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Assessing Blood pressure using a doppler ultrasound01:19

Assessing Blood pressure using a doppler ultrasound

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A Deep Learning-Based Method for Stress Measurement Using Longitudinal Critically Refracted Waves.

Yong Gan1, Jingkun Ma1, Binpeng Zhang2

  • 1School of Mechanical and Electrical Engineering, Guilin University of Electronic Technology, Guilin 541004, China.

Sensors (Basel, Switzerland)
|April 14, 2026
PubMed
Summary

This study introduces a deep learning model using ultrasonic waves for accurate stress prediction in steel structures. The intelligent, calibration-free method enhances structural health monitoring and service life prediction.

Keywords:
convolutional neural networkdeep learninglongitudinal critically refracted wavesstress measurementultrasonic

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Area of Science:

  • Materials Science
  • Structural Engineering
  • Artificial Intelligence

Background:

  • Accurate stress measurement is crucial for structural integrity, health monitoring, and service life prediction of steel infrastructures.
  • Traditional methods may require manual feature extraction or explicit physical modeling, limiting efficiency and applicability.

Purpose of the Study:

  • To propose a novel deep learning approach for direct stress prediction from ultrasonic signals.
  • To develop an intelligent and calibration-free method for stress evaluation in steel structures.

Main Methods:

  • Utilized longitudinal critically refracted (LCR) ultrasonic waves for stress evaluation.
  • Integrated gated recurrent units (GRU), attention mechanisms, and 1D convolutional neural networks (1D-CNN) for direct stress prediction.
  • Acquired LCR signals from steel specimens under varying uniaxial stresses (0-200 MPa) using a custom piezoelectric ultrasonic system.

Main Results:

  • The deep learning model achieved a mean absolute error of 1.94 MPa for stress prediction.
  • Generalization tests showed high accuracy across diverse stress levels, with average errors below 3 MPa.
  • The model demonstrated robustness and reliability in stress evaluation.

Conclusions:

  • The proposed deep learning approach offers an accurate, intelligent, and calibration-free ultrasonic method for stress evaluation.
  • This research provides practical support for real-time stress assessment in steel structures under operational conditions.
  • The findings contribute to enhanced structural health monitoring and extended service life prediction for steel infrastructures.