Prediction of Static Modulus and Compressive Strength of Concrete from Dynamic Modulus Associated with Wave Velocity and Resonance Frequency Using Machine Learning Techniques.

P-wave S-wave compressive strength concrete dynamic elastic modulus machine learning nondestructive method resonance frequency test static elastic modulus

Journal

Materials (Basel, Switzerland)
ISSN: 1996-1944
Titre abrégé: Materials (Basel)
Pays: Switzerland
ID NLM: 101555929

Informations de publication

Date de publication:
27 Jun 2020
Historique:
received: 27 05 2020
revised: 19 06 2020
accepted: 24 06 2020
entrez: 2 7 2020
pubmed: 2 7 2020
medline: 2 7 2020
Statut: epublish

Résumé

The static elastic modulus (

Identifiants

pubmed: 32605042
pii: ma13132886
doi: 10.3390/ma13132886
pmc: PMC7372401
pii:
doi:

Types de publication

Journal Article

Langues

eng

Subventions

Organisme : Incheon National University
ID : 0

Références

Materials (Basel). 2015 Oct 22;8(10):7169-7178
pubmed: 28793627
Materials (Basel). 2018 Mar 22;11(4):
pubmed: 29565830
Materials (Basel). 2019 Nov 10;12(22):
pubmed: 31717660

Auteurs

Jong Yil Park (JY)

Department of Safety Engineering, Seoul National University of Science and Technology, Seoul 01811, Korea.

Sung-Han Sim (SH)

School of Civil, Architectural Engineering and Landscape Architecture, Sungkyunkwan University, Suwon 16419, Korea.

Young Geun Yoon (YG)

Department of Safety Engineering, Incheon National University, Incheon 22012, Korea.

Tae Keun Oh (TK)

Department of Safety Engineering, Incheon National University, Incheon 22012, Korea.
Research Institute for Engineering and Technology, Incheon National University, Incheon 22012, Korea.

Classifications MeSH