Rapid seismic damage evaluation of bridge portfolios using machine learning techniques
• Rapid damage assessment of bridges in the transportation networks. • An easy-to-implement machine learning based tagging procedure. • Comparison of various machine learning approaches for damage assessment. • Identification of the optimal machine learning procedure for tagging the bridge systems....
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Mangalathu, Sujith [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2019 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Artificial intelligence in innovation research: A systematic review, conceptual framework, and future research directions - Mariani, Marcello M. ELSEVIER, 2022, the journal of earthquake, wind and ocean engineering, Amsterdam [u.a.] |
|---|---|
| Übergeordnetes Werk: |
volume:201 ; year:2019 ; day:15 ; month:12 ; pages:0 |
| Links: |
|---|
| DOI / URN: |
10.1016/j.engstruct.2019.109785 |
|---|
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ELV048485977 |
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• Rapid damage assessment of bridges in the transportation networks. • An easy-to-implement machine learning based tagging procedure. • Comparison of various machine learning approaches for damage assessment. • Identification of the optimal machine learning procedure for tagging the bridge systems. |
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