The actual concrete compressive strength (MPa) for a given mixture under a specific age (days) was determined from laboratory assays. Data are in raw form (not scaled).
data("concrete")A data frame with 1030 observations on the following 9 variables.
X1kg of cement in a m\(^3\) mixture.
X2kg of blast furnace slag in a m\(^3\) mixture.
X3kg of fly ash in a m\(^3\) mixture.
X4kg of water in a m\(^3\) mixture.
X5kg of superplasticizer in a m\(^3\) mixture.
X6kg of coarse aggregate in a m\(^3\) mixture.
X7kg of fine aggregate in a m\(^3\) mixture.
X8Age: day(1-365), a numeric vector
YConcrete compressive strength in MPa, a numeric vector
The order of variables corresponds to the order in the original data.
Past Usage:
Primary
I-Cheng, Y. (1998) Modeling of strength of high performance concrete using artificial neural networks. Cement and Concrete Research, 28(12): 1797-1808 .
Others
I-Cheng. Y. (1998) Modeling concrete strength with augment-neuron networks. J. of Materials in Civil Engineering, ASCE 10(4): 263-268.
I-Cheng, Y. (1999) Design of high performance concrete mixture using neural networks. J. of Computing in Civil Engineering, ASCE 13 (1): 36-42.
I-Cheng, Y. (2003) Prediction of Strength of Fly Ash and Slag Concrete By The Use of Artificial Neural Networks. Journal of the Chinese Institute of Civil and Hydraulic Engineering Vol. 15, No. 4, pp. 659-663 (2003).
I-Cheng, Y. (2003) A mix Proportioning Methodology for Fly Ash and Slag Concrete Using artificial neural networks. Chung Hua Journal of Science and Engineering 1(1): 77-84.
I-Cheng, Y. (2006). Analysis of strength of concrete using design of experiments and neural networks. Journal of Materials in Civil Engineering, ASCE 18(4): 597-604.
Acknowledgements, Copyright Information, and Availability:
NOTE: Reuse of this database is unlimited with retention of copyright notice for Prof. I-Cheng Yeh.