A review of the state-of-the-art of wireless SHM systems and an experimental set-up towards an improved design

Pentaris, Fragkiskos P.; Stonham, John; Makris, J. P.

doi:10.1109/eurocon.2013.6624997

Cited by 17 publications

(17 citation statements)

References 35 publications

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“…The combinations are [ (1,2,3,4,5,6,7,8,9) and (2,3,4,5,6,7,8,9)] . But since the second combination satisfies the objective function, which is minimizing the number of sensors, then it is chosen to be the optimal solution.…”

Section: Results Of Implementing Brute Force On a 9-sensors Story Buimentioning

confidence: 99%

“…This is shown when = 40. In Table 13, and Figure 11, one can see that depending on 10 different runs, different solutions with different number of sensors resulted, where the first combination (2,3,5,6,8,9) show the optimized solution of placing six sensors out of nine, but the other two combinations (3,4,5,6,7,8,9) and (…”

Section: Results Of Implementing Ga On a Nine-sensors Story Buildingmentioning

confidence: 99%

“…Maximum transmission range 30 m 5,6 Number of bits per packet 2Kb 5,7,8 Α The path loss exponent 2 to 6 5,6 Initial energy at a sensor node 1500mAhr 5,6 The reception energy cost 50nJ/bit 5,6 The transmission energy cost 50nJ/bit 5,6 The power amplifier energy cost 10 pJ/bit/m 2 5,6 Platform specifications:…”

Section: Parameter Description Valuementioning

confidence: 99%

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Minimising number of sensors in wireless sensor networks for structure health monitoring systems

El-Qawasma

Elfouly

Ahmed

2019

IET wirel. sens. syst.

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Nowadays, wireless sensor networks (WSNs) are considered an essential candidate to apply structural health monitoring (SHM). An important problem in this area is sensor placement optimization. In many research works, solving this problem focuses only on the network properties and requirements such as energy consumption, network coverage, …etc., without considering the civil engineering requirements. However, there are other research works that consider network and civil requirements while optimizing the sensor placement. Unfortunately, although minimizing the number of sensors is important, it has never been addressed. This could be noticed from the limited literature found that addresses this problem while considering both the civil and the network requirements. As a result, in this thesis we study the problem of minimizing the number of sensors for SHM in WSNs. The idea behind this research is to reduce the network size, which can solve some problems such as the scalability, installation time and cost. Our contribution in this work is not limited to the mathematical model of the mentioned problem, but will extend to solve the problem using different methods: the exhaustive search, genetic algorithm (GA), and a heuristic algorithm that applies the binary search. The problem is then solved for different number of sensors as well as different placements in many conducted experiments. Finally, the time complexity is evaluated to compare between all the applied methods. The obtained results showed that minimizing the number of sensors becomes more significant with big structures. Furthermore, the binary search algorithm is the best to use to solve the problem for small buildings. But, For larger buildings, there is a trade-off between the performance, and time complexity, where

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Section: Results Of Implementing Brute Force On a 9-sensors Story Buimentioning

confidence: 99%

Section: Results Of Implementing Ga On a Nine-sensors Story Buildingmentioning

confidence: 99%

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Minimising number of sensors in wireless sensor networks for structure health monitoring systems

El-Qawasma

Elfouly

Ahmed

2019

IET wirel. sens. syst.

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“…By repeating the experiments multiple times, the average time delay is 98μs, which meets the 1ms minimum requirement of SHM, as suggested by [53]. For applications require higher accuracy, the present synchronization algorithm can be modified to the ones in [54] and [55].…”

Section: ) Sychronization Performancementioning

confidence: 89%

SnowFort: An Open Source Wireless Sensor Network for Data Analytics in Infrastructure and Environmental Monitoring

et al. 2014

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SnowFort, an open source wireless sensor network (WSN) for data analytics, is proposed for monitoring infrastructure and environment. The wireless sensing unit is optimized to be low power for extremely long-term deployments. Several features, such as data compression and online reconfiguration, are introduced to further reduce power consumption. A low-power WSN over optimized time division multiple access scheme is designed to be scalable and reliable for a network with hundreds of sensors. Real-time data visualization and analytical tools are provided with a representational state transfer (RESTful) application programming interface. We utilize SnowFort to develop a real-time damage detection application in structural health monitoring. We develop a distributed algorithm robust to data loss and validate it in a laboratory setup.

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“…Figure 4 presents a map of the HVSR frequency for the large area of the city of Chania. Case study buildings are located in the geographical region where recorded HVSR frequency is in the range of 0.49-0.69 Hz (in the red square of The instrumentation that is used in this work is from the wired structural health monitoring (SHM) system which is deployed in the Technological Educational Institute of Crete, Chania, with accelerometers of high sensitivity, sample rate at 125 Hz, and configured in triggered mode (Pentaris et al, 2013).…”

Section: Case Study Infrastructuresmentioning

confidence: 99%

A novel horizontal to vertical spectral ratio approach in a wired structural health monitoring system

Pentaris

2014

J. Sens. Sens. Syst.

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Abstract. This work studies the effect ambient seismic noise can have on building constructions, in comparison with the traditional study of strong seismic motion in buildings, for the purpose of structural health monitoring. Traditionally, engineers have observed the effect of earthquakes on buildings by usage of seismometers at various levels. A new approach is proposed in which acceleration recordings of ambient seismic noise are used and horizontal to vertical spectra ratio (HVSR) process is applied, in order to determine the resonance frequency of movement due to excitation of the building from a strong seismic event. The HVSR technique is widely used by geophysicists to study the resonance frequency of sediments over bedrock, while its usage inside buildings is limited. This study applies the recordings inside two university buildings attached to each other, but with different construction materials and different years of construction. Also there is HVSR application in another much older building, with visible cracks in its structure. Sensors have been installed on every floor of the two university buildings, and recordings have been acquired both of ambient seismic noise and earthquakes. Resonance frequencies for every floor of every building are calculated, from both noise and earthquake records, using the HVSR technique for the ambient noise data and the receiver function (RF) for the earthquake data. Differential acceleration drift for every building is also calculated, and there is correlation with the vulnerability of the buildings. Results indicate that HVSR process on acceleration data proves to be an easy, fast, economical method for estimation of fundamental frequency of structures as well as an assessment method for building vulnerability estimation. Comparison between HVSR and RF technique shows an agreement at the change of resonance frequency as we move to higher floors.

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A review of the state-of-the-art of wireless SHM systems and an experimental set-up towards an improved design

Cited by 17 publications

References 35 publications

Minimising number of sensors in wireless sensor networks for structure health monitoring systems

Minimising number of sensors in wireless sensor networks for structure health monitoring systems

SnowFort: An Open Source Wireless Sensor Network for Data Analytics in Infrastructure and Environmental Monitoring

A novel horizontal to vertical spectral ratio approach in a wired structural health monitoring system

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