Dataset on predictive compressive strength model for self-compacting concrete

The determination of compressive strength is affected by many variables such as the water cement (WC) ratio, the superplasticizer (SP), the aggregate combination, and the binder combination. In this dataset article, 7, 28, and 90-day compressive strength models are derived using statistical analysis. The response surface methodology is used toinvestigate the effect of the parameters: Varying percentages of ash, cement, WC, and SP on hardened properties-compressive strengthat 7,28 and 90 days. Thelevels of independent parameters are determinedbased on preliminary experiments. The experimental values for compressive strengthat 7, 28 and 90 days and modulus of elasticity underdifferent treatment conditions are also discussed and presented.These dataset can effectively be used for modelling and prediction in concrete production settings.


a b s t r a c t
The determination of compressive strength is affected by many variables such as the water cement (WC) ratio, the superplasticizer (SP), the aggregate combination, and the binder combination. In this dataset article, 7, 28, and 90-day compressive strength models are derived using statistical analysis. The response surface methodology is used toinvestigate the effect of the parameters: Varying percentages of ash, cement, WC, and SP on hardened propertiescompressive strengthat 7,28 and 90 days. Thelevels of independent parameters are determinedbased on preliminary experiments. The experimental values for compressive strengthat 7, 28 and 90 days and modulus of elasticity underdifferent treatment conditions are also discussed and presented.These dataset can effectively be used for modelling and prediction in concrete production settings.
& 2018 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

Subject area
Civil Engineering More specific subject area Production of concrete and strength properties Type of data

Value of the data
The present data can be used to predict the strength of auto-compacting concrete at varying days. The dataset can be used to determine the trend of strength associate with concrete. The dataset can be used to detect the effect of SP. The dataset can be used to determine the nature of concrete, and the corresponding degree of hydration.
The dataset can serve as an experimental framework for the analysis of other basic properties of concrete.
The dataset can help in developing experimental programme for the evaluation of model accuracy and precision.

Data, and experimental design
Strength data presented here are from seventy-two (72) different POFA concrete samples fabricated to compare with normal concrete without ash. We make reference to [1][2][3][4][5][6][7][8] for related views such as forecasting and prediction. In this dataset article, a 7-day, 28-day, and 90-day compressive strength models were derived by statistical analysis and the proposed models results and description as contained in Tables 1-3, and Figs. 1-5 are as follows.

Quadratic equation generated from the model
Besides the statistical software used in the data analysis, is a predictive quadratic model defined as follows: where x and C i ; i Z0 denote varying percentages of POFA and compressive strength respectively.

Data analysis
For x the varying percentages of POFA with zero (0) as the control, andy the average compressive strength, we present in Tables 1-6 the relationship between xandy at varying intervals in days.
It is noted from Tables 1-6 that there was an increase in strength as the percentage of POFA increased but the control was slightly high. Table 7 shows the experimental and numerical results for POFA with regards to Compressive Strength for 7, 28, and 90 days.

Models correlationpredicted and measured
Matlab statistical software was used to analyse and investigate the effect of the parameters (cement, water cement (WC) ratio, POFA and superplasticiser (SP) on the hardened properties (compressive strength at 7, 28 and 90 days. Determination of the independent parameters with respect to their percentage replacement was made on initial experiments as shown in Tables 1-6