README.md

---
library_name: sklearn
tags:
- tabular-regression
- machine-learning
- civil-engineering
datasets:
- custom
license: mit
---

# Model Name: c3cf

## Model Description

**c3cf**, Cascade Forest models for predicting the Compressive strength of Coal-ash-incorporated Cement composites, were developed in the research article: [Coal ashes as supplementary cementitious materials: physicochemical property effects on hydration and strength, along with property-informed machine learning modeling](). They are tree-based ensemble models that implements the [deep forest](https://github.com/LAMDA-NJU/Deep-Forest) algorithm. 

- **Developed by:** Kangyi Cai @ Missouri S&T
- **Model type:** Cascade Forest
- **Language(s):** Python
- **License:** MIT

## Uses & Limitations
**c3cf** can make reasonable predictions for coal-ash-incorporated cement mortars, whose strength is in the range of 15-65 MPa, replacement level of coal ash is <50% by mass, and curing age is between 7 and 91 days.

## How to Get Started with the Model
This repository contains a collection of regression models located in the `regs/` directory. Refer to the [kycai/c3cf](https://github.com/kycai/c3cf) for detailed guides.

```python
from huggingface_hub import snapshot_download
path = snapshot_download(repo_id="kycai23/c3cf")
```

## Training & Evaluation
Refer to the [research article]() mentioned above.