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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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The dynamic modulus of elasticity assesses how a concrete structure deforms under impact or dynamic loads. It is typically higher than the static modulus of elasticity, measured under slow, steady loading conditions.
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Elasticity in Concrete01:20

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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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Fatigue Strength of Concrete01:22

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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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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 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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ConcreteXAI: A multivariate dataset for concrete strength prediction via deep-learning-based methods.

José A Guzmán-Torres1, Francisco J Domínguez-Mota1, Elia M Alonso-Guzmán1

  • 1Civil Engineering Faculty, Universidad Michoacana de San Nicolás de Hidalgo, Morelia, Michoacán 58030, Mexico.

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Summary

This study introduces ConcreteXAI, a 10-year dataset of concrete mechanical and non-destructive tests. It supports AI-driven prediction models for concrete performance, enhancing material science research.

Keywords:
Artificial intelligenceCompressive strength predictionConcrete propertiesMechanical testsNon-destructive tests

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Area of Science:

  • Materials Science
  • Civil Engineering
  • Data Science

Background:

  • Concrete is a vital global construction material, but formulating optimal mixtures with novel additives presents challenges.
  • Existing methods for predicting concrete performance lack diversity in materials and properties considered.
  • Developing robust predictive models for concrete properties is crucial for advancing construction technology.

Purpose of the Study:

  • To introduce the ConcreteXAI dataset, a comprehensive 10-year compilation of concrete mechanical and non-destructive test results.
  • To provide a resource for developing advanced deep learning-based prediction models for concrete attributes.
  • To facilitate research in concrete material science and engineering through a large-scale, diverse dataset.

Main Methods:

  • Conducted a 10-year laboratory investigation involving mechanical tests and non-destructive assessments on concrete materials.
  • Compiled a dataset (ConcreteXAI) comprising 18,480 data points from twelve distinct concrete formulations with varied additives and aggregates.
  • Designed the dataset for seamless integration with deep learning models for predictive analysis.

Main Results:

  • The ConcreteXAI dataset offers extensive data on mechanical performances and non-destructive tests for diverse concrete mixtures.
  • It includes detailed evaluations of compressive strength, flexural strength, tensile strength, and durability indicators like homogeneity and porosity.
  • The dataset serves as a cutting-edge resource for analyzing concrete properties and performance.

Conclusions:

  • The ConcreteXAI dataset is a valuable resource for researchers aiming to develop high-quality prediction models for concrete elements.
  • Deep learning techniques can be precisely applied using this dataset to predict or estimate desired concrete attributes.
  • This work advances the application of AI in concrete material science, enabling more accurate performance predictions.