Concrete
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knitr::opts_chunk$set(echo = TRUE)

Description

Data about concrete mixtures and resulting properties (Slump, Flow and Strength). Input variables are:

Water content : ACI 211.1 (Standard practice for selecting proportions for normal, heavyweight, and mass concrete) assumes that, for a given maximum size of coarse aggregate, the slump or consistency of concrete is a direct function of the water content; i.e., within limits it is independent of other factors such as aggregate grading and cement content.

Superplasticizer : When the water content of a concrete mixture is held constant, the addition of a superplasticizer will increase the consistency.

Pozzolanic admixtures : Pozzolanic admixtures tend to improve the cohesiveness of concrete. Fly ash, when used as a partial replacement for fine aggregate, generally increases the consistency at a given water content.

Cement content : Concretes containing a very high proportion of cement show excellent cohesiveness, but tend to be sticky. At a given water content, a considerable lowering of the cement content tends to produce harsh mixtures, with poor cohesiveness.

Aggregate characteristics : Very fine sands require more water for a given consistency; alternatively, they will produce harsh and unworkable mixtures at a water content that might have been adequate with coarser sands. Also, the particle size of coarse aggregate influences the water requirement for a given consistency.

Usage

library("rio")
#concrete <- import("https://shinyapps.hu-berlin.de/u/stat/data/concrete.sav")
#head(concrete)

Format

The data set contains data of 237 children between 11 and 20 years.

Variable | Meaning -------------|----------------------------------------------------------------------------------------- Cement | Cement (in kg/m$^3$ concrete) Slag | Slag (in kg/m$^3$ concrete) Flyash | Fly ash (in kg/m$^3$ concrete) Water | Water (in kg/m$^3$ concrete) SP | Superplasticizer (in kg/m$^3$ concrete) CoarseAggr | Coarse sand (in kg/m$^3$ concrete) FineAggr | Fine sand (in kg/m$^3$ concrete) Slump | Slump (in cm) Flow | Flow (in cm) Strength | 28day compressive strength (in Megapascal)

Source

Concrete Slump Test Data Set from the UCI Machine Learning Repository

References

Yeh, I-Cheng (2007) Modeling slump flow of concrete using second-order regressions and artificial neural networks, Cement and Concrete Composites, Vol.29, No. 6, 474-480. https://doi.org/10.1016/j.cemconcomp.2007.02.001