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Damage detection under varying temperature using artificial neural networks
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
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Nonlinear principal component analysis using autoassociative neural networks
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Robust direct adaptive controller for the nonlinear highway bridge benchmark
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Fault-tolerant adaptive control of nonlinear base-isolated buildings using EMRAN
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A robust singular value decomposition for damage detection under changing operating conditions and structural uncertainties
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Novelty detection: a review—part 2:
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Structural damage diagnosis under varying environmental conditions—Part I: A linear analysis
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Structural damage alarming using auto-associative neural network technique: Exploration of environment-tolerant capacity and setup of alarming threshold
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Environmental effects on the identified natural frequencies of the Dowling Hall Footbridge
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Blind identification of damage in time-varying systems using independent component analysis with wavelet transform
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Structural damage identification via a combination of blind feature extraction and sparse representation classification
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Structural Damage Identification using Modal Data. I: Simulation Verification
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Effect of Temperature on Modal Variability of a Curved Concrete Bridge under Ambient Loads
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Eliminating Temperature Effect in Vibration-Based Structural Damage Detection
inferredcites →https://doi.org/10.1080/10589759.2014.949709openalex-snapshot
A robust damage-detection technique with environmental variability combining time-series models with principal components
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Damage diagnosis using time series analysis of vibration signals
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Novelty detection applied to vibration data from a CX-100 wind turbine blade under fatigue loading
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Statistical Damage Classification Under Changing Environmental and Operational Conditions
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ENVIRONMENTAL VARIABILITY OF MODAL PROPERTIES
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Nitric oxide and muscle VO2 kinetics
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Implementation issues of novelty detection technique for cable-supported bridges instrumented with a long-term monitoring system
inferredcites →https://doi.org/10.1117/12.914710openalex-snapshot
Damage detection in carbon composite material typical of wind turbine blades using auto-associative neural networks
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A Summary Review of Correlations between Temperatures and Vibration Properties of Long‐Span Bridges
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Eliminating Environmental or Operational Influences in Structural Health Monitoring using the Missing Data Analysis
inferredcites →https://doi.org/10.1177/1475921703002002005openalex-snapshot
Principal Component Analysis of a Piezosensor Array for Damage Localization
inferredcites →https://doi.org/10.1177/1475921707081968openalex-snapshot
Online Identification of Linear Time-varying Stiffness of Structural Systems by Wavelet Analysis
inferredcites →https://doi.org/10.1177/1475921710388971openalex-snapshot
Machine learning algorithms for damage detection under operational and environmental variability
inferredcites →https://doi.org/10.1177/1475921714542890openalex-snapshot
Damage identification based on response-only measurements using cepstrum analysis and artificial neural networks
inferredcites →https://doi.org/10.12989/sss.2011.7.2.133openalex-snapshot
System identification of a super high-rise building via a stochastic subspace approach
inferredcites →https://doi.org/10.18356/1df6bd7f-enopenalex-snapshot
Arancha González, Executive Director, ITC
inferredcites →https://doi.org/10.4028/www.scientific.net/ast.56.277openalex-snapshot
Current Directions of Structural Health Monitoring and Control in USA
inferredcites →https://doi.org/10.4028/www.scientific.net/kem.558.52openalex-snapshot
Modeling of Environmental Effects for Vibration-Based SHM Using Recursive Stochastic Subspace Identification Analysis
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Neural Network-Based Techniques for Damage Identification of Bridges: A Review of Recent Advances
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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Structural health monitoring research under varying temperature condition: a review
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