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Mr Vahidreza Gharehbaghi

Mr Vahidreza Gharehbaghi
Name Vahidreza Gharehbaghi
Position Adjunct Associate Research Fellow
Section School of Engineering
Email
Languages English (non accredited translator)
Homepage https://www.researchgate.net/profile/Vahidreza-Gharehbaghi
The views expressed on staff homepages may not reflect the views of the University.

Vahid focuses his research programs on the fields of structural health monitoring and structural analysis. He has several years of experience in designing and supervising buildings and teaching experience on multiple engineering workshops and symposiums in his professional career. Additionally, he has experience as a reviewer in several journals such as Wiley and ASCE and as a researcher at University of South Queensland.


Research interests
Structural health monitoring, Machine learning, Computer vision, FEM

Professional memberships
Reviewer:
ASCE Natural Hazards (ASCE Journals)
Frontiers in Built Environment (Frontiers Media S.A.)
Journal of Engineering: Wiley Online Library
Journal of Applied Science and Engineering (Tamkang University)
Practice Periodical on Structural Design and Construction (ASCE)
Iranian (Iranica) Journal of Energy & Environment
International Journal of Earthquake and Impact Engineering (Inderscience)

Faculty Member of Smart Structures, Kharazmi University

Industry affiliations
Iran Construction Engineering Organization (IRCEO)

Research most recent
Deterioration and Damage Identification in Building Structures Using A Novel Feature Selection Method, 2021, Structures

A Critical Review On Structural Health Monitoring: Definitions and Methods, 2021, Archives of Computational Methods in Engineering

A Novel Approach for Deterioration and Damage Identification in Building Structures Based on Stockwell-Transform and Deep Convolutional Neural Network, 2021, Journal of Structural Integrity and Maintenance

A Study on the Significance of the Design Parameters of Steel Plate Shear Walls Subjected to Monotonic Loading, 2020, Civil and Environmental Engineering Reports

Influence of image noise on crack detection performance of deep convolutional neural networks, 2021, SHMII-10

Investigation of the effectiveness of crack detection using non-contact measurements and deep learning, 2020, 12th ANSHM Workshop


Research most notable
Supervised damage and deterioration detection in building structures using an enhanced autoregressive time-series approach, 2020, Journal of Building Engineering