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Structural Control and Health Monitoring - v. 26, n. 2 (janvier 2019)

Publié:	janvier 2019

Articles dans ce numéro

Auteur(s)	Titre	Page(s)
Cao, Shancheng / Ouyang, Huajiang / Cheng, Li	Baseline-free multidamage identification in plate-like structures by using multiscale approach and low-rank modelling	e2293
Eftekhar Azam, Saeed / Rageh, Ahmed / Linzell, Daniel	Damage detection in structural systems utilizing artificial neural networks and proper orthogonal decomposition	e2288
Lin, Chaoning / Li, Tongchun / Liu, Xiaoqing / Zhao, Lanhao / Chen, Siyu / Qi, Huijun	A deformation separation method for gravity dam body and foundation based on the observed displacements	e2304
Vidya Sagar, R. / Kumar, Gyaneshwar / Prasad, Gaurav / Suarez, Elisabet / Gallego, Antolino	Determination of yielding point by means a probabilistic method on acoustic emission signals for application to health monitoring of reinforced concrete structures	e2305
Giardina, Giorgia / Milillo, Pietro / DeJong, Matthew J. / Perissin, Daniele / Milillo, Giovanni	Evaluation of InSAR monitoring data for post‐tunnelling settlement damage assessment	e2285
Fu, Bo / Jiang, Huanjun / Wu, Tao	Experimental study of seismic response reduction effects of particle damper using substructure shake table testing method	e2295
Makki Alamdari, Mehrisadat / Anaissi, Ali / Khoa, Nguyen L. D. / Mustapha, Samir	Frequency domain decomposition-based multisensor data fusion for assessment of progressive damage in structures	e2299
Xu, Mingqiang / Wang, Shuqing / Jiang, Yufeng	Iterative two-stage approach for identifying structural damage by combining the modal strain energy decomposition method with the multiobjective particle swarm optimization algorithm	e2301
Jia, Ziguang / Ren, Liang / Li, Hongnan / Jiang, Tao / Wu, Wenlin	Pipeline leakage identification and localization based on the fiber Bragg grating hoop strain measurements and particle swarm optimization and support vector machine	e2290
Khodabandehlou, Hamid / Pekcan, Gokhan / Sami Fadali, M.	Vibration‐based structural condition assessment using convolution neural networks	e2308

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sur cette fiche
IssueID
26930
Publié(e) le:
20.01.2019
Modifié(e) le:
30.06.2019

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