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Related Experiment Video

Updated: Jun 13, 2026

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
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Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

Evaluating the Influence of Normalisation Procedures on a Negative Selection Algorithm to Enhance Damage Detection.

Alberto Barontini1, Maria-Giovanna Masciotta1, Luís F Ramos2

  • 1Department of Engineering and Geology, "G. d'Annunzio" University of Chieti-Pescara, 65127 Pescara, Italy.

Sensors (Basel, Switzerland)
|June 12, 2026
PubMed
Summary
This summary is machine-generated.

This study optimizes damage detection in structural health monitoring using a negative selection algorithm. Feature scaling and artificial damage data generation improve sensitivity to minor structural damage.

Keywords:
bridge managementdamage identificationdynamic identificationvibration-based structural health monitoring

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Last Updated: Jun 13, 2026

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

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Published on: August 16, 2020

Area of Science:

  • Structural Health Monitoring (SHM)
  • Machine Learning for Infrastructure Management
  • Data-Driven Damage Detection

Background:

  • Data-driven machine learning is crucial for SHM, but limited knowledge of damage effects poses challenges.
  • One-class classification algorithms for damage detection require careful parameter tuning, often relying solely on undamaged data, which can reduce sensitivity to minor damage.

Purpose of the Study:

  • Investigate the sensitivity of a Deterministically Generated Negative Selection Algorithm to feature scaling and intrinsic parameters for damage detection.
  • Propose and evaluate a novel strategy for generating artificial damaged data to improve parameter tuning.
  • Generalize findings on feature scaling to other machine learning algorithms used in damage detection.

Main Methods:

  • Employed a numerical case study simulating bridge natural frequencies with temperature effects and three damage scenarios.
  • Explored a wide range of parameter values for the negative selection algorithm, considering multiple feature pairs.
  • Compared four feature scaling methods and evaluated a novel artificial damaged data generation strategy against alternatives.

Main Results:

  • Feature scaling is critical for early detection of minor damage, with limited influence on large damage extents.
  • Z-score normalization offered the best balance between false negatives and positives; methods with denominator multipliers < 1 improved True Positive Rates for small damage.
  • The proposed artificial damaged data generation strategy outperformed approaches using only undamaged data.

Conclusions:

  • Anomaly detection is most effective in feature spaces where damage impacts features differently, such as pairing damage-sensitive features with temperature.
  • Parameter tuning, specifically small detector radii and short censoring distances, enhances algorithm performance across various normalization strategies and damage scenarios.
  • Feature scaling significantly impacts the early detection of minor structural damage, highlighting its importance in SHM.