MAVE (version 1.3.12)

Concrete: Concrete Compressive Strength Data Set

Description

Concrete strength is very important in civil engineering and is a highly nonlinear function of age and ingredients. This dataset contains 1030 instances and there are 8 features relevant to concrete strength. The description of the varaibles are given below. The description is from https://archive.ics.uci.edu/ml/datasets/Concrete+Compressive+Strength. Name -- Data Type -- Measurement -- Description

Format

A data frame with 1030 rows and 8 covariate variables and 1 response variable

Details

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

References

-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).

Examples

data(Concrete)
train = sample(1:1030)[1:500]
x.train = as.matrix(Concrete[train,1:8])
y.train = as.matrix(Concrete[train,9])
x.test  = as.matrix(Concrete[-train,1:8])
y.test  = as.matrix(Concrete[-train,9])

dr = mave.compute(x.train,y.train, method='meanopg',max.dim=8)
dr.dim = mave.dim(dr)
y.pred = predict(dr.dim,x.test)
#estimation error
mean((y.pred-y.test)^2)