Machine learning and structural health monitoring overview with emerging technology and high-dimensional data source highlights
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 ap...
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2021
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| Online Access: | http://hdl.handle.net/11073/23876 |
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| _version_ | 1864513440762036224 |
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| author | Malekloo, Arman |
| author2 | Ozer, Ekin AlHamaydeh, Mohammad Girolami, Mark |
| author2_role | author author author |
| author_facet | Malekloo, Arman Ozer, Ekin AlHamaydeh, Mohammad Girolami, Mark |
| author_role | author |
| dc.creator.none.fl_str_mv | Malekloo, Arman Ozer, Ekin AlHamaydeh, Mohammad Girolami, Mark |
| dc.date.none.fl_str_mv | 2021 2022-05-24T07:01:37Z 2022-05-24T07:01:37Z |
| dc.format.none.fl_str_mv | application/pdf |
| dc.identifier.none.fl_str_mv | Malekloo, A., Ozer, E., AlHamaydeh, M., & Girolami, M. (2021). Machine learning and structural health monitoring overview with emerging technology and high-dimensional data source highlights. Structural Health Monitoring. https://doi.org/10.1177/14759217211036880 1741-3168 http://hdl.handle.net/11073/23876 10.1177/14759217211036880 |
| dc.language.none.fl_str_mv | en_US |
| dc.publisher.none.fl_str_mv | Sage Publishing |
| dc.relation.none.fl_str_mv | https://doi.org/10.1177/14759217211036880 |
| dc.subject.none.fl_str_mv | Structural health monitoring Machine learning Internet of things Big data Emerging technologies |
| dc.title.none.fl_str_mv | Machine learning and structural health monitoring overview with emerging technology and high-dimensional data source highlights |
| dc.type.none.fl_str_mv | Peer-Reviewed Published version info:eu-repo/semantics/publishedVersion info:eu-repo/semantics/article |
| description | 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. |
| format | article |
| id | aus_4256d026ba5c1168ddce6c7eafea2968 |
| identifier_str_mv | Malekloo, A., Ozer, E., AlHamaydeh, M., & Girolami, M. (2021). Machine learning and structural health monitoring overview with emerging technology and high-dimensional data source highlights. Structural Health Monitoring. https://doi.org/10.1177/14759217211036880 1741-3168 10.1177/14759217211036880 |
| language_invalid_str_mv | en_US |
| network_acronym_str | aus |
| network_name_str | aus |
| oai_identifier_str | oai:repository.aus.edu:11073/23876 |
| publishDate | 2021 |
| publisher.none.fl_str_mv | Sage Publishing |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| spelling | Machine learning and structural health monitoring overview with emerging technology and high-dimensional data source highlightsMalekloo, ArmanOzer, EkinAlHamaydeh, MohammadGirolami, MarkStructural health monitoringMachine learningInternet of thingsBig dataEmerging technologiesConventional 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.Horizon 2020 Project TURNkeyAmerican University of SharjahSage Publishing2022-05-24T07:01:37Z2022-05-24T07:01:37Z2021Peer-ReviewedPublished versioninfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfMalekloo, A., Ozer, E., AlHamaydeh, M., & Girolami, M. (2021). Machine learning and structural health monitoring overview with emerging technology and high-dimensional data source highlights. Structural Health Monitoring. https://doi.org/10.1177/147592172110368801741-3168http://hdl.handle.net/11073/2387610.1177/14759217211036880en_UShttps://doi.org/10.1177/14759217211036880oai:repository.aus.edu:11073/238762024-08-22T12:06:35Z |
| spellingShingle | Machine learning and structural health monitoring overview with emerging technology and high-dimensional data source highlights Malekloo, Arman Structural health monitoring Machine learning Internet of things Big data Emerging technologies |
| status_str | publishedVersion |
| title | Machine learning and structural health monitoring overview with emerging technology and high-dimensional data source highlights |
| title_full | Machine learning and structural health monitoring overview with emerging technology and high-dimensional data source highlights |
| title_fullStr | Machine learning and structural health monitoring overview with emerging technology and high-dimensional data source highlights |
| title_full_unstemmed | Machine learning and structural health monitoring overview with emerging technology and high-dimensional data source highlights |
| title_short | Machine learning and structural health monitoring overview with emerging technology and high-dimensional data source highlights |
| title_sort | Machine learning and structural health monitoring overview with emerging technology and high-dimensional data source highlights |
| topic | Structural health monitoring Machine learning Internet of things Big data Emerging technologies |
| url | http://hdl.handle.net/11073/23876 |