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Related Concept Videos

Behavior of Concrete Under Compressive Load01:23

Behavior of Concrete Under Compressive Load

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Concrete exhibits specific behaviors under different compressive loads. Understanding this is crucial for understanding its structural integrity. When concrete undergoes uniaxial compression, it tends to develop cracks that run parallel to the direction of the force. These parallel cracks stem from localized tensile stresses that occur perpendicular to the compression direction. Additionally, angled cracks may appear due to the formation of shear planes.
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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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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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Upon subjecting concrete to moderate or high uniaxial compressive or tensile stresses, the strain response is non-linear relative to the stress applied. As the stress is removed, the resulting stress-strain curve deviates from the original path traced during loading, creating a hysteresis loop, indicative of the concrete's non-linear and non-elastic properties. Typically, a material's modulus of elasticity, which is a measure of the material's stiffness, is inferred from the linear...
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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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Strength tests for cement are not performed directly on neat cement paste due to difficulty in obtaining consistent, reliable specimens. Instead, cement is typically tested in the form of cement-sand mortar.
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Explainable Ensemble Learning and Multilayer Perceptron Modeling for Compressive Strength Prediction of

Yaren Aydın1, Celal Cakiroglu2, Gebrail Bekdaş1

  • 1Department of Civil Engineering, Istanbul University-Cerrahpaşa, 34320 Istanbul, Turkey.

Biomimetics (Basel, Switzerland)
|September 27, 2024
PubMed
Summary
This summary is machine-generated.

Machine learning models accurately predict ultra-high-performance concrete compressive strength. Ensemble stacking regressor models outperformed multilayer perceptron, with concrete age and material composition being key predictors.

Keywords:
ANNSHAPUHPCXGBoostcompressive strengthstacking regressor

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Area of Science:

  • Civil Engineering
  • Materials Science
  • Data Science

Background:

  • Ultra-high-performance concrete (UHPC) offers enhanced durability and enables thinner structural elements.
  • Predicting UHPC compressive strength is crucial for material design and application.
  • Traditional methods for determining compressive strength are time-consuming.

Purpose of the Study:

  • To develop and compare machine learning models for predicting UHPC compressive strength.
  • To interpret the developed models using feature importance and SHAP analysis.
  • To assess the impact of data splitting strategies on model performance.

Main Methods:

  • Utilized multilayer perceptron (MLP) and an ensemble Stacking Regressor model.
  • The Stacking Regressor integrated outputs from Extreme Gradient Boosting (XGBoost), Category Boosting (CatBoost), Light Gradient Boosting Machine (LightGBM), and Extra Trees regressors.
  • Investigated the effect of different random splits for training and testing datasets.

Main Results:

  • The Stacking Regressor achieved a superior average R-squared score of 0.971 on the test set.
  • The MLP model attained an average R-squared score of 0.909.
  • Feature importance analysis revealed that concrete age, silica fume, fiber, superplasticizer, cement, aggregate, and water content significantly influence predictions.

Conclusions:

  • Ensemble machine learning, particularly the Stacking Regressor, provides a highly accurate method for predicting UHPC compressive strength.
  • Model interpretability through SHAP analysis highlights critical material components and age affecting concrete performance.
  • The findings support the use of machine learning for efficient material characterization in concrete technology.