Sensor technology innovation for the advancement of structural health monitoring: a strategic program of US-China research for the next decade

Glaser, Steven D.; Li, Hui; Wang, Ming L.; Ou, Jinping; Lynch, Jerome

doi:10.12989/sss.2007.3.2.221

Cited by 90 publications

(49 citation statements)

References 50 publications

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“…Probabilistic seismic risk is the probability of occurrence of a destructive seismic event in a location in a period (Glaser et al, 2007). When investigating the earthquake occurrences in a location, an assumption for probabilistic assessment is a linear relationship between magnitude and frequency that may be obtained as a function of magnitude (Belazougui et al, 2004; Boylu, 2005;Glaser et al, 2007;Lia et al, 2014). Recent earthquake data were taken into account to determine the probabilistic seismic hazard between 1973 and 2016.…”

Section: Risk Mitigation and Controlmentioning

confidence: 99%

Structural Risk Assessment and Mitigation for Low- to Mid-Rise Residential Buildings in China

Korkmaz

2017

Front. Built Environ.

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Section: Risk Mitigation and Controlmentioning

confidence: 99%

Structural Risk Assessment and Mitigation for Low- to Mid-Rise Residential Buildings in China

Korkmaz

2017

Front. Built Environ.

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“…In substructure 1, S 1 =[1-3], and substructure 2, S 2 = [3][4][5][6][7][8][9], no force is applied, whereas in substructure 3, S 3 =[9-10], a dynamic force is applied at level 10. In this example, the substructures have overlapping levels, providing an alternative to the non-overlap version of substructural identification used in the previous two examples.…”

Section: Identification With Complete Measurementsmentioning

confidence: 99%

An improved substructural identification strategy for large structural systems

Trinh

Koh

2011

Struct. Control Health Monit.

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To identify physical parameters of a large structural system, the computational challenges in dealing with a large number of unknowns are formidable. A divide-and-conquer approach is often required to partition the structural system into many substructures, each with much lesser unknowns for more accurate and efficient identification. Furthermore, in view of the ill-conditioned nature of inverse analysis, it is highly beneficial to adopt non-gradient based search methods such as genetic algorithm (GA). To this end, this paper presents a GA-based substructural identification strategy for large structural systems. As compared to some recent work on substructural identification, the proposed strategy presents two significant improvements: (a) the use of acceleration measurements to directly account for interaction between substructures without approximation of interface force, and (b) the use of an improved identification method based on multi-feature GA. In numerical simulations, the mass, damping and stiffness parameters of a 100-storey shear building, involving 202 unknowns, are identified with very good accuracy (mean error of less than 3%) based on incomplete acceleration measurements with 10% noise. In addition, an experimental study on a 10-storey small-scale steel frame further validates the superior performance of the proposed strategy over complete structural identification.2

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“…For detailed review of vibration-based damage identification methods, readers are referred to [3] and [7].…”

Section: A Damage Identification Algorithmsmentioning

confidence: 99%

“…The computation unit is able to process raw data and make decisions. Extensive surveys of SHM based on WSNs can be found in [4], [5], [7].…”

Section: B Online Shm Based On Wsnsmentioning

confidence: 99%

Localized Structural Health Monitoring Using Energy-Efficient Wireless Sensor Networks

et al. 2009

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Abstract-This paper presents a localized information processing approach for long-term, online structural health monitoring (SHM) using wireless sensor networks (WSNs). Based on the embedded AR-ARX method, each sensor independently calculates a statistical damage-sensitive coefficient using the measured acceleration data during each monitoring period. A nonlinear programming formulation is developed to identify damage presence, localize damage position, and quantify damage severity from the damage-sensitive coefficients in the whole sensing field. By limiting each sensor to exchange information among its neighboring sensors only, a localized near-optimal algorithm is proposed to reduce communication costs, thus alleviating the channel interference and prolonging the network lifetime. Simulation results on a steel frame structure prove the effectiveness of the proposed algorithm.Index Terms-Localized information processing, structure health monitoring (SHM), wireless sensor networks (WSNs).

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Sensor technology innovation for the advancement of structural health monitoring: a strategic program of US-China research for the next decade

Cited by 90 publications

References 50 publications

Structural Risk Assessment and Mitigation for Low- to Mid-Rise Residential Buildings in China

Structural Risk Assessment and Mitigation for Low- to Mid-Rise Residential Buildings in China

An improved substructural identification strategy for large structural systems

Localized Structural Health Monitoring Using Energy-Efficient Wireless Sensor Networks

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