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Understanding beam deflection, particularly for indeterminate beams with overhanging segments and multiple concentrated loads, is crucial for ensuring structural integrity and functionality. The process begins with constructing an accurate free-body diagram, which helps identify the forces and moments acting on the beam. This diagram is vital for visualizing how bending moments vary along the beam's length, influencing its curvature.
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Beam Damage Assessment Using Natural Frequency Shift and Machine Learning.

Nicoleta Gillich1, Cristian Tufisi1, Christian Sacarea2

  • 1Department of Engineering Science, Babeș-Bolyai University, Str. M. Kogălniceanu 1, 400084 Cluj-Napoca, Romania.

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Summary
This summary is machine-generated.

This study introduces machine learning methods, random forest (RF) and artificial neural network (ANN), for accurate crack detection in cantilever beams. These tools efficiently assess crack location and severity, achieving less than 0.6% error in experiments.

Keywords:
artificial neural networkdamage detectionlinear regressionnatural frequencyrandom foresttraining parameters

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Area of Science:

  • Structural Health Monitoring
  • Mechanical Engineering
  • Machine Learning Applications

Background:

  • Modal parameter changes are increasingly used for damage detection.
  • Mathematical models predict natural frequency shifts due to damage.
  • Databases of damage scenarios are complex, especially for multiple cracks.

Purpose of the Study:

  • To propose machine learning methods (RF and ANN) as efficient search tools for damage assessment.
  • To develop databases for crack scenarios in cantilever beams.
  • To accurately locate and quantify crack severity using inverse methods.

Main Methods:

  • Utilized random forest (RF) and artificial neural network (ANN) algorithms.
  • Created databases of damage scenarios for prismatic cantilever beams with single cracks.
  • Implemented a two-step crack assessment: coarse location followed by refined analysis.

Main Results:

  • Achieved high accuracy in estimating crack location and severity for both simulations and laboratory experiments.
  • Reduced crack location errors to less than 0.6%.
  • Demonstrated the effectiveness of the two-step machine learning approach.

Conclusions:

  • The proposed damage assessment method, combined with RF and ANN, is robust and reliable.
  • Machine learning tools significantly improve the efficiency and accuracy of crack detection.
  • The approach is suitable for practical structural health monitoring applications.