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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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The rebound hammer test, also known as the Schmidt hammer test, is a non-destructive technique for evaluating the hardness of concrete and, indirectly, the strength of concrete. It operates on the principle that the rebound of a spring-driven mass from a concrete surface correlates to the surface's hardness. The device comprises a mass within a tubular housing, a spring mechanism, and a plunger that strikes the concrete. Upon release, the energy imparted to the mass by the spring causes it...
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Abrasion resistance is an essential characteristic of concrete that determines its durability and longevity under various wear conditions. Concrete surfaces are vulnerable to different types of abrasion. For instance, surfaces may wear down due to the constant movement of vehicles or be eroded by solids carried in water, as seen in concrete canal linings. Specific tests are conducted to measure the abrasion resistance of concrete.
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Fiber Reinforced Concrete01:22

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Fiber-reinforced concrete significantly enhances the structural and nonstructural properties of traditional concrete by incorporating fibers like steel, glass, and polymers. These fibers, varying from natural ones such as sisal and cellulose to manufactured ones like polypropylene and Kevlar, are mixed into hydraulic cement with aggregates. Steel fibers, often preferred for their robustness, contribute to improved ductility, toughness, and post-cracking performance. The concrete is classified...
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Fatigue, in the context of materials science and engineering, refers to the weakening or failure of a material caused by repeatedly applied loads, even if these loads are below the strength limit of the material. Fatigue strength in concrete is a critical property that influences its durability and longevity. Concrete can fail in two ways due to fatigue. Static fatigue or creep rupture occurs under a constant load or one that increases slowly. The other failure mode is due to cyclical or...
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Impact strength in concrete is a critical measure that reflects the material's capability to endure the forces applied during pile driving and when supporting machinery foundations that experience impulsive loads. It is also essential when handling precast concrete components to prevent accidental damage. The impact strength is assessed by observing the concrete's resistance to repeated impacts and energy absorption capacity. A key indicator of significant damage to concrete is when it...
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Application of XGBoost Model Optimized by Multi-Algorithm Ensemble in Predicting FRP-Concrete Interfacial Bond

Yuxin Chen1, Yulin Zhang1, Chuanqi Li1

  • 1School of Resources and Safety Engineering, Central South University, Changsha 410083, China.

Materials (Basel, Switzerland)
|June 27, 2025
PubMed
Summary

This study introduces an advanced XGBoost model optimized with Nevergrad to predict fiber-reinforced polymer (FRP)-concrete bond strength. The new model significantly improves prediction accuracy and provides key insights into influential design factors.

Keywords:
FRP-concrete interfaceNevergradXGBoost modelbond strength predictionexplainable machine learning

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Area of Science:

  • Civil Engineering
  • Materials Science
  • Computational Mechanics

Background:

  • Accurate prediction of fiber-reinforced polymer (FRP)-concrete interfacial bond strength is vital for the safety and longevity of strengthened structures.
  • Traditional empirical models often lack the necessary accuracy for complex structural designs.
  • Developing robust predictive tools is essential for advancing the application of FRP in civil engineering.

Purpose of the Study:

  • To develop a highly accurate and interpretable predictive model for FRP-concrete interfacial bond strength.
  • To overcome the limitations of existing empirical approaches through advanced machine learning techniques.
  • To identify the key parameters governing FRP-concrete bond strength.

Main Methods:

  • Utilized the extreme gradient boosting (XGBoost) machine learning algorithm.
  • Enhanced XGBoost model performance through global hyperparameter optimization using the Nevergrad framework with seven integrated optimizers.
  • Employed a five-fold cross-validation strategy for robust model generalization.
  • Validated the model using 855 single-lap shear test datasets.

Main Results:

  • The optimized XGBoost model achieved superior prediction performance on the test set with R² = 0.9726, RMSE = 1.8745, and MAE = 1.3857.
  • Demonstrated a 22.3% improvement in R² and significant reductions in RMSE (63.4%) and MAE (61.8%) compared to the best existing empirical model.
  • SHAP interpretability analysis identified FRP width, thickness, elastic modulus, and bond length as critical factors influencing bond strength.

Conclusions:

  • The developed XGBoost-Nevergrad model offers a significant advancement in predicting FRP-concrete interfacial bond strength.
  • The model provides a reliable and interpretable tool for engineers, enhancing the design process for FRP-strengthened structures.
  • This intelligent approach combines high predictive accuracy with valuable insights into material behavior and design parameters.