Foundations of Population-based SHM, Part II: Heterogeneous populations – Graphs, networks, and communities

Gosliga, Julian; Gardner, Paul A.; Bull, Larry; Dervilis, Nikolaos; Worden, Keith

doi:10.1016/j.ymssp.2020.107144

Cited by 96 publications

(101 citation statements)

References 12 publications

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“…In order to clarify these questions and their potential answers, it will be useful to introduce some terminology to guide the reader through the remainder of the paper without the mathematical complexities that can be found in the general framework described in Bull et al (2020a), Gosliga et al (2020a), and Gardner et al (2020a).…”

Section: The Potential Advantages Of Pbshm; Why It Is Neededmentioning

confidence: 99%

“…The basic concepts in the framework will be summarized here in a compact and comprehensive manner. Following the first steps discussed in the last section, many of the ideas were published in Bull et al (2020b), Gosliga et al (2020b), Gosliga et al (2020c), Gardner and Worden (2020), Worden (2020), and Lin et al (2020), and were then consolidated into (Bull et al, 2020a;Gardner et al, 2020a;Gosliga et al, 2020a).…”

Section: A General Framework For Pbshmmentioning

confidence: 99%

“…The representation associates a parameter vector with each IE, which stores the material properties and dimensions of the IE. The process is described in much more detail in Gosliga et al (2020a) The IE model is then converted into the attributed graph (AG) model. This step is carried out by associating a graph node with each IE; the parameter vector from the IE is attached to the node, thus creating an attributed graph.…”

Section: Irreducible Elements and Attributed Graphsmentioning

confidence: 99%

“…While SS did provide some useful ideas relating to spatio-temporal novelty detection (Hensman et al, 2011), it did not provide a rich enough framework for diagnostics in general populations of structures. Such a general framework was first outlined in the sequence of papers (Bull et al, 2020b;Gardner and Worden, 2020;Gosliga et al, 2020b,c;Lin et al, 2020;Worden, 2020), which were subsequently refined and consolidated into the series: (Bull et al, 2020a;Gardner et al, 2020a;Gosliga et al, 2020a). These papers pointed out that there are differing requirements for population-based SHM, depending on whether the populations are homogenous (i.e., composed of nominallyidentical structures) or heterogeneous.…”

Section: Introductionmentioning

confidence: 99%

“…To reiterate, the aim of this short paper is not to give an overview of SHM in general, or even PBSHM in particular, as there is not enough space; more extensive and detailed discussions can be found in Bull et al (2020a), Gosliga et al (2020a), and Gardner et al (2020a), including a range of challenging case studies and experimental investigations. Furthermore, the emphasis of the work presented here is on the general concepts, emphasizing and illustrating the practical benefits of PBSHM, explaining why it is needed in non-expert terms as far as possible.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

A Brief Introduction to Recent Developments in Population-Based Structural Health Monitoring

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et al. 2020

Front. Built Environ.

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Section: The Potential Advantages Of Pbshm; Why It Is Neededmentioning

confidence: 99%

Section: A General Framework For Pbshmmentioning

confidence: 99%

Section: Irreducible Elements and Attributed Graphsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A Brief Introduction to Recent Developments in Population-Based Structural Health Monitoring

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et al. 2020

Front. Built Environ.

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On the use of domain adaptation techniques for bridge damage detection in a changing environment

Giglioni,

Poole,

Venanzi

et al. 2023

ce papers

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Structural Health Monitoring of civil infrastructures often suffers from the limited availability of damage labelled data. The work here seeks to overcome this issue by using Transfer Learning approaches, in the form of Domain Adaptation, for leveraging information from a source structure with determined health‐state labels to make inferences on an unlabeled monitored structure. The idea is to exploit source data to train a Machine Learning algorithm and achieve improved early‐stage damage detection capabilities across a population of bridges. To account for differences in the underlying distributions of each structure, Transfer Learning is seen as a strategy enabling population‐level bridge SHM. In this paper, the natural frequencies obtained from multiple vibration measurements are extracted to characterise different domains during pristine and abnormal conditions. Such damage‐sensitive features are aligned via Domain Adaptation and used to train a standard classifier within a shared feature space. The methodology is validated on the heterogeneous population composed of the Z24 and S101 bridges. The results prove the effectiveness to successfully exchange damage labels, thus increasing available information for health‐state inference for SHM applications with sparce datasets.

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On the Application of Heterogeneous Transfer Learning to Population-Based Structural Health Monitoring

Gardner

Bull

Dervilis

et al. 2021

Data Science in Engineering, Volume 9

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Population-based structural health monitoring (PBSHM) is a branch of structural health monitoring (SHM) which seeks to leverage information from across a population of structures, with the aim of making robust data-based models that generalise across the population, allowing information to be exchanged and harnessed in constructing better inferences than considering an individual structure alone. PBSHM approaches overcome many of the challenges associated with conventional data-based SHM, such as limited labelled observations in training, classifiers failing to generalise when structural modifications or environmental variations occur etc. Transfer learning provides an important set of tools in performing PBSHM, with the technologies offering mechanisms for transferring label information between structures, and the ability to harness all the available information from all structures in the population, creating a single classification model that generalises across the complete population. This paper explores heterogeneous transfer learning, a branch of transfer learning where datasets have inconsistent feature spaces, i.e. the dimensions of datasets from one structure are different to those from another. In PBSHM, this scenario arises for several reasons; for example, the data acquisition processes on each structure may be different: e.g. the sample rates and durations were different for each structure, leading to transmissibilities with a different number of spectral lines. The paper compares two heterogeneous transfer learning approaches that are formed in a discriminative manner, namely kernelised Bayesian transfer learning and heterogeneous feature augmentation. The techniques are benchmarked against conventional approaches to data-based SHM, with the benefits of a heterogeneous transfer learning approach highlighted by a case study on a Gnat aircraft wing.

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Foundations of Population-based SHM, Part II: Heterogeneous populations – Graphs, networks, and communities

Cited by 96 publications

References 12 publications

A Brief Introduction to Recent Developments in Population-Based Structural Health Monitoring

A Brief Introduction to Recent Developments in Population-Based Structural Health Monitoring

On the use of domain adaptation techniques for bridge damage detection in a changing environment

On the Application of Heterogeneous Transfer Learning to Population-Based Structural Health Monitoring

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