Name
Abstract | ||
|---|---|---|
In 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. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/EECS.2018.00019 | 2018 2nd European Conference on Electrical Engineering and Computer Science (EECS) |
Keywords | Field | DocType |
Structural Health Monitoring (SHM),Utility Computing,Things as a Service (TaaS),Geographical Area Network (GAN),Internet of Things (IoT) | Structural health monitoring,Computer science,Utility computing,Reliability engineering | Conference |
ISBN | Citations | PageRank |
978-1-7281-1930-4 | 0 | 0.34 |
References | Authors | |
4 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hasan Tariq | 1 | 0 | 1.01 |
| Mohammed Abdulla E. Al-Hitmi | 2 | 0 | 1.01 |
| Anas Tahir | 3 | 1 | 1.03 |
| Damiano Crescini | 4 | 0 | 0.68 |
| Farid Touati | 5 | 160 | 14.62 |
| Adel Ben Manouer | 6 | 0 | 0.34 |