Assessing uncertainty in image-based monitoring: addressing false positives, false negatives, and base rate bias in structural health evaluation
<p dir="ltr">This study explores the limitations of image-based structural health monitoring (SHM) techniques in detecting structural damage. Leveraging machine learning and computer vision, image-based SHM offers a scalable and efficient alternative to manual inspections. However, i...
محفوظ في:
| المؤلف الرئيسي: | |
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| منشور في: |
2025
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| الموضوعات: | |
| الوسوم: |
إضافة وسم
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| _version_ | 1864513538601517056 |
|---|---|
| author | Vagelis Plevris (14158863) |
| author_facet | Vagelis Plevris (14158863) |
| author_role | author |
| dc.creator.none.fl_str_mv | Vagelis Plevris (14158863) |
| dc.date.none.fl_str_mv | 2025-01-26T09:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1007/s00477-024-02898-7 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Assessing_uncertainty_in_image-based_monitoring_addressing_false_positives_false_negatives_and_base_rate_bias_in_structural_health_evaluation/30197968 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Engineering Civil engineering Information and computing sciences Artificial intelligence Machine learning Image-based damage detection Structural health monitoring (SHM) Risk-based decision making Structural safety Base rate fallacy |
| dc.title.none.fl_str_mv | Assessing uncertainty in image-based monitoring: addressing false positives, false negatives, and base rate bias in structural health evaluation |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">This study explores the limitations of image-based structural health monitoring (SHM) techniques in detecting structural damage. Leveraging machine learning and computer vision, image-based SHM offers a scalable and efficient alternative to manual inspections. However, its reliability is impacted by challenges such as false positives, false negatives, and environmental variability, particularly in low base rate damage scenarios. The Base Rate Bias plays a significant role, as low probabilities of actual damage often lead to misinterpretation of positive results. This study uses both Bayesian analysis and a frequentist approach to evaluate the precision of damage detection systems, revealing that even highly accurate models can yield misleading results when the occurrence of damage is rare. Strategies for mitigating these limitations are discussed, including hybrid systems that combine multiple data sources, human-in-the-loop approaches for critical assessments, and improving the quality of training data. These findings provide essential insights into the practical applicability of image-based SHM techniques, highlighting both their potential and their limitations for real-world infrastructure monitoring.</p><h2>Other Information</h2><p dir="ltr">Published in: Stochastic Environmental Research and Risk Assessment<br>License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1007/s00477-024-02898-7" target="_blank">https://dx.doi.org/10.1007/s00477-024-02898-7</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_8f75a2fd585174f07594d83a98bb98bb |
| identifier_str_mv | 10.1007/s00477-024-02898-7 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/30197968 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Assessing uncertainty in image-based monitoring: addressing false positives, false negatives, and base rate bias in structural health evaluationVagelis Plevris (14158863)EngineeringCivil engineeringInformation and computing sciencesArtificial intelligenceMachine learningImage-based damage detectionStructural health monitoring (SHM)Risk-based decision makingStructural safetyBase rate fallacy<p dir="ltr">This study explores the limitations of image-based structural health monitoring (SHM) techniques in detecting structural damage. Leveraging machine learning and computer vision, image-based SHM offers a scalable and efficient alternative to manual inspections. However, its reliability is impacted by challenges such as false positives, false negatives, and environmental variability, particularly in low base rate damage scenarios. The Base Rate Bias plays a significant role, as low probabilities of actual damage often lead to misinterpretation of positive results. This study uses both Bayesian analysis and a frequentist approach to evaluate the precision of damage detection systems, revealing that even highly accurate models can yield misleading results when the occurrence of damage is rare. Strategies for mitigating these limitations are discussed, including hybrid systems that combine multiple data sources, human-in-the-loop approaches for critical assessments, and improving the quality of training data. These findings provide essential insights into the practical applicability of image-based SHM techniques, highlighting both their potential and their limitations for real-world infrastructure monitoring.</p><h2>Other Information</h2><p dir="ltr">Published in: Stochastic Environmental Research and Risk Assessment<br>License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1007/s00477-024-02898-7" target="_blank">https://dx.doi.org/10.1007/s00477-024-02898-7</a></p>2025-01-26T09:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1007/s00477-024-02898-7https://figshare.com/articles/journal_contribution/Assessing_uncertainty_in_image-based_monitoring_addressing_false_positives_false_negatives_and_base_rate_bias_in_structural_health_evaluation/30197968CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/301979682025-01-26T09:00:00Z |
| spellingShingle | Assessing uncertainty in image-based monitoring: addressing false positives, false negatives, and base rate bias in structural health evaluation Vagelis Plevris (14158863) Engineering Civil engineering Information and computing sciences Artificial intelligence Machine learning Image-based damage detection Structural health monitoring (SHM) Risk-based decision making Structural safety Base rate fallacy |
| status_str | publishedVersion |
| title | Assessing uncertainty in image-based monitoring: addressing false positives, false negatives, and base rate bias in structural health evaluation |
| title_full | Assessing uncertainty in image-based monitoring: addressing false positives, false negatives, and base rate bias in structural health evaluation |
| title_fullStr | Assessing uncertainty in image-based monitoring: addressing false positives, false negatives, and base rate bias in structural health evaluation |
| title_full_unstemmed | Assessing uncertainty in image-based monitoring: addressing false positives, false negatives, and base rate bias in structural health evaluation |
| title_short | Assessing uncertainty in image-based monitoring: addressing false positives, false negatives, and base rate bias in structural health evaluation |
| title_sort | Assessing uncertainty in image-based monitoring: addressing false positives, false negatives, and base rate bias in structural health evaluation |
| topic | Engineering Civil engineering Information and computing sciences Artificial intelligence Machine learning Image-based damage detection Structural health monitoring (SHM) Risk-based decision making Structural safety Base rate fallacy |