Optimal sensor placement for structural health monitoring: a comparative study between the control engineering and civil engineering approaches

Movva, Gopichand; Wan, Yan; Fu, Shengli; Wu, Hui

doi:10.1117/12.2009754

Cited by 2 publications

(10 citation statements)

References 16 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%

“…In civil engineering there are some traditional methods for optimizing the sensor placement in SHM that are reviewed in [8], [27], [28], and [29], such as the effective independence (EFI) method, and effective independence driving point residue (EFI-DPR) method . EFI is defined in [30] to be a sensor placement algorithm that starts with all possible sensor positions and reaches the wanted number of locations by gradually removing those that have the minimum contributions to the linearly independent manifestation of the fixture faults.…”

Section: Sensor Placement Based On Civil Engineering and Network Requmentioning

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%

“…If it is larger than , then the solution meets the constraints and objective function, so it is feasible and correct. For instance, when =60, the combination that does not show in implementing the brute force on nine sensors is (2,3,4,5,6,8,9). This solution is feasible because the information quality L equals to 63.451, which is larger than 60.…”

Section: Validationmentioning

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%

Section: Sensor Placement Based On Civil Engineering and Network Requmentioning

confidence: 99%

Section: Parameter Description Valuementioning

confidence: 99%

Section: Validationmentioning

confidence: 99%

See 3 more Smart Citations

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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Optimal Placement of Wireless Sensor Nodes for Bridge Dynamic Monitoring Based on Improved Particle Swarm Algorithm

You

Jin

2013

International Journal of Distributed Sensor Networks

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The issue of optimal placement of wireless sensor networks (WSNs) is one of the major challenges for dynamic monitoring of bridges. It should be solved based on combining the effective monitoring of the dynamic performance of a bridge with the energy consumption of WSNs. Thus, the relationship between the bridge modal and the energy consumption of wireless networks is derived. Optimizing sensor location is achieved by using the improved wavelet particle swarm algorithm, which overcomes the disadvantage of the conventional particle swarm algorithm by applying mutation operator to the selected particles with certain small probability. Based on the comparison between the ANSYS simulation results and the measured bridge data, as well as the energy distribution diagram of WSNs, the proposed method is shown to reflect the dynamic performance of the test bridge well. Moreover, the method effectively controls the energy consumption of WSNs and achieves reasonable optimization effects. Therefore, this method can be used for dynamic monitoring of wireless sensors in various bridge types.

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Optimal sensor placement for structural health monitoring: a comparative study between the control engineering and civil engineering approaches

Cited by 2 publications

References 16 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

Optimal Placement of Wireless Sensor Nodes for Bridge Dynamic Monitoring Based on Improved Particle Swarm Algorithm

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