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Related Concept Videos

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The bond between aggregate particles and the cement matrix is significantly influenced by the shape and surface texture of the aggregates. High-strength concretes benefit from a rougher texture, which leads to stronger bonding due to greater adhesion. Angular aggregates with larger surface areas also enhance this bond. The bonding quality, however, is complex to assess as no universally accepted test exists. Good bonding is indicated when a crushed concrete specimen shows some aggregate...
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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.