Zobrazeno 1 - 6
of 6
pro vyhledávání: '"Vahid Reza Gharehbaghi"'
Autor:
Vahid Reza Gharehbaghi, Hashem Kalbkhani, Ehsan Noroozinejad Farsangi, T.Y. Yang, Andy Nguyen, Seyedali Mirjalili, Christian Málaga-Chuquitaype
Publikováno v:
Journal of Structural Integrity and Maintenance. 7:136-150
In this paper, a novel deterioration and damage identification procedure (DIP) is presented and applied to building models. The challenge associated with applications on these types of structures is related to the strong correlation of responses, whi
Autor:
Vahid Reza Gharehbaghi, Hashem Kalbkhani, Ehsan Noroozinejad Farsangi, T. Y. Yang, Seyedali Mirjalili
Publikováno v:
Engineering with Computers. 39:2017-2034
In this article, an original data-driven approach is proposed to detect both linear and nonlinear damage in structures using output-only responses. The method deploys variational mode decomposition (VMD) and a generalised autoregressive conditional h
Autor:
Christian Málaga-Chuquitaype, Paolo Gardoni, Seyedali Mirjalili, Mohammad Noori, Shaofan Li, Andy Nguyen, Vahid Reza Gharehbaghi, Tony Yang, Ehsan Noroozinejad Farsangi
Publikováno v:
Archives of Computational Methods in Engineering. 29:2209-2235
The benefits of tracking, identifying, measuring features of interest from structure responses have endless applications for saving cost, time and improving safety. To date, structural health monitoring (SHM) has been extensively applied in several f
Publikováno v:
Structures. 29:458-470
Identifying structural defects in complex structures is one of the main objectives in real-world structural health monitoring (SHM) applications. In this article, a signal-based supervised methodology is proposed for detecting deterioration and damag
Publikováno v:
Journal of Building Engineering. 45:103682
Steel plate shear walls with beam-connected web plates (B-SPSW) are considered effective configurations in alleviating the tension field effects on vertical boundary elements through the detachment of web plates from them. It is critical to establish
Publikováno v:
Journal of Building Engineering. 30:101292
In this paper, a supervised learning approach is introduced for detecting both damage and deterioration in two building models under ambient and forced vibrations. The coefficients and residuals of autoregressive (AR) time-series models are utilized