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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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Microcracking in concrete refers to the tiny cracks that can form within the material even before any external load is applied. These microcracks typically occur at the interface between the coarse aggregate and the hydrated cement paste, often as a result of differential volume changes prompted by variations in stress-strain behavior, as well as thermal and moisture movement. Initially, these microcracks remain stable and do not grow substantially until the concrete is stressed to about 30...
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Segregation in fresh concrete is a phenomenon where the components of the concrete mix separate, leading to uneven distribution and compromised structural integrity. This separation typically occurs when concrete is subjected to excessive horizontal movement within forms, or when it is dropped from considerable heights or forced through narrow, winding paths. As a result, heavier coarse aggregate particles settle at the bottom, while lighter, finer materials such as cement and water rise to the...
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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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Shrinkage in concrete is primarily due to water loss from evaporation, hydration of cement, or carbonation, leading to a reduction in volume. The volumetric contraction results in volumetric strain in concrete. However, in practice, shrinkage is measured as linear strain, which is one-third of the volumetric strain.
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Interpretable Machine Learning for Predicting Splitting Strength of Asphalt Concrete: Insights from SHAP Analysis.

Jianglei Xing1, Xiao Tan1,2, Yihao Li1

  • 1College of Water Conservancy and Hydropower Engineering, Hohai University, Nanjing 210024, China.

Materials (Basel, Switzerland)
|May 4, 2026
PubMed
Summary

This study introduces an interpretable machine learning model to predict asphalt concrete splitting strength, aiding data-driven mixture design. The TabPFN model demonstrated superior performance, identifying key variables for enhanced asphalt concrete properties.

Keywords:
SHAP interpretationTabPFNasphalt concreteexplainable artificial intelligencesplitting strength

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Area of Science:

  • Civil Engineering
  • Materials Science
  • Computer Science

Background:

  • Asphalt concrete performance is critical for road infrastructure.
  • Predicting splitting strength is essential for durable asphalt mixtures.
  • Data-driven approaches can optimize asphalt concrete mixture design.

Purpose of the Study:

  • To develop and evaluate interpretable machine learning models for asphalt concrete splitting strength prediction.
  • To support data-driven asphalt concrete mixture design using predictive modeling.
  • To identify key material parameters influencing asphalt concrete splitting strength.

Main Methods:

  • A database of 296 asphalt concrete samples was compiled.
  • Fourteen input variables (asphalt properties, aggregate gradation, fiber characteristics) were used for modeling.
  • Eight machine learning models (TabPFN, ANN, SVR, RF, XGBoost, LightGBM, FLAML, FT-Transformer) were developed and compared using Monte Carlo cross-validation.

Main Results:

  • All eight models showed satisfactory predictive capabilities.
  • The TabPFN model achieved the best performance with the lowest RMSE (0.34 ± 0.10) and highest R² (0.85 ± 0.08).
  • SHAP analysis identified Ag9.5, AC, Du, FT, and Ag4.75 as dominant variables, with quantified optimal ranges for improved splitting strength.

Conclusions:

  • Interpretable machine learning, particularly TabPFN, effectively predicts asphalt concrete splitting strength.
  • The study provides valuable insights into key parameters for optimizing asphalt concrete mixtures.
  • A user-friendly interface was developed for practical application of the predictive model and interpretation framework.