Machine Learning-Based Prediction of Freeze-Thaw Resistance in Geopolymer Concrete

This tool is developed as part of the research titled "Machine Learning for Freeze-Thaw Resistance Prediction in Geopolymer Concrete: A Boosting Algorithm Approach with Interpretability and Practical Deployment." It predicts the freeze-thaw resistance of geopolymer concrete, based on key parameters such as base materials, alkali activator, additive types, freeze-thaw scenario, and curing time, using information from the input structure defined below. The model leverages advanced machine learning techniques to assist in designing durable and environmentally resilient concrete.

Note: The initially displayed values are for demonstration purposes only. For accurate predictions, please enter values within the recommended ranges for all fields. Results may be unreliable if inputs fall outside these suggested limits.

Base Material

Type:

Amount (kg/m³):

(Recommended input: 0-1200) (kg/m³)

Input does not meet the range. Possible Misprediction!

Alkali Activator

Type:

NaOH KOH NaOH/Na₂SiO₃

Additives

Additive Option:

Additive Types No Additives

Type:

Amount (kg/m³):

(Recommended input: 0-500) (kg/m³)

Input does not meet the range. Possible Misprediction!

Freeze-Thaw Action

Number of Freeze-Thaw Cycles:

(Recommended input: 0-300) (kg/m³)

Input does not meet the range. Possible Misprediction!

FT Cycle Period (Hours):

(Recommended input: 0-8) (Hours)

Input does not meet the range. Possible Misprediction!

Curing Periods

Curing Days:

(Recommended input: 1-28 Days)

Input does not meet the range. Possible Misprediction!

Enter your parameters and click "Predict Strength" to see results

GUI Developers & Research Team:

Rishav Jaiswal (McMaster University, Canada)

Naresh Bhatta (Kathmandu University, Nepal)