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RISS 인기검색어
- 검색키워드
- 저자 : Seyed Sina Kourehli (검색결과 6 건)
- 무료
- 기관 내 무료
- 유료
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Kourehli, Seyed Sina Techno-Press 2018 Structural monitoring and maintenance Vol.5 No.3
In this paper, a novel and effective damage diagnosis algorithm is proposed to detect and estimate damage using two stages least squares support vector machine (LS-SVM) and limited number of attached sensors on structures. In the first stage, LS-SVM1 is used to predict the unmeasured mode shapes data based on limited measured modal data and in the second stage, LS-SVM2 is used to predicting the damage location and severity using the complete modal data from the first-stage LS-SVM1. The presented methods are applied to a three story irregular frame and cantilever plate. To investigate the noise effects and modeling errors, two uncertainty levels have been considered. Moreover, the performance of the proposed methods has been verified through using experimental modal data of a mass-stiffness system. The obtained damage identification results show the suitable performance of the proposed damage identification method for structures in spite of different uncertainty levels.
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Crack identification in Timoshenko beam under moving mass using RELM
Seyed Sina Kourehli,Siamak Ghadimi,Reza Ghadimi 국제구조공학회 2018 Steel and Composite Structures, An International J Vol.28 No.3
In this paper, a new method has been proposed to detect crack in beam structures under moving mass using regularized extreme learning machine. For this purpose, frequencies of beam under moving mass used as input to train machine. This data is acquired by the analysis of cracked structure applying the finite element method (FEM). Also, a validation study used for verification of the FEM. To evaluate performance of the presented method, a fixed simply supported beam and two span continuous beam are considered containing single or multi cracks. The obtained results indicated that this method can provide a reliable tool to accurately identify cracks in beam structures under moving mass.
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Structural Damage Detection Using Incomplete Modal Data and Incomplete Static Response
Seyed Sina Kourehli,Abdollah Bagheri,Gholamreza Ghodrati Amiri,Mohsen Ghafory-Ashtiany 대한토목학회 2013 KSCE JOURNAL OF CIVIL ENGINEERING Vol.17 No.1
This paper presents novel approaches to structural damage detection and estimation using incomplete modal data and incomplete static response of a damaged structure. The proposed methods use modal data or static displacement to formulate objective functions. Damage location and severity in structural elements are determined using optimization of the objective functions by the simulated annealing algorithm. The presented methods are applied to a simply supported beam and a three-story plane frame with and without noise in modal data and containing several damages. Moreover, the performance of the proposed methods has been verified through using experimental modal data of a mass-stiffness system. The results indicate that the proposed methods perform quite well using different objective functions in spite of the incomplete data.
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Multiple Crack Identification in Euler Beams using Extreme Learning Machine
Siamak Ghadimi,Seyed Sina Kourehli 대한토목학회 2017 KSCE JOURNAL OF CIVIL ENGINEERING Vol.21 No.1
In this paper, a novel method proposed for multiple crack identification in Euler beams using extreme learning machine (ELM). For this purpose, the extreme learning machine used the modal strain energy and natural frequencies of cracked beam as input and crack states in beam elements as output. To illustrate the performance of the presented method in crack detection, Euler beam with different support conditions consist of cantilever, simply supported and fixed simply supported with single or several cracks in beam elements has been investigated. In other work, a validation study has been done using a simply supported beam. Also, noise effect on the measured modal data has been investigated. The obtained results show the capability of the proposed method for crack detection using ELM.
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Investigation of the accuracy of different finite element model reduction techniques
Ghannadi, Parsa,Kourehli, Seyed Sina Techno-Press 2018 Structural monitoring and maintenance Vol.5 No.3
In this paper, various model reduction methods were assessed using a shear frame, plane and space truss structures. Each of the structures is one-dimensional, two-dimensional and three-dimensional, respectively. Three scenarios of poor, better, and the best were considered for each of the structures in which 25%, 40%, and 60% of the total degrees of freedom (DOFs) were measured in each of them, respectively. Natural frequencies of the full and reduced order structures were compared in each of the numerical examples to assess the performance of model reduction methods. Generally, it was found that system equivalent reduction expansion process (SEREP) provides full accuracy in the model reduction in all of the numerical examples and scenarios. Iterated improved reduced system (IIRS) was the second-best, providing acceptable results and lower error in higher modes in comparison to the improved reduced system (IRS) method. Although the Guyan's method has very low levels of accuracy. Structures were classified with the excitation frequency. High-frequency structures compared to low-frequency structures have been poor performance in the model reduction methods (Guyan, IRS, and IIRS).
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Structural damage detection based on MAC flexibility and frequency using moth-flame algorithm
Parsa Ghannadi,Seyed Sina Kourehli 국제구조공학회 2019 Structural Engineering and Mechanics, An Int'l Jou Vol.70 No.6
Vibration-based structural damage detection through optimization algorithms and minimization of objective function has recently become an interesting research topic. Application of various objective functions as well as optimization algorithms may affect damage diagnosis quality. This paper proposes a new damage identification method using Moth-Flame Optimization (MFO). MFO is a nature-inspired algorithm based on moth’s ability to navigate in dark. Objective function consists of a term with modal assurance criterion flexibility and natural frequency. To show the performance of the said method, two numerical examples including truss and shear frame have been studied. Furthermore, Los Alamos National Laboratory test structure was used for validation purposes. Finite element model for both experimental and numerical examples was created by MATLAB software to extract modal properties of the structure. Mode shapes and natural frequencies were contaminated with noise in above mentioned numerical examples. In the meantime, one of the classical optimization algorithms called particle swarm optimization was compared with MFO. In short, results obtained from numerical and experimental examples showed that the presented method is efficient in damage identification.
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