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Interpretable Machine Learning Models for Punching Shear Strength Estimation of FRP Reinforced Concr...

Interpretable Machine Learning Models for Punching Shear Strength Estimation of FRP Reinforced Concr...

https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_9bb3f858a72f4458abda4aa4d7660c24

Interpretable Machine Learning Models for Punching Shear Strength Estimation of FRP Reinforced Concrete Slabs

About this item

Full title

Interpretable Machine Learning Models for Punching Shear Strength Estimation of FRP Reinforced Concrete Slabs

Publisher

Basel: MDPI AG

Journal title

Crystals (Basel), 2022-02, Vol.12 (2), p.259

Language

English

Formats

Publication information

Publisher

Basel: MDPI AG

More information

Scope and Contents

Contents

Fiber reinforced polymer (FRP) serves as a prospective alternative to reinforcement in concrete slabs. However, similarly to traditional reinforced concrete slabs, FRP reinforced concrete slabs are susceptible to punching shear failure. Accounts of the insufficient consideration of impact factors, existing empirical models and design provisions for...

Alternative Titles

Full title

Interpretable Machine Learning Models for Punching Shear Strength Estimation of FRP Reinforced Concrete Slabs

Authors, Artists and Contributors

Identifiers

Primary Identifiers

Record Identifier

TN_cdi_doaj_primary_oai_doaj_org_article_9bb3f858a72f4458abda4aa4d7660c24

Permalink

https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_9bb3f858a72f4458abda4aa4d7660c24

Other Identifiers

ISSN

2073-4352

E-ISSN

2073-4352

DOI

10.3390/cryst12020259

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