# Performance Analysis of Boosting-Based Machine Learning Models for Predicting the Compressive Strength of Biochar-Cementitious Composites

## TL;DR

This study uses machine learning to predict the compressive strength of cement mixed with biochar, finding that biochar can reduce carbon emissions while maintaining mechanical performance.

## Contribution

The novel use of boosting-based machine learning models to predict biochar-cement composite strength with high accuracy.

## Key findings

- Biochar content and properties strongly influence compressive strength of cementitious composites.
- LightGBM achieved the best predictive performance with MAE = 3.3258 and R2 = 0.8271.
- Water-to-cement ratio and cement content are the dominant predictors of compressive strength.

## Abstract

What are the main findings?
Biochar content and properties significantly affect compressive strength of cementitious composites.Optimal biochar dosages improve mechanical performance while supporting carbon reduction.Machine learning models accurately capture strength trends of biochar-modified composites.

Biochar content and properties significantly affect compressive strength of cementitious composites.

Optimal biochar dosages improve mechanical performance while supporting carbon reduction.

Machine learning models accurately capture strength trends of biochar-modified composites.

What are the implications of the main findings?
Biochar can be effectively used to design more sustainable cementitious materials.Data-driven models reduce experimental effort in strength prediction and mix optimization.Findings support low-carbon construction practices and performance-based material design.

Biochar can be effectively used to design more sustainable cementitious materials.

Data-driven models reduce experimental effort in strength prediction and mix optimization.

Findings support low-carbon construction practices and performance-based material design.

Biochar, a carbon-rich material produced through the pyrolysis of wood residues and agricultural byproducts, has carbon storage capacity and potential as a low-carbon construction material. This study predicts the compressive strength of cementitious composites in which cement is partially replaced with biochar using machine learning models. A total of 716 data samples were analyzed, including 480 experimental measurements and 236 literature-derived values. Input variables included the water-to-cement ratio (W/C), biochar content, cement, sand, aggregate, silica fume, blast furnace slag, superplasticizer, and curing conditions. Predictive performance was evaluated using Multiple Linear Regression (MLR), Elastic Net Regression (ENR), Support Vector Regression (SVR), and Gradient Boosting Machine (GBM), with GBM showing the highest accuracy. Further optimization was conducted using XGBoost, Light Gradient-Boosting Machine (LightGBM), CatBoost, and NGBoost with GridSearchCV and Optuna. LightGBM achieved the best predictive performance (mean absolute error (MAE) = 3.3258, root mean squared error (RMSE) = 4.6673, mean absolute percentage error (MAPE) = 11.19%, and R2 = 0.8271). SHAP analysis identified the W/C and cement content as dominant predictors, with fresh water curing and blast furnace slag also exerting strong influence. These results support the potential of biochar as a partial cement replacement in low-carbon construction material.