Risk monitoring of buildings with wireless sensor networks

Kurata, Narito; Spencer, Billie F.; Ruiz-Sandoval, Manuel E.

doi:10.1002/stc.73

Cited by 96 publications

(70 citation statements)

References 2 publications

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“…Traditional monitoring methods, such as metal foil strain gauges or fiber optic sensors, generally require cabled installation of the sensor and associated data acquisition system [2][3][4][5]. To eliminate the high cabling cost for large structures, wireless sensing devices have been developed for digitizing and transmitting sensor data [6,7]. But these devices usually require batteries or energy harvesters for power supply.…”

Section: Introductionmentioning

confidence: 99%

Sensitivity Modeling of a Strain-Sensing Antenna

Chen¹,

Geng²,

Wan³

et al. 2017

PIER C

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Abstract-A quarter-wavelength folded patch antenna is adopted as the passive wireless strain sensor for structural health monitoring (SHM) of bridges. It can be used for continuous surveillance and damage detection. According to theoretical formulations, strain simulation and experiments, it is found that a good linearity relationship can be achieved between normalized resonance frequency shift and the strain both in longitudinal and transverse directions. And the sensing sensitivity in longitudinal strain is better than that in transverse strain. Through conducting tensile experiments, we find that many factors can influence the strain sensitivity. To address this fundamental issue in antenna sensors for strain sensing, a new strain sensitivity experiment is proposed to take the influence of strain transfer ratio change under strain. The linear relationship of strain transfer ratio and deformation is obtained by sensitivity experiment. The corrected sensitivity in longitudinal and transverse strains is calculated based on the linearity. Furthermore, the Possion effect is taken into consideration to explain the opposite effects of experimental and simulated sensitivities in transverse strain.

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Section: Introductionmentioning

confidence: 99%

Sensitivity Modeling of a Strain-Sensing Antenna

Chen¹,

Geng²,

Wan³

et al. 2017

PIER C

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“…One solution would be to use solar panels with a small battery for supplying power, and to use a wireless local area network to enable communication between sensing nodes placed on different buildings. In recent applications, similar approaches have been used to install arrays of sensors on single structures [22][23][24][25]. In dense urban areas, using consumer-grade antennas with a range of 100 m would allow having a redundant communication network across a city.…”

Section: Monitoring Frameworkmentioning

confidence: 99%

Data‐driven post‐earthquake rapid structural safety assessment

Goulet

Michel

Kiureghian

2015

Earthq Engng Struct Dyn

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SummaryEarthquake‐prone cities are exposed to important societal and financial losses. An important part of these losses stems from the inability to use structures as shelters or for generating economic activity after the event of an earthquake. The inability to use structures is not only due to collapse or damage; it is also due to the lack of knowledge about their safety state, which prohibits their normal use. Because a diagnosis is required for thousands of structures, city‐scale safety assessment requires solutions that are economically sustainable and scalable. Data‐driven algorithms supported by sensing technologies have the potential to solve this challenge. Several ambient vibration monitoring studies of buildings, before and after earthquakes, have shown that the extent of damage in a building is correlated with a decrease in the natural frequency. However, the observed worldwide data may not be representative of specific cities due to factors such as construction type, quality, material, and age. In this paper, we propose a framework that is able to progressively learn the relationship between frequency shift and damage state as a small number of buildings in a city are inspected after an earthquake, and to use that information to predict the safety state of uninspected but monitored buildings. The capacity of the proposed framework to learn and perform prognosis is validated by applying the methodology to a city with 1000 buildings having simulated frequency shifts and damage states. Copyright © 2015 John Wiley & Sons, Ltd.

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“…Some research projects, including sensoractuator cooperation services [34], [35], earthquake monitoring [10], [36], context-aware services [37], [38], battery-less sensor networks [39], and a collision detection mechanism [40], have used PAVENET OS. At present, the PAVENET OS community is very small compared to the TinyOS community.…”

Section: Programming Complexitymentioning

confidence: 99%

PAVENET OS: A Compact Hard Real-Time Operating System for Precise Sampling in Wireless Sensor Networks

Saruwatari

Morikawa

2012

SICE Journal of Control, Measurement, and System Integration

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: The paper shows a compact hard real-time operating system for wireless sensor nodes called PAVENET OS. PAVENET OS provides hybrid multithreading: preemptive multithreading and cooperative multithreading. Both of the multithreading are optimized for two kinds of tasks on wireless sensor networks, and those are real-time tasks and best-effort ones. PAVENET OS can efficiently perform hard real-time tasks that cannot be performed by TinyOS. The paper demonstrates the hybrid multithreading realizes compactness and low overheads, which are comparable to those of TinyOS, through quantitative evaluation. The evaluation results show PAVENET OS performs 100 Hz sensor sampling with 0.01% jitter while performing wireless communication tasks, whereas optimized TinyOS has 0.62% jitter. In addition, PAVENET OS has a small footprint and low overheads (minimum RAM size: 29 bytes, minimum ROM size: 490 bytes, minimum task switch time: 23 cycles).

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Risk monitoring of buildings with wireless sensor networks

Cited by 96 publications

References 2 publications

Sensitivity Modeling of a Strain-Sensing Antenna

Sensitivity Modeling of a Strain-Sensing Antenna

Data‐driven post‐earthquake rapid structural safety assessment

PAVENET OS: A Compact Hard Real-Time Operating System for Precise Sampling in Wireless Sensor Networks

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