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Performance Analysis of Boosting-Based Machine Learning Models for Predicting the Compressive Strength of

Jinwoong Kim1, Daehee Ryu1, Heojeong Hwan1

  • 1Department of Civil Engineering, Chosun University, 10 Chosundae 1-gil, Dong-Gu, Gwangju 61452, Republic of Korea.

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Summary

This study uses machine learning to predict the compressive strength of cementitious composites with biochar (a sustainable material) as a partial cement replacement. The findings support biochar

Keywords:
biocharboosting-based modelcement substitutecompressive strength predictionmachine learningoptimal model

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Area of Science:

  • Materials Science
  • Sustainable Construction
  • Machine Learning Applications

Background:

  • Biochar, derived from pyrolyzed biomass, offers carbon sequestration and potential as a sustainable construction material.
  • Partial replacement of cement with biochar in cementitious composites is explored for low-carbon construction.

Purpose of the Study:

  • To predict the compressive strength of cementitious composites with partial cement replacement by biochar.
  • To evaluate and optimize machine learning models for accurate strength prediction.

Main Methods:

  • Analysis of 716 data samples (experimental and literature-derived).
  • Input variables included water-to-cement ratio, biochar content, and various mix components.
  • Machine learning models (MLR, ENR, SVR, GBM, XGBoost, LightGBM, CatBoost, NGBoost) were employed and optimized using GridSearchCV and Optuna.

Main Results:

  • Gradient Boosting Machine (GBM) initially showed high accuracy.
  • LightGBM achieved the best predictive performance with MAE=3.3258, RMSE=4.6673, MAPE=11.19%, and R²=0.8271.
  • SHAP analysis revealed water-to-cement ratio and cement content as key predictors.

Conclusions:

  • Machine learning models, particularly LightGBM, can accurately predict the compressive strength of biochar-cement composites.
  • Biochar shows significant potential as a partial cement substitute in developing low-carbon construction materials.