DC FieldValueLanguage
dc.contributor.authorLuckey, Daniel-
dc.contributor.authorFritz, Henrieke-
dc.contributor.authorLegatiuk, Dmitrii-
dc.contributor.authorPeralta Abadia, Jose-
dc.contributor.authorWalther, Christian-
dc.contributor.authorSmarsly, Kay-
dc.date.accessioned2021-11-08T09:21:36Z-
dc.date.available2021-11-08T09:21:36Z-
dc.date.issued2022-
dc.identifier.citationStructural Integrity 21: 331-346 (2022)de_DE
dc.identifier.issn2522-560Xde_DE
dc.identifier.urihttp://hdl.handle.net/11420/10806-
dc.description.abstractIn recent years, structural health monitoring (SHM) applications have significantly been enhanced, driven by advancements in artificial intelligence (AI) and machine learning (ML), a subcategory of AI. Although ML algorithms allow detecting patterns and features in sensor data that would otherwise remain undetected, the generally opaque inner processes and black-box character of ML algorithms are limiting the application of ML to SHM. Incomprehensible decision-making processes often result in doubts and mistrust in ML algorithms, expressed by engineers and stakeholders. In an attempt to increase trust in ML algorithms, explainable artificial intelligence (XAI) aims to provide explanations of decisions made by black-box ML algorithms. However, there is a lack of XAI approaches that meet all requirements of SHM applications. This chapter provides a review of ML and XAI approaches relevant to SHM and proposes a conceptual XAI framework pertinent to SHM applications. First, ML algorithms relevant to SHM are categorized. Next, XAI approaches, such as transparent models and model-specific explanations, are presented and categorized to identify XAI approaches appropriate for being implemented in SHM applications. Finally, based on the categorization of ML algorithms and the presentation of XAI approaches, the conceptual XAI framework is introduced. It is expected that the proposed conceptual XAI framework will provide a basis for improving ML acceptance and transparency and therefore increase trust in ML algorithms implemented in SHM applications.en
dc.language.isoende_DE
dc.relation.ispartofStructural integrityde_DE
dc.subjectArtificial intelligence (AI)de_DE
dc.subjectExplainable artificial intelligence (XAI)de_DE
dc.subjectMachine learning (ML)de_DE
dc.subjectStructural health monitoring (SHM)de_DE
dc.titleExplainable Artificial Intelligence to Advance Structural Health Monitoringde_DE
dc.typeArticlede_DE
dc.type.diniarticle-
dcterms.DCMITypeText-
tuhh.abstract.englishIn recent years, structural health monitoring (SHM) applications have significantly been enhanced, driven by advancements in artificial intelligence (AI) and machine learning (ML), a subcategory of AI. Although ML algorithms allow detecting patterns and features in sensor data that would otherwise remain undetected, the generally opaque inner processes and black-box character of ML algorithms are limiting the application of ML to SHM. Incomprehensible decision-making processes often result in doubts and mistrust in ML algorithms, expressed by engineers and stakeholders. In an attempt to increase trust in ML algorithms, explainable artificial intelligence (XAI) aims to provide explanations of decisions made by black-box ML algorithms. However, there is a lack of XAI approaches that meet all requirements of SHM applications. This chapter provides a review of ML and XAI approaches relevant to SHM and proposes a conceptual XAI framework pertinent to SHM applications. First, ML algorithms relevant to SHM are categorized. Next, XAI approaches, such as transparent models and model-specific explanations, are presented and categorized to identify XAI approaches appropriate for being implemented in SHM applications. Finally, based on the categorization of ML algorithms and the presentation of XAI approaches, the conceptual XAI framework is introduced. It is expected that the proposed conceptual XAI framework will provide a basis for improving ML acceptance and transparency and therefore increase trust in ML algorithms implemented in SHM applications.de_DE
tuhh.publisher.doi10.1007/978-3-030-81716-9_16-
tuhh.publication.instituteDigitales und autonomes Bauen B-1de_DE
tuhh.type.opus(wissenschaftlicher) Artikel-
dc.type.driverarticle-
dc.type.casraiJournal Article-
tuhh.container.volume21de_DE
tuhh.container.startpage331de_DE
tuhh.container.endpage346de_DE
dc.relation.projectBIM-basierte Informationsmodellierung zur semantischen Abbildung intelligenter Bauwerksmonitoringsystemede_DE
dc.relation.projectDatengestützte Analysemodelle für schlanke Bauwerke unter Nutzung von Explainable Artificial Intelligencede_DE
dc.relation.projectFehlertolerantes, drahtloses Bauwerksmonitoring basierend auf Frameanalyse und Deep Learningde_DE
dc.relation.projectSemi-probabilistische, sensorbasierte Bemessungs- und Entwurfskonzepte für intelligente Bauwerkede_DE
dc.identifier.scopus2-s2.0-85117883229de_DE
datacite.resourceTypeArticle-
datacite.resourceTypeGeneralJournalArticle-
item.mappedtypeArticle-
item.openairetypeArticle-
item.languageiso639-1en-
item.grantfulltextnone-
item.cerifentitytypePublications-
item.creatorOrcidLuckey, Daniel-
item.creatorOrcidFritz, Henrieke-
item.creatorOrcidLegatiuk, Dmitrii-
item.creatorOrcidPeralta Abadia, Jose-
item.creatorOrcidWalther, Christian-
item.creatorOrcidSmarsly, Kay-
item.creatorGNDLuckey, Daniel-
item.creatorGNDFritz, Henrieke-
item.creatorGNDLegatiuk, Dmitrii-
item.creatorGNDPeralta Abadia, Jose-
item.creatorGNDWalther, Christian-
item.creatorGNDSmarsly, Kay-
item.fulltextNo Fulltext-
item.openairecristypehttp://purl.org/coar/resource_type/c_6501-
crisitem.project.funderDeutsche Forschungsgemeinschaft (DFG)-
crisitem.project.funderDeutsche Forschungsgemeinschaft (DFG)-
crisitem.project.funderDeutsche Forschungsgemeinschaft (DFG)-
crisitem.project.funderDeutsche Forschungsgemeinschaft (DFG)-
crisitem.project.funderid501100001659-
crisitem.project.funderid501100001659-
crisitem.project.funderid501100001659-
crisitem.project.funderid501100001659-
crisitem.project.funderrorid018mejw64-
crisitem.project.funderrorid018mejw64-
crisitem.project.funderrorid018mejw64-
crisitem.project.funderrorid018mejw64-
crisitem.project.grantnoSM 281/12-1-
crisitem.project.grantnoSM 281/14-1-
crisitem.project.grantnoSM 281/15-1-
crisitem.project.grantnoSM 281/9-1-
crisitem.author.deptDigitales und autonomes Bauen B-1-
crisitem.author.deptDigitales und autonomes Bauen B-1-
crisitem.author.orcid0000-0002-0028-5793-
crisitem.author.orcid0000-0003-0261-6792-
crisitem.author.orcid0000-0001-7228-3503-
crisitem.author.parentorgStudiendekanat Bauwesen (B)-
crisitem.author.parentorgStudiendekanat Bauwesen (B)-
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