library_name: sklearn
tags:
- regression
- scikit-learn
- UCS
- cement
license: mit
pipeline_tag: tabular-regression
emoji: 🚀
colorFrom: blue
colorTo: green
sdk: docker
app_port: 7860
RandomForestRegressor for UCS Prediction
Model Overview
This model is a RandomForestRegressor trained to predict the Unconfined Compressive Strength (UCS) of soil-cement mixtures based on the following features:
- Curing Period (
curing_period): Duration in days. - Compaction Rate (
compaction_rate): Numerical value. - Cement Percentage (
cement_percent): Percentage of cement in the mixture.
Performance
The model achieved an R² score of 0.968 during cross-validation, indicating a high level of accuracy in predicting UCS values.
Feature Ranges
- Curing Period: Min = 0.0 days, Max = 28.0 days, Mean = 11.06 days
- Compaction Rate: Min = 0.5, Max = 1.25, Mean = 0.989
- Cement Percentage: Min = 0.0%, Max = 10.0%, Mean = 5.77%
Usage
To utilize this model, load the model.joblib file and input data within the specified feature ranges to obtain UCS predictions.
Limitations
The model is calibrated for predictions within the specified feature ranges. Using it outside these ranges may result in less accurate predictions. It is specifically designed for soil-cement mixtures and may not be applicable to other materials.
Author
Bogdan TEODORU Gheorghe Asachi Technical University of Iasi (TUIASI)
Contact
For inquiries or suggestions, please contact bteodoru@tuiasi.ro.