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Abrasion Resistance of Concrete01:23

Abrasion Resistance of Concrete

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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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An Efficient Method for Wheel-Flattened Defects Detection Based on Acoustic Emission Technique.

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    IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control
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    Summary

    This study introduces a new method using an improved synthesized health index (ISHI) and time adaptive threshold (time-ATH) to accurately detect wheel defects using acoustic emission (AE) technology, even with significant wheel-rail rolling interference (WRRI). The proposed approach enhances detection accuracy and rate for flattened wheel defects.

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    Area of Science:

    • Mechanical Engineering
    • Materials Science
    • Non-destructive Testing

    Background:

    • Acoustic emission (AE) technology shows promise for detecting wheel defects.
    • Wheel-rail rolling interference (WRRI) currently hinders the accuracy of AE-based wheel defect detection.
    • Accurate detection of flattened wheel defects is crucial for railway safety.

    Purpose of the Study:

    • To develop a novel detection method for wheel-flattened defects that overcomes the limitations of WRRI.
    • To improve the accuracy and detection rate of AE-based wheel defect identification.
    • To provide an effective solution for AE detection under strong and numerous WRRI conditions.

    Main Methods:

    • Extraction of a comprehensive feature set from AE signals.
    • Fusion of effective features into an improved synthesized health index (ISHI) based on detection rate and accuracy.
    • Development of a time adaptive threshold (time-ATH) calculation to mitigate WRRI influence.
    • Validation of the proposed method using actual datasets.

    Main Results:

    • The proposed ISHI combined with time-ATH significantly improves the detection rate and accuracy of wheel-flattened defects.
    • The method effectively reduces the impact of WRRI on AE signal analysis.
    • Demonstrated superior performance compared to existing methods in challenging WRRI environments.

    Conclusions:

    • The novel detection method offers an effective solution for AE-based detection of wheel-flattened defects.
    • The integration of ISHI and time-ATH enhances the reliability of AE technology in real-world railway conditions.
    • This research advances non-destructive testing capabilities for critical railway components.