#Context **Abstract**: ------------- Concrete is the most important material in civil engineering. The concrete compressive strength is a highly nonlinear function of age and ingredients. #Content **Concrete Compressive Strength Data Set** ------------------------------------------ **Data Set Information:** ------------------------- Number of instances 1030 Number of Attributes 9 Attribute breakdown 8 quantitative input variables, and 1 quantitative output variable Missing Attribute Values None **Attribute Information:** -------------------------- Given are the variable name, variable type, the measurement unit and a brief description. The concrete compressive strength is the regression problem. The order of this listing corresponds to the order of numerals along the rows of the database. Name -- Data Type -- Measurement -- Description Cement (component 1) -- quantitative -- kg in a m3 mixture -- Input Variable Blast Furnace Slag (component 2) -- quantitative -- kg in a m3 mixture -- Input Variable Fly Ash (component 3) -- quantitative -- kg in a m3 mixture -- Input Variable Water (component 4) -- quantitative -- kg in a m3 mixture -- Input Variable Superplasticizer (component 5) -- quantitative -- kg in a m3 mixture -- Input Variable Coarse Aggregate (component 6) -- quantitative -- kg in a m3 mixture -- Input Variable Fine Aggregate (component 7) -- quantitative -- kg in a m3 mixture -- Input Variable Age -- quantitative -- Day (1~365) -- Input Variable Concrete compressive strength -- quantitative -- MPa -- Output Variable #Acknowledgements **Source:** ----------- Original Owner and Donor Prof. I-Cheng Yeh Department of Information Management Chung-Hua University, Hsin Chu, Taiwan 30067, R.O.C. e-mail:icyeh '@' chu.edu.tw TEL:886-3-5186511 Date Donated: August 3, 2007 From: https://archive.ics.uci.edu/ml/datasets/Concrete+Compressive+Strength **Relevant Papers:** -------------------- **Main** 1) I-Cheng Yeh, "Modeling of strength of high performance concrete using artificial neural networks," Cement and Concrete Research, Vol. 28, No. 12, pp. 1797-1808 (1998). **Others** 2) I-Cheng Yeh, "Modeling Concrete Strength with Augment-Neuron Networks," J. of Materials in Civil Engineering, ASCE, Vol. 10, No. 4, pp. 263-268 (1998). 3) I-Cheng Yeh, "Design of High Performance Concrete Mixture Using Neural Networks," J. of Computing in Civil Engineering, ASCE, Vol. 13, No. 1, pp. 36-42 (1999). 4) I-Cheng Yeh, "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). 5) I-Cheng Yeh, "A mix Proportioning Methodology for Fly Ash and Slag Concrete Using Artificial Neural Networks," Chung Hua Journal of Science and Engineering, Vol. 1, No. 1, pp. 77-84 (2003). 6) Yeh, I-Cheng, "Analysis of strength of concrete using design of experiments and neural networks," Journal of Materials in Civil Engineering, ASCE, Vol.18, No.4, pp.597-604 (2006). **Citation Request:** --------------------- NOTE: Reuse of this database is unlimited with retention of copyright notice for Prof. I-Cheng Yeh and the following published paper: I-Cheng Yeh, "Modeling of strength of high performance concrete using artificial neural networks," Cement and Concrete Research, Vol. 28, No. 12, pp. 1797-1808 (1998). #Inspiration Can you predict the strength of concrete?