Structural Health Monitoring Using Wireless Sensor Networks: A Comprehensive Survey

Noel, Adam B.; Abdaoui, Abderrazak; Elfouly, Tarek; Ahmed, Mohamed H.; Badawy, Ahmed; Shehata, Mohamed

doi:10.1109/comst.2017.2691551

Cited by 380 publications

(195 citation statements)

References 92 publications

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“…Following this Arthur .,et al [12] used Discrete Wavelet Transform along with Artificial Immune System improving the NDE meth-od. In [13], Adam .,et al conducted a survey for comparing various algorithms deployed for SHM along with discussion on the challenges faced in the design of network. In [14], Sarfraz Nawaz .,et al deployed a sensors to monitor the excavation site and discussed the challenges they faced thereby urging the need for improvising the current protocols.…”

Section: Related Workmentioning

confidence: 99%

Structural Health Monitoring using Varied Machine Learning Algorithms

Shanthi¹,

Mahalakshmi²,

Shobana³

2018

IJET

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Structural Health Monitoring is essential in today's world where large amount of money and labour are involved in building a structure. There arises a need to periodically check whether the built structure is strong and flawless, also how long it will be strong and if not how much it is damaged. These information are needed so that the precautions can be made accordingly. Otherwise, it may result in disastrous accidents which may take away even human lives. There are various methods to evaluate a structure. In this paper, we apply various classification algorithms like J48, Naive Bayes and many other classifiers available, to the dataset to check on the accuracy of the prediction determined by all of these classification algorithms and ar-rive at the conclusion of the best possible classifier to say whether a structure is damaged or not.

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Section: Related Workmentioning

confidence: 99%

Structural Health Monitoring using Varied Machine Learning Algorithms

Shanthi¹,

Mahalakshmi²,

Shobana³

2018

IJET

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“…The survey of Noel et al [26] dealt with applications of WSN to Structural Health Monitoring (SHM) which is referred to the monitoring of critical infrastructures in order to expand their lifespan through "early damage detection". This application requires the deployment of numerous sensors throughout a structure for damage localization.…”

Section: Related Workmentioning

confidence: 99%

A Social Environmental Sensor Network Integrated within a Web GIS Platform

Voutos

Mylonas

Spyrou

et al. 2017

JSAN

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Abstract:We live in an era where typical measures towards the mitigation of environmental degradation follow the identification and recording of natural parameters closely associated with it. In addition, current scientific knowledge on the one hand may be applied to minimize the environmental impact of anthropogenic activities, whereas informatics on the other, playing a key role in this ecosystem, do offer new ways of implementing complex scientific processes regarding the collection, aggregation and analysis of data concerning environmental parameters. Furthermore, another related aspect to consider is the fact that almost all relevant data recordings are influenced by their given spatial characteristics. Taking all aforementioned inputs into account, managing such a great amount of complex and remote data requires specific digital structures; these structures are typically deployed over the Web on an attempt to capitalize existing open software platforms and modern developments of hardware technology. In this paper we present an effort to provide a technical solution based on sensing devices that are based on the well-known Arduino platform and operate continuously for gathering and transmitting of environmental state information. Controls, user interface and extensions of the proposed project rely on the Android mobile device platform (both from the software and hardware side). Finally, a crucial novel aspect of our work is the fact that all herein gathered data carry spatial information, which is rather fundamental for the successful correlation between pollutants and their place of origin. The latter is implemented by an interactive Web GIS platform operating oversight in situ and on a timeline basis.

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“…Structural health monitoring (SHM) systems are widely deployed in large civil infrastructures (especially for long‐span bridges and high‐rise buildings) to enable health diagnosis and damage detection so as to improve public safety . Most existing SHM systems were implemented based on wired monitoring networks . In recent years, with the fast development of wireless communication technologies, wireless sensors network (WSN) is emerging to replace wired monitoring technologies, because of the superiority in installation, cost, system expansion, and maintenance .…”

Section: Introductionmentioning

confidence: 99%

“…[1][2][3][4][5][6][7][8][9] Most existing SHM systems were implemented based on wired monitoring networks. 10,11 In recent years, with the fast development of wireless communication technologies, wireless sensors network (WSN) is emerging to replace wired monitoring technologies, because of the superiority in installation, cost, system expansion, and maintenance. [12][13][14] In terms of network topology, the SHM-oriented WSNs typically feature a three-dimensional long and narrow chain distribution, which will cause a much more serious network congestion and energy hole problems in contrast to conventional WSNs.…”

Section: Introductionmentioning

confidence: 99%

Optimal data collection of multi-radio multi-channel multi-power wireless sensor networks for structural monitoring applications: A simulation study

Chen

et al. 2019

Struct Control Health Monit

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Summary Structural health monitoring (SHM) is a kind of data‐intensive applications for wireless sensors network (WSN), which usually requires a high network capacity. However, the bandwidth of traditional single‐radio single‐channel (SR‐SC) WSN is quite limited. In order to meet the requirement of structural monitoring, we investigate the multi‐radio multi‐channel multi‐power (MR‐MC‐MP) communication to improve the data collection performance of SHM‐oriented WSNs in terms of network capacity and power consumption. First, the data collection problem in MR‐MC‐MP WSNs is modeled as an optimization problem under the constraint of available time slots, radios, channels, and power levels. And then, combining the fast convergence of the particle swarm optimization (PSO) algorithm and high exploration performance of flower pollination optimization (FPA) algorithm, we propose a novel binary hybrid meta‐heuristic algorithm named BFPA‐PSO to solve the problem. In order to verify the advantage of the proposed BFPA‐PSO, some other meta‐heuristic algorithms are tested for the problem as well. Finally, several simulation experiments are carried out to test and compare the performance of different algorithms. Experiment results demonstrate that the proposed BFPA‐PSO algorithm has superior performance in terms of network capacity and energy consumption.

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Structural Health Monitoring Using Wireless Sensor Networks: A Comprehensive Survey

Cited by 380 publications

References 92 publications

Structural Health Monitoring using Varied Machine Learning Algorithms

Structural Health Monitoring using Varied Machine Learning Algorithms

A Social Environmental Sensor Network Integrated within a Web GIS Platform

Optimal data collection of multi-radio multi-channel multi-power wireless sensor networks for structural monitoring applications: A simulation study

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