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.
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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: |
137263 |
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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