Dataset obtained from DoE for the construction of lightweight concrete blocks using expanded polystyrene

The dataset used in this study was generated through a Design of Experiments (DOE) approach to predict the material mixture proportions using machine learning techniques. It contains 36 experiments, where the independent variables include the proportions of cement, sand, gravel, and expanded polystyrene (Styrofoam). The dependent variables are the compressive strength values and water absorption rates, calculated across multiple components and used to classify the mixtures.

The dataset columns include:

Cement: The proportion of cement used in the mixture.

Sand: The proportion of sand used.

Gravel: The proportion of gravel.

Styrofoam (Sty): The proportion of expanded polystyrene.

Comp. 1-6: Compressive strength values for different components.

Comp. Mean: The mean compressive strength value.

Abs. 1-3: Water absorption values for different components.

Abs. Mean: The mean water absorption rate.

Classification: The final classification of the mixture based on the obtained results, indicating if the mixture belongs to categories such as "B," "C," "D," or "No classification."

This dataset was used to train and validate machine learning models, aiming to predict the mixture properties based on the material proportions.