cheng, "Prediction of workability of concrete using design of experiments for mixtures," COMPUTERS AND CONCRETE, Vol.5, No.1, 1-20, 2008.

4. Yeh, I-Cheng, "Modeling slump flow of concrete using second-order regressions and artificial neural networks," Cement and Concrete Composites, Vol.29, No. 6, 474-480, 2007.

5. Yeh, I-Cheng, "Exploring concrete slump model using artificial neural networks," J. of Computing in Civil Engineering, ASCE, Vol.20, No.3, 217-221, 2006.



Citation Request:

Yeh, I-Cheng, "Modeling slump flow of concrete using second-order regressions and artificial neural networks," Cement and Concrete Composites, Vol.29, No. 6, 474-480, 2007.


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