Structural Health Monitoring: A Machine Learning Perspective

By Charles R. Farrar, Keith Worden. 2012

1,539 citations recorded by the source corpus.

Subfields: Civil and Structural Engineering

View Paper: https://www.semanticscholar.org/paper/5888c776e38f39efb9b96d0ba2713981008a86b1

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In Civil and Structural Engineering

Citations in the corpus

Machine learning and structural health monitoring overview with emerging technology and high-dimensional data source highlights (2021, methodology): “A feasibility study of utilizing smartphone cameras for seismic structural damage detection was presented by Alzughaibi et al. 321 With their experimental vision-based solution for in-building damage detection, the authors showed sub-millimeter accuracy demonstrating the feasibility of smartphones f”
Energy-based damage localization under ambient vibration and non-stationary signals by ensemble empirical mode decomposition and Mahalanobis-squared distance (2019, methodology): “The main reason for using statistical techniques lies in the fact that the definition of a meaningful relationship between damage and features extracted from raw vibration signals may be difficult and impossible (Farrar and Worden, 2013).”
A Review of Vibration-Based Damage Detection in Civil Structures: From Traditional Methods to Machine Learning and Deep Learning Applications (2020, background): “ending on the type of the damage [12,13]. For instance, failure due to corrosion or fatigue usually occur over long time periods, while earthquakes and fire-induced damage can lead to a rapid failure [14]. Engineering structures are susceptible to human-induced and environmental factors which speed ”
A framework for quantifying the value of vibration-based structural health monitoring (2022, background): “, the system’s natural frequencies and mode shapes [3].”
Data-Driven Methods for Predictive Maintenance of Industrial Equipment: A Survey (2019, methodology): “driven PdM is described in detail, and it is basically consistent with the design methodology of [29].”
A comparison of linear approaches to filter out environmental effects in structural health monitoring (2018, methodology): “These methods are however generally limited to the lowest levels of SHM: damage detection and in some cases, damage localisation or classification and severity if training data are available [1].”
Image‐based post‐disaster inspection of reinforced concrete bridge systems using deep learning with Bayesian optimization (2018, background): “Therefore, structural health condition inspections and rapid damage assessment (Farrar & Worden, 2012) of these bridges, as one of the most critical components in transportation infrastructure systems, are becoming critically important, especially after extreme events (e.g., earthquake).”
Review of Bridge Structural Health Monitoring Aided by Big Data and Artificial Intelligence: From Condition Assessment to Damage Detection (2020, neutral): “Traditional data-driven methods are derived from multivariate statistics as damage detection can be cast into a statistical pattern recognition problem (Farrar and Worden 2012).”
Structural health monitoring: Closing the gap between research and industrial deployment (2018, background): “Farrar and Worden13 point out that the success of machine condition monitoring is due in part to Minimal operational and environmental variability associated with this type of monitoring; Well-defined damage types that occur at known locations; Large databases on similar machines that include data…”