Foundations of population-based SHM, Part IV: The geometry of spaces of structures and their feature spaces

Tsialiamanis, George; Mylonas, Charilaos; Chatzi, Eleni; Dervilis, Nikolaos; Wagg, DJ; Worden, Keith

doi:10.1016/j.ymssp.2021.107692

Cited by 25 publications

(18 citation statements)

References 31 publications

(81 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In terms of volume, assume a structure with multiple sensors at different locations, in addition to external monitoring of weather and traffic with sensor and computer vision. Together they can include more than hundreds of sensors, and with 24 h continuous monitoring of structure populations, [267][268][269][270] they can generate an enormous amount of data in terms of petabytes per week. Processing this many data, let alone the vast storage required, is a major challenge for continuous SHM monitoring solutions.…”

Section: Big Data and Shm A Symbiotic Strategymentioning

confidence: 99%

Machine learning and structural health monitoring overview with emerging technology and high-dimensional data source highlights

Malekloo

Özer

AlHamaydeh

et al. 2021

Structural Health Monitoring

219

View full text Add to dashboard Cite

Conventional damage detection techniques are gradually being replaced by state-of-the-art smart monitoring and decision-making solutions. Near real-time and online damage assessment in structural health monitoring (SHM) systems is a promising transition toward bridging the gaps between the past’s applicative inefficiencies and the emerging technologies of the future. In the age of the smart city, Internet of Things (IoT), and big data analytics, the complex nature of data-driven civil infrastructures monitoring frameworks has not been fully matured. Machine learning (ML) algorithms are thus providing the necessary tools to augment the capabilities of SHM systems and provide intelligent solutions for the challenges of the past. This article aims to clarify and review the ML frontiers involved in modern SHM systems. A detailed analysis of the ML pipelines is provided, and the in-demand methods and algorithms are summarized in augmentative tables and figures. Connecting the ubiquitous sensing and big data processing of critical information in infrastructures through the IoT paradigm is the future of SHM systems. In line with these digital advancements, considering the next-generation SHM and ML combinations, recent breakthroughs in (1) mobile device-assisted, (2) unmanned aerial vehicles, (3) virtual/augmented reality, and (4) digital twins are discussed at length. Finally, the current and future challenges and open research issues in SHM-ML conjunction are examined. The roadmap of utilizing emerging technologies within ML-engaged SHM is still in its infancy; thus, the article offers an outlook on the future of monitoring systems in assessing civil infrastructure integrity.

show abstract

Section: Big Data and Shm A Symbiotic Strategymentioning

confidence: 99%

Machine learning and structural health monitoring overview with emerging technology and high-dimensional data source highlights

Malekloo

Özer

AlHamaydeh

et al. 2021

Structural Health Monitoring

219

View full text Add to dashboard Cite

show abstract

“…Although the two bridges are very different in design (forming a heterogeneous population), there are similarities in their modal responses 4 . Despite different absolute values in natural frequencies, the first and third natural frequencies of the Z24 have correspondence with the tenth and twelfth natural frequencies of the KW51; both represent vertical bending modes of the deck, with effectively the same nodal and anti-nodal pattern (see [19] and [20] for a visualisation of the Z24 and KW51 mode shapes, respectively).…”

Section: Z24 and Kw51 Datasetsmentioning

confidence: 99%

“…This lack of available health-state data means that most approaches are limited to novelty detection, or only being able to identify previously-seen health-states. Populationbased structural health monitoring (PBSHM) is a branch of SHM that seeks to overcome these challenges by expanding the set of available labelled health-state data by considering a population of structures [1][2][3][4]. By pooling together datasets from a population, there is likely to be more labelled information available and an increase in the potential to diagnose more health-states of interest.…”

Section: Introductionmentioning

confidence: 99%

“…population is heterogeneous [1][2][3][4]. Second, issues arise because the monitoring setup is not exactly equivalent for each member of the population.…”

Section: Introductionmentioning

confidence: 99%

“…A case study inferring the mapping between a population of two numerical shear-building structures is presented in Sect. 4, where the approach is benchmarked against several other GMM models. In Sect.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Domain-adapted Gaussian mixture models for population-based structural health monitoring

Gardner

Bull

Dervilis

et al. 2022

J Civil Struct Health Monit

View full text Add to dashboard Cite

Transfer learning, in the form of domain adaptation, seeks to overcome challenges associated with a lack of available health-state data for a structure, which severely limits the effectiveness of conventional machine learning approaches to structural health monitoring (SHM). These technologies utilise labelled information across a population of structures (and physics-based models), such that inferences are improved, either for the complete population, or for particular target structures — enabling a population-based view of SHM. The aim of these methods is to infer a mapping between each member of the population’s feature space (called a domain) in which a classifier trained on one member of the population will generalise to the remaining structures. This paper introduces the domain-adapted Gaussian mixture model (DA-GMM) for population-based SHM (PBSHM) scenarios. The DA-GMM, infers a linear mapping that transforms target data from one structure onto a Gaussian mixture model that has been inferred from source data (from another structure). The proposed model is solved via an expectation maximisation technique. The method is demonstrated on three case studies: an artificial dataset demonstrating the approach’s effectiveness when the target domain differs by two-dimensional rotations; a population of two numerical shear-building structures; and a heterogeneous population of two bridges, the Z24 and KW51 bridges. In each case study, the method is shown to provide informative results, outperforming other conventional forms of GMM (where no target labelled data are assumed available), and provide mappings that allow the effective exchange of labelled information from source to target datasets.

show abstract

Population-Based Structural Health Monitoring

Gardner

Bull

Gosliga

et al. 2021

Structural Integrity

View full text Add to dashboard Cite

Foundations of population-based SHM, Part IV: The geometry of spaces of structures and their feature spaces

Cited by 25 publications

References 31 publications

Machine learning and structural health monitoring overview with emerging technology and high-dimensional data source highlights

Machine learning and structural health monitoring overview with emerging technology and high-dimensional data source highlights

Domain-adapted Gaussian mixture models for population-based structural health monitoring

Population-Based Structural Health Monitoring

Contact Info

Product

Resources

About