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GRNN-based cascade ensemble model for non-destructive damage state identification: small data approach.

Ivan Izonin1,2, Athanasia K Kazantzi1, Roman Tkachenko3

  • 1Department of Civil Engineering, School of Engineering, University of Birmingham, Birmingham, B15 2FG UK.

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Summary
This summary is machine-generated.

This study introduces a machine learning model for rapid, non-destructive bridge damage assessment. It accurately identifies structural issues like tendon loss from deflection data, improving safety and restoration planning.

Keywords:
Artificial intelligenceBridgesCascadeDamage characterisationEnsemble modelGRNNInterdependent output variablesNon-destructiveSmall data approach

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Area of Science:

  • Structural Engineering
  • Machine Learning
  • Artificial Intelligence

Background:

  • Ageing infrastructure faces climate-induced degradation, challenging traditional inspection methods.
  • Conventional prestressed concrete bridge inspections often miss latent defects like tendon loss, leading to potential failures.
  • Existing methods necessitate expensive continuous monitoring due to limitations in detecting subtle damage.

Purpose of the Study:

  • To address the capability gap in early detection of bridge damage.
  • To propose a novel machine learning approach for rapid, non-destructive assessment of bridge structural health.
  • To enable informed structural interventions and effective restoration planning.

Main Methods:

  • Assembled a comprehensive training dataset by simulating various bridge damage scenarios, including different degrees and patterns of tendon losses.
  • Developed a novel General Regression Neural Network (GRNN)-based cascade ensemble model for predicting interdependent output attributes from limited data.
  • Optimised the cascade model using the differential evolution method and validated it on a real long-span bridge.

Main Results:

  • The proposed GRNN-based cascade ensemble model demonstrated high accuracy in identifying bridge damage states.
  • The model effectively predicted structural damage based on measurable structural deflections, outperforming existing methods.
  • Validation on a real long-span bridge confirmed the model's efficacy and reliability in non-destructive damage assessment.

Conclusions:

  • The developed machine learning model offers a practical solution for non-destructive bridge damage assessment.
  • Accurate and reliable damage identification facilitates timely structural interventions and effective restoration planning.
  • This approach enhances the safety of ageing structures subjected to climate-induced stressors.