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Concrete is a fundamental building material, and understanding its strengths is crucial for construction projects. The relationship between its tensile and compressive strengths is intricate, showing that while these strengths are related, they do not increase at the same rate. Tensile strength's growth is slower and is affected by various factors such as the methods used for testing, the size and shape of the specimen, the texture of the aggregate used, and the moisture content of the...
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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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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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Considering the tensile strength of concrete involves recognizing that the theoretical strength of cement paste can be up to a thousand times higher than what is observed in practical applications. This significant discrepancy is largely attributed to the presence of microscopic cracks within the concrete. These cracks tend to amplify stress at their tips when a load is applied, a phenomenon explained by Griffith's theory of brittle fracture.
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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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Compressive Strength Evaluation of Ultra-High-Strength Concrete by Machine Learning.

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Materials (Basel, Switzerland)
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Researchers used advanced soft computing techniques like XGBoost to accurately predict ultra-high-strength concrete (UHSC) compressive strength. Curing time significantly impacts strength, offering efficient material assessment for civil engineering applications.

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Area of Science:

  • Civil Engineering
  • Materials Science
  • Computational Intelligence

Background:

  • Ultra-high-strength concrete (UHSC) is a critical material in modern civil engineering.
  • Accurate estimation of UHSC properties, like compressive strength, is vital for cost-effective and time-efficient construction.
  • Soft computing approaches offer sophisticated methods for predicting material characteristics.

Purpose of the Study:

  • To employ advanced soft computing techniques for estimating the compressive strength of ultra-high-strength concrete (UHSC).
  • To compare the performance of XGBoost, AdaBoost, and Bagging algorithms in predicting UHSC compressive strength.
  • To identify key factors influencing UHSC compressive strength using explainability analysis.

Main Methods:

  • Utilized soft computing techniques: XGBoost, AdaBoost, and Bagging.
  • Input variables included mix design parameters (cement, fly ash, silica fume, sand, water, superplasticizer) and material properties (steel fiber, aspect ratio, curing time).
  • Evaluated model performance using statistical metrics: Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Coefficient of Determination (R²).
  • Applied SHapley Additive exPlanations (SHAP) for feature importance analysis.

Main Results:

  • The XGBoost model demonstrated superior performance with a high R² value of 0.90 and minimal errors compared to AdaBoost and Bagging.
  • SHAP analysis revealed that curing time exerted the most significant positive influence on UHSC compressive strength.
  • The developed models provide a reliable method for predicting UHSC compressive strength based on mix proportions and curing conditions.

Conclusions:

  • XGBoost is a highly accurate and effective soft computing technique for predicting UHSC compressive strength.
  • Understanding the influence of factors like curing time can optimize UHSC performance.
  • This study provides a valuable tool for engineers and researchers to efficiently assess UHSC properties, facilitating better structural design and material selection.