Structural health monitoring installation scheme using utility computing model

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dc.contributor.authorTariq, Hasan
dc.contributor.authorAl-Hitmi, Mohammed Abdulla E.
dc.contributor.authorTahir, Anas
dc.contributor.authorCrescini, Damiano
dc.contributor.authorTouati, Farid
dc.contributor.authorMnaouer, Adel Ben
dc.date.accessioned2020-01-29T09:47:00Z
dc.date.available2020-01-29T09:47:00Z
dc.date.copyright2018en_US
dc.date.issued2018
dc.descriptionThis conference paper is not available at CUD collection. The version of scholarly record of this conference paper is published in 2018 2nd European Conference on Electrical Engineering and Computer Science (EECS) (2018), available online at: https://doi.org/10.1109/EECS.2018.00019.en_US
dc.description.abstractIn view of intensified disasters and fatalities caused by natural phenomena, there is a pressing need for an efficient environment logging that provides structural information to administrators for a better management and urban planning. This paper proposes a novel utility model for structural health monitoring that would enable early detection of risk factors and mitigation of loss. The proposed utility computing model takes the input data in terms of 'number of occupants' in a building (i.e. MAC, International Mobile Equipment Identifier addresses and biometric attendance system installed) and SHM system data (i.e. sensors readings). It give visual representation of all the data for utility managers and experts to decide better location of SHM and number of SHM needed per zone depending on high disturbances created due to higher occupancy and number of structures per zone. Denser area with higher structures and higher population will require more precise and accurate SHM systems compared to rural areas. It also analyzes the data from SHM system and using simple machine learning algorithm give experts' suggestions for type of SHM needed at an area. It make it possible for the data of each and every device of SHM systems over several zones to be accessible by specific authorities that can be used to predict as well as forecast any natural disaster. The Structural Health Monitoring utility model is unique in terms of its heterogeneity of resource management in realizing the utility processes. Finally, a case study of Qatar University is looked at where nodes are distributed in zones along with occupant measuring is used over each building. The data was taken over simulated occupation models and mathematical models from literature for occupation and zone calculation using ideal SHM system data. It can be inferred from the data that real-time analysis data will act similar to simulated and proposed Utility Computing System will give visual data and analyze the zones as can be seen in the results. Therefore, SHM Utility Computing model is efficient and most effective system that save cost as well as prepare authorities for maintenance of a structure or crisis management due to external surroundings. © 2018 IEEE.en_US
dc.description.sponsorship"Qatar National Research Fund, QNRF Qatar National Research Fund, QNRF Qatar Foundation, QF"en_US
dc.identifier.citationTariq, H., Al-Hitmi, M. A. E., Tahir, A., Crescini, D., Touati, F., & Manouer, A. B. (2018). Structural health monitoring installation scheme using utility computing model. In Proceedings - 2018 2nd European Conference on Electrical Engineering and Computer Science, EECS 2018 (pp. 50–55). https://doi.org/10.1109/EECS.2018.00019en_US
dc.identifier.isbn9781728119298
dc.identifier.urihttp://dx.doi.org/10.1109/EECS.2018.00019
dc.identifier.urihttps://hdl.handle.net/20.500.12519/63
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relationAuthors Affiliations: Tariq, H., Department of Electrical Engineering, College of Engineering, Qatar University, Doha, Qatar; Al-Hitmi, M.A.E., Qatar University, Doha, Qatar; Tahir, A., Department of Electrical Engineering, College of Engineering, Qatar University, Doha, Qatar; Crescini, D., Brescia University, Brescia, Italy; Touati, F., Department of Electrical Engineering, College of Engineering, Qatar University, Doha, Qatar; Manouer, A.B., Canadian University of Dubai, Dubai, United Arab Emirates
dc.relation.ispartofseriesProceedings - 2018 2nd European Conference on Electrical Engineering and Computer Science, EECS 2018;
dc.rightsPermission to reuse abstract has been secured from Institute of Electrical and Electronics Engineers Inc.
dc.rights.holderCopyright : 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
dc.rights.urihttps://www.ieee.org/publications/rights/rights-policies.html
dc.subjectGeographical Area Network (GAN)en_US
dc.subjectInternet of Things (IoT)en_US
dc.subjectStructural Health Monitoring (SHM)en_US
dc.subjectThings as a Service (TaaS)en_US
dc.subjectUtility Computing (UC)en_US
dc.subjectDisastersen_US
dc.subjectHealth risksen_US
dc.subjectLearning algorithmsen_US
dc.subjectMachine learningen_US
dc.subjectMachineryen_US
dc.subjectThings as a servicesen_US
dc.subjectUtility computingen_US
dc.titleStructural health monitoring installation scheme using utility computing modelen_US
dc.typeConference Paperen_US

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