Preprint Article Version 1 This version is not peer-reviewed

Geographical Area Network—Structural Health Monitoring Utility Computing Model

Version 1 : Received: 15 November 2018 / Approved: 19 November 2018 / Online: 19 November 2018 (03:58:56 CET)

How to cite: Tariq, H.; Tahir, A.; Touati, F.; Al-Hitmi, M.A.E.; Crescini, D.; Ben Mnaouer, A. Geographical Area Network—Structural Health Monitoring Utility Computing Model. Preprints 2018, 2018110412 (doi: 10.20944/preprints201811.0412.v1). Tariq, H.; Tahir, A.; Touati, F.; Al-Hitmi, M.A.E.; Crescini, D.; Ben Mnaouer, A. Geographical Area Network—Structural Health Monitoring Utility Computing Model. Preprints 2018, 2018110412 (doi: 10.20944/preprints201811.0412.v1).

Abstract

In view of intensified disasters and fatalities caused by natural phenomena and geographical expansion, there is a pressing need for a more effective environment logging for a better management and urban planning. This paper proposes a novel utility computing model (UCM) for structural health monitoring (SHM) that would enable dynamic planning of monitoring systems in an efficient and cost-effective manner. The proposed UCM consists of network-attached data drive that stores data from SHM logger, population count system and Geographic Information System (GIS) enhanced with a Cloud IoT data backup, display, and analysis server. The UCM using this data and data from building information systems applies a simple machine learning algorithm to generate real-time structure health and suggests re-planning of SHM units. The health of structure varies dynamically with disturbances created by higher occupancy and structure density per zone. The proposed SHM-UCM is unique in terms of its capability to manage heterogeneous SHM resources. This was tested in a case study on Qatar University (QU) in Doha Qatar, where it looked at where SHM nodes are distributed along with occupancy density in each building. This information was taken from QU simulated occupation and zone calculation models and then compared to ideal SHM system data. Results show the effectiveness of the proposed model in logging and dynamically planning SHM.

Subject Areas

Geographical Area Network (GAN); Structural Health Monitoring (SHM); Utility Computing (UC); Things as a Service (TaaS); Internet of Things (IoT)

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