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Related Concept Videos

Fatigue Strength of Concrete01:22

Fatigue Strength 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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Non-destructive Tests for Concrete Strength01:12

Non-destructive Tests for Concrete Strength

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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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Bonding and Strength of Aggregate01:12

Bonding and Strength of Aggregate

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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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Tensile Strength Considerations of Concrete01:16

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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.
The dimensions and shape of a concrete specimen...
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Impact Strength of Concrete01:21

Impact Strength of Concrete

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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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Relation Between Tensile Strength and Compressive Strength of Concrete01:30

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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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High-Performance Concrete Strength Prediction Based on Machine Learning.

Yanning Liu1

  • 1Shanxi Polytechnic College, Taiyuan 030006, China.

Computational Intelligence and Neuroscience
|June 7, 2022
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Summary

The XGBoost machine learning model accurately predicts high-performance concrete (HPC) compressive strength. This approach enhances concrete design by providing reliable strength predictions, outperforming other models.

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Area of Science:

  • Materials Science
  • Civil Engineering
  • Machine Learning Applications

Background:

  • High-performance concrete (HPC) offers superior mechanical properties and durability for modern construction.
  • Compressive strength is a critical parameter for HPC, necessitating accurate prediction methods.
  • Current engineering applications widely utilize HPC with 28-day strengths between 100-120 MPa, with potential exceeding 200 MPa.

Purpose of the Study:

  • To investigate the predictive accuracy of machine learning algorithms for high-performance concrete compressive strength.
  • To compare the performance of XGBoost, Support Vector Regression (SVR), and Random Forest (RF) algorithms on HPC strength prediction.
  • To identify the most effective machine learning model for reliable HPC compressive strength estimation.

Main Methods:

  • A dataset of 60 concrete samples was prepared to capture variations in material properties.
  • Eight input variables related to concrete composition were used to train machine learning models.
  • The XGBoost, SVR, and RF algorithms were applied to predict the compressive strength of HPC.

Main Results:

  • The XGBoost model demonstrated the highest prediction accuracy among the evaluated algorithms.
  • XGBoost achieved an R-squared value of 0.9993 and an RMSE of 1.372 on the test set.
  • The study highlights the significant impact of model selection on prediction accuracy for HPC.

Conclusions:

  • The XGBoost algorithm is highly effective for predicting the compressive strength of high-performance concrete.
  • Accurate HPC strength prediction using machine learning can significantly aid in structural design and material optimization.
  • Model selection is a crucial factor in achieving reliable and precise predictions in concrete technology.