Machine Learning-Assisted probabilistic fatigue evaluation of Rib-to-Deck joints in orthotropic steel decks
• Probabilistic fatigue crack growth (PFCG) model is proposed for rib-to-deck joint. • Gaussian process regression is employed to incorporate the numerical simulation. • Dynamic Bayesian network is adapted to calibrate the PFCG model with test data. • Numerical S-N curve is derived and recommended f...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Heng, Junlin [verfasserIn] |
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| Format: |
E-Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2022 |
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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.] |
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| Übergeordnetes Werk: |
volume:265 ; year:2022 ; day:15 ; month:08 ; pages:0 |
| Links: |
|---|
| DOI / URN: |
10.1016/j.engstruct.2022.114496 |
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ELV058217541 |
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| 520 | |a • Probabilistic fatigue crack growth (PFCG) model is proposed for rib-to-deck joint. • Gaussian process regression is employed to incorporate the numerical simulation. • Dynamic Bayesian network is adapted to calibrate the PFCG model with test data. • Numerical S-N curve is derived and recommended for rib-to-deck joints in steel decks. | ||
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• Probabilistic fatigue crack growth (PFCG) model is proposed for rib-to-deck joint. • Gaussian process regression is employed to incorporate the numerical simulation. • Dynamic Bayesian network is adapted to calibrate the PFCG model with test data. • Numerical S-N curve is derived and recommended for rib-to-deck joints in steel decks. |
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• Probabilistic fatigue crack growth (PFCG) model is proposed for rib-to-deck joint. • Gaussian process regression is employed to incorporate the numerical simulation. • Dynamic Bayesian network is adapted to calibrate the PFCG model with test data. • Numerical S-N curve is derived and recommended for rib-to-deck joints in steel decks. |
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• Probabilistic fatigue crack growth (PFCG) model is proposed for rib-to-deck joint. • Gaussian process regression is employed to incorporate the numerical simulation. • Dynamic Bayesian network is adapted to calibrate the PFCG model with test data. • Numerical S-N curve is derived and recommended for rib-to-deck joints in steel decks. |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">ELV058217541</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230625004609.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">220808s2022 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.engstruct.2022.114496</subfield><subfield code="2">doi</subfield></datafield><datafield tag="028" ind1="5" ind2="2"><subfield code="a">/cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001951.pica</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)ELV058217541</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)S0141-0296(22)00604-6</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">330</subfield><subfield code="a">600</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">85.15</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Heng, Junlin</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Machine Learning-Assisted probabilistic fatigue evaluation of Rib-to-Deck joints in orthotropic steel decks</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2022</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zzz</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">z</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zu</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">• Probabilistic fatigue crack growth (PFCG) model is proposed for rib-to-deck joint. • Gaussian process regression is employed to incorporate the numerical simulation. • Dynamic Bayesian network is adapted to calibrate the PFCG model with test data. • Numerical S-N curve is derived and recommended for rib-to-deck joints in steel decks.</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Orthotropic steel decks</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Rib-to-deck joints</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Probabilistic fatigue assessment</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">dynamic Bayesian network</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Gaussian process regression</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Zheng, Kaifeng</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Feng, Xiaoyang</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Veljkovic, Milan</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Zhou, Zhixiang</subfield><subfield code="4">oth</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="n">Elsevier Science</subfield><subfield code="a">Mariani, Marcello M. 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