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Related Concept Videos

Wave Parameters01:10

Wave Parameters

The simplest mechanical waves are associated with simple harmonic motion and repeat themselves for several cycles. These simple harmonic waves can be modeled using a combination of sine and cosine functions. Consider a simplified surface water wave that moves across the water's surface. Unlike complex ocean waves, in surface water waves, water moves vertically, oscillating up and down, whereas the disturbance of the wave moves horizontally through the medium. If a seagull is floating on the...
Interference and Superposition of Waves01:07

Interference and Superposition of Waves

When two waves of the same nature occur in the same region simultaneously, they result in interference. Interference of waves implies that the net effect of the waves is the sum of the individual waves' effects. However, it does not imply that the individual waves affect the propagation of other waves.
Interference occurs in mechanical waves, such as sound waves, waves on a string, and surface water waves. Mechanical waves correspond to the physical displacement of particles. Hence,...

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Related Experiment Video

Updated: May 22, 2026

Environmental Dynamic Mechanical Analysis to Predict the Softening Behavior of Neural Implants
06:59

Environmental Dynamic Mechanical Analysis to Predict the Softening Behavior of Neural Implants

Published on: March 1, 2019

A shallow Bayesian neural network with wavelet transform for impact localization in multi-material plate structures.

Hussain Altammar1

  • 1Department of Mechanical Engineering, College of Engineering, King Faisal University, Al-Ahsa 31982, Saudi Arabia.

Ultrasonics
|May 20, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a Bayesian-regularized artificial neural network (BRANN) for efficient impact localization in multi-material structures. The model accurately identifies impact locations with minimal error, offering a robust solution for structural health monitoring.

Keywords:
Additive manufacturingBayesian regularizationImpact localizationShallow neural networkStructural health monitoring

Related Experiment Videos

Last Updated: May 22, 2026

Environmental Dynamic Mechanical Analysis to Predict the Softening Behavior of Neural Implants
06:59

Environmental Dynamic Mechanical Analysis to Predict the Softening Behavior of Neural Implants

Published on: March 1, 2019

Area of Science:

  • Structural Health Monitoring (SHM)
  • Artificial Intelligence (AI)
  • Materials Science

Background:

  • Developing robust and computationally efficient impact localization systems for structural health monitoring (SHM) is challenging, especially for structures with varying material properties.
  • Bayesian regularization in artificial neural networks (ANNs) shows promise but requires further exploration for cross-material generalization in SHM.

Purpose of the Study:

  • To propose and validate a shallow Bayesian-regularized artificial neural network (BRANN) model for accurate and efficient impact localization in multi-material structures.
  • To assess the model's performance across different materials, sensor layouts, impact energies, and locations.

Main Methods:

  • Utilized a shallow BRANN with two hidden layers, processing time-delay features of impact-induced guided waves.
  • Extracted signal features using wavelet transform from piezoelectric sensor data.
  • Experimentally validated the model on aluminum and polylactic acid plate structures with varying impact conditions and sensor configurations.

Main Results:

  • Achieved an average impact localization error below 4.4% across all tested variables (materials, energies, locations, sensor layouts).
  • Demonstrated high computational efficiency with an inference time under 8μs.
  • The BRANN model proved robust and accurate for multi-material structures.

Conclusions:

  • The proposed shallow BRANN combined with wavelet analysis offers a computationally efficient and robust solution for impact localization in resource-constrained SHM applications.
  • This approach overcomes limitations of complex deep learning models for SHM tasks involving material variability.