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Related Concept Videos

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Air content measurement in concrete is critical for ensuring structural integrity and durability of concrete structures, especially in environments prone to severe weather conditions. Accurate air content analysis optimizes concrete's resistance to freeze-thaw cycles and enhances its workability and strength. Several methods are standardized under ASTM guidelines to measure the air content in fresh concrete, each suitable for different concrete types and conditions.
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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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Permeability in the context of concrete refers to how easily liquids or gases can pass through the material. This quality is crucial for assessing the water-tightness and durability of concrete structures and their resistance to chemical attacks. Concrete permeability can be determined through comparative laboratory tests. These tests typically involve sealing a concrete specimen from the sides, applying water pressure to the top surface with pressure, and measuring the amount of water passing...
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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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Inverse Estimation of Moisture Diffusion Model for Concrete Using Artificial Neural Network.

Jae Min Lee1, Chang Joon Lee1

  • 1Department of Architectural Engineering, Chungbuk National University, Cheongju 286442, Korea.

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|September 9, 2022
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Summary

This study used artificial neural networks (ANNs) to accurately estimate concrete's moisture diffusion model. The ANN model successfully predicted moisture distribution, showing good agreement with known models.

Keywords:
artificial neural network (ANN)concrete moisture diffusion modelinverse estimation

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Area of Science:

  • Materials Science
  • Civil Engineering
  • Computational Modeling

Background:

  • Accurate modeling of moisture diffusion in concrete is crucial for predicting material durability and performance.
  • Traditional methods for estimating diffusion parameters can be time-consuming and may not account for real-world complexities.
  • Artificial Neural Networks (ANNs) offer a powerful data-driven approach for complex inverse problems in engineering.

Purpose of the Study:

  • To inversely estimate the moisture diffusion model for concrete using artificial neural networks (ANNs).
  • To predict moisture distribution within concrete using a trained ANN model.
  • To validate the performance of the ANN-based model against a known diffusion model and virtual experimental data.

Main Methods:

  • Virtual experiments were generated by simulating moisture distribution and adding noise to a known concrete moisture diffusion model.
  • Two distinct artificial neural network (ANN) architectures were employed for the inverse estimation of the diffusion model.
  • The accuracy of the inversely estimated ANN model was evaluated by comparing its predictions with the original known model and simulated data.

Main Results:

  • The inversely estimated ANN model demonstrated a strong agreement with the known moisture diffusion model used in the virtual experiments.
  • The ANN model successfully predicted moisture distribution, validating its capability in numerical analysis.
  • Performance comparison confirmed the reliability of the ANN approach for estimating concrete moisture diffusion parameters.

Conclusions:

  • Artificial neural networks provide an effective tool for the inverse estimation of moisture diffusion models in concrete.
  • The developed ANN model can accurately predict moisture distribution, aiding in material performance assessment.
  • This data-driven methodology offers a promising alternative for analyzing complex transport phenomena in construction materials.