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Related Experiment Video

Updated: Sep 17, 2025

Preparation of Aligned Steel Fiber Reinforced Cementitious Composite and Its Flexural Behavior
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Ensemble boosting-based soft-computing models for predicting the bond strength between steel and CFRP plate.

Irwan Afriadi1, Chanachai Thongchom2, Divesh Ranjan Kumar3

  • 1Research Unit in Structural and Foundation Engineering, Department of Civil Engineering, Department of Civil Engineering, Faculty of Engineering, Thammasat School of Engineering, Thammasat University, Khlong Luang, Pathumthani, Thailand.

Scientific Reports
|July 2, 2025
PubMed
Summary

This study explores boosting-based machine learning for Carbon Fiber Reinforced Polymer (CFRP) to steel bonded connections. ADABoost demonstrated superior performance in predicting bond behavior, enhancing structural design accuracy and durability.

Keywords:
ADABoostCATBoostCFRPGBMLGBMXGBoost

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Area of Science:

  • Materials Science
  • Civil Engineering
  • Computational Mechanics

Background:

  • Fiber Reinforced Polymers (FRPs) offer high strength-to-weight ratio and corrosion resistance for structural reinforcement and repair.
  • Understanding FRP-to-steel bonded connection behavior is crucial for modeling debonding failures.
  • Boosting-based machine learning for Carbon Fiber Reinforced Polymer (CFRP)-to-steel bonds is an under-researched area.

Purpose of the Study:

  • To investigate the bond behavior of CFRP sheets bonded to steel beams using boosting-based ensemble machine learning.
  • To evaluate the predictive performance of XGBoost, GBM, CATBoost, LGBM, and ADABoost algorithms for CFRP-steel bond strength.
  • To identify the optimal machine learning model for predicting the maximum load capacity of these bonded connections.

Main Methods:

  • Utilized boosting-based ensemble machine learning algorithms (XGBoost, GBM, CATBoost, LGBM, ADABoost).
  • Selected eight input variables and one output variable (maximum load, PU) for model training and testing.
  • Employed a database of 317 experimental datasets from existing literature.
  • Performed rank analysis using multiple performance criteria to determine the best-performing model.

Main Results:

  • ADABoost outperformed other algorithms in predicting the bond behavior of CFRP sheets on steel beams.
  • ADABoost achieved high accuracy with R² values of 1 for training and 0.99882 for testing.
  • Rank analysis confirmed ADABoost as the optimal model based on various performance metrics.

Conclusions:

  • Boosting-based machine learning, particularly ADABoost, is effective for analyzing CFRP-to-steel bond behavior.
  • This methodology can enhance the accuracy of structural design, reduce costs, and improve the performance and durability of CFRP-reinforced structures.
  • The findings provide valuable insights for the construction industry in utilizing advanced computational techniques for structural retrofitting and reinforcement.