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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.
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.