The application of machine learning to structural health monitoring
By Keith Worden, G. Manson. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 2007
448 citations recorded by the source corpus.
Subfields: Civil and Structural Engineering, Mechanical Engineering
View Paper: https://www.semanticscholar.org/paper/c39a7d4c36efd7d08678f6ab6d97e31df91235b5
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Civil and Structural Engineering
- Hoon Sohn
- Keith Worden
- Charles R. Farrar
- Mark A. Girolami
- Ekin Ozer
- Arman Malekloo
- Mohammad AlHamaydeh
- Dana Hughes
In Mechanical Engineering
- Hoon Sohn
- Arman Malekloo
- Mark A. Girolami
- Ekin Ozer
- Mohammad AlHamaydeh
- Nikolaus Correll
- Dana Hughes
- Srinivasan Gopalakrishnan
Citations in the corpus
- Machine learning and structural health monitoring overview with emerging technology and high-dimensional data source highlights (2021, methodology): “81 Embedded in data acquisition devices, noise rejections with low/high bandpass fi lters, resampling, or other techniques can be employed.”
- Review of Bridge Structural Health Monitoring Aided by Big Data and Artificial Intelligence: From Condition Assessment to Damage Detection (2020, unknown): “The data-driven method also establishes a model but is in the form of statistical representations, e.g., the probability density function (Worden and Manson 2006), hence could consider the uncertainties in raw data.”
- Smart Infrastructure Maintenance Using Incremental Tensor Analysis: Extended Abstract (2017, background): “Level 1 can be solved using a one-class learning while levels 2 and 3 usually require a supervised learning approach [26].”
- Effects of environmental and operational variability on structural health monitoring (2007, methodology): “…discordancy of a candidate outlier is measured by the following squared Mahalanobis distance measure D (Barnett & Lewis 1994; Farrar & Worden 2007; Worden & Manson 2007): D Z ðx K xÞ T Cðx K xÞ; where x is a vector of the potential outlier; x and C are the mean vector and covariance matrix of the…”
- An introduction to structural health monitoring (2007, background): “Articles appearing within this theme issue that focus on the statistical modelling portion of the SHM process include Hayton et al. (2007), Sohn (2007) and Worden & Manson (2007).”
- Materials that couple sensing, actuation, computation, and communication (2015, methodology): “As transmissibility measurements are between multiple measurement points, they provide more information than simply detecting resonant frequencies, and can be useful for localization of defects [170].”
- Guided wave based structural health monitoring: A review (2016, methodology): “Various statistical and ML techniques including artificial neural network (ANN), support vector machine (SVM), Bayesian and probabilistic algorithms, fuzzy logic [218], and statistical regressionhave been used in literature for guided wave based SHM [12, 219].”