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Related Concept Videos

Microcracking in Concrete01:20

Microcracking in Concrete

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Microcracking in concrete refers to the tiny cracks that can form within the material even before any external load is applied. These microcracks typically occur at the interface between the coarse aggregate and the hydrated cement paste, often as a result of differential volume changes prompted by variations in stress-strain behavior, as well as thermal and moisture movement. Initially, these microcracks remain stable and do not grow substantially until the concrete is stressed to about 30...
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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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Measurement of Air Content in Concrete01:23

Measurement of Air Content in Concrete

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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.
The pressure method,...
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Manufacture of Concrete Masonry Units01:27

Manufacture of Concrete Masonry Units

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The process of manufacturing concrete masonry units begins by mixing stiff concrete composed of Portland cement, aggregates, and water. This mixture is then poured into metal molds. To ensure the concrete settles uniformly and to avoid separation of its components, the mixture in the molds is subjected to vibration. Shortly after, the still-wet blocks are removed from the molds and placed on racks.
These wet blocks are then transported for curing, which can occur in one of two environments: a...
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Effects of Air-entrainment in Concrete01:28

Effects of Air-entrainment in Concrete

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Air entrainment in concrete significantly enhances the material's durability, especially in environments subjected to freeze-thaw cycles. Introducing small air bubbles into the concrete mix acts as internal voids that accommodate the expansion of water when it freezes, thereby alleviating internal stress and preventing structural cracks. This function is crucial in climates with significant freezing and thawing, as it protects the concrete from repeated stresses that could lead to premature...
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Preplaced Aggregate Concrete01:29

Preplaced Aggregate Concrete

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Preplaced aggregate concrete is ideal for construction environments that are not easily accessible. The process begins by properly wetting the gap-graded coarse aggregates to remove the dirt, then placing it in the form and compacting it. Voids are filled with a mortar mix pumped under pressure through slotted pipes. This mortar typically consists of Portland cement, pozzolan, fine aggregates, water, and a fluidizing aid. The pozzolan helps reduce bleeding and segregation while improving the...
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In the Direction of an Artificial Intelligence-Enabled Monitoring Platform for Concrete Structures.

Gloria Cosoli1, Maria Teresa Calcagni1, Giovanni Salerno1

  • 1Department of Industrial Engineering and Mathematical Sciences, Università Politecnica delle Marche, 60131 Ancona, Italy.

Sensors (Basel, Switzerland)
|January 23, 2024
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Summary

This study demonstrates that self-sensing concrete beams can monitor structural health in seismic areas. Artificial intelligence models, particularly Prophet, accurately predict electrical impedance changes, enhancing structural health monitoring (SHM) and maintenance predictions.

Keywords:
Artificial Intelligencecrack detectionearly warningelectrical impedancemonitoringmonitoring platformself-sensing concretevision systems

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Area of Science:

  • Materials Science
  • Civil Engineering
  • Structural Health Monitoring

Background:

  • Structural Health Monitoring (SHM) is crucial for seismic risk management in the built environment.
  • Artificial Intelligence (AI) offers advanced capabilities for analyzing structural data and enabling early warnings.
  • Self-sensing materials, like concrete, present opportunities for integrated structural monitoring.

Purpose of the Study:

  • To evaluate the potential of self-sensing concrete beams for SHM in seismic contexts.
  • To assess the correlation between electrical impedance and applied load in concrete beams.
  • To compare AI-based (Prophet) and statistical (ARIMA, SARIMAX) models for predicting electrical impedance.

Main Methods:

  • Testing self-sensing concrete beams under controlled loading conditions.
  • Measuring electrical impedance changes within the concrete specimens.
  • Utilizing a vision-based system for objective crack assessment.
  • Applying Prophet, ARIMA, and SARIMAX models for electrical impedance prediction.

Main Results:

  • A high correlation (Pearson's r > 0.9) was found between the real part of electrical impedance and applied load, confirming the piezoresistive properties of the concrete.
  • The Prophet model significantly outperformed ARIMA and SARIMAX in predicting electrical impedance, achieving a Mean Absolute Percentage Error (MAPE) below 1.00%.

Conclusions:

  • Self-sensing concrete beams, coupled with vision-based systems and AI, offer a promising approach for SHM.
  • Electrical impedance monitoring effectively indicates structural integrity under load.
  • AI-driven prediction models, especially Prophet, enhance the accuracy and reliability of SHM systems for seismic applications.