README.md

---
license: other
task_categories:
  - tabular-regression
  - tabular-classification
tags:
  - materials-science
  - chemistry
  - foundry-ml
  - scientific-data
size_categories:
  - 1K<n<10K
---

# Machine learning in concrete science: applications, challenges, and best practices

Dataset containing concrete compressive strength for 1030 materials

## Dataset Information

- **Source**: [Foundry-ML](https://github.com/MLMI2-CSSI/foundry)
- **DOI**: [10.18126/8k1f-mx77](https://doi.org/10.18126/8k1f-mx77)
- **Year**: 2022
- **Authors**: Li, Zhanzhao, Yoon, Jinyoung, Zhang, Rui, Rajabipour, Farshad, Srubar III, Wil V., Dabo, Ismaila, Radlińska, Aleksandra
- **Data Type**: tabular

### Fields

| Field | Role | Description | Units |
|-------|------|-------------|-------|
| Cement (component 1)(kg in a m^3 mixture) | input | Amount of cement | kg/m^3 |
| Blast Furnace Slag (component 2)(kg in a m^3 mixture) | input | Amount of blast furnace slag | kg/m^3 |
| Fly Ash (component 3)(kg in a m^3 mixture) | input | Amount of fly ash | kg/m^3 |
| Water  (component 4)(kg in a m^3 mixture) | input | Amount of water | kg/m^3 |
| Superplasticizer (component 5)(kg in a m^3 mixture) | input | Amount of superplasticizer | kg/m^3 |
| Coarse Aggregate  (component 6)(kg in a m^3 mixture) | input | Amount of coarse aggregate | kg/m^3 |
| Age (day) | input | Age of concrete | days |
| Concrete compressive strength(MPa, megapascals)  | target | Concrete compressive strength | MPa |


### Splits

- **train**: train


## Usage

### With Foundry-ML (recommended for materials science workflows)

```python
from foundry import Foundry

f = Foundry()
dataset = f.get_dataset("10.18126/8k1f-mx77")
X, y = dataset.get_as_dict()['train']
```

### With HuggingFace Datasets

```python
from datasets import load_dataset

dataset = load_dataset("Dataset_concrete_compressive_strength")
```

## Citation

```bibtex
@misc{https://doi.org/10.18126/8k1f-mx77
doi = {10.18126/8k1f-mx77}
url = {https://doi.org/10.18126/8k1f-mx77}
author = {Li, Zhanzhao and Yoon, Jinyoung and Zhang, Rui and Rajabipour, Farshad and Srubar III, Wil V. and Dabo, Ismaila and Radlińska, Aleksandra}
title = {Machine learning in concrete science: applications, challenges, and best practices}
keywords = {machine learning, foundry}
publisher = {Materials Data Facility}
year = {root=2022}}
```

## License

other

---

*This dataset was exported from [Foundry-ML](https://github.com/MLMI2-CSSI/foundry), a platform for materials science datasets.*