Development of an Automated Structural Health Monitoring System Based on Wireless Sensor Network for Civil Structures
| aut.embargo | No | en_NZ |
| aut.thirdpc.contains | No | en_NZ |
| dc.contributor.advisor | Beskhyroun, Sherif | |
| dc.contributor.author | Navabian, Niusha | |
| dc.date.accessioned | 2020-08-26T03:53:43Z | |
| dc.date.available | 2020-08-26T03:53:43Z | |
| dc.date.copyright | 2020 | |
| dc.date.issued | 2020 | |
| dc.date.updated | 2020-08-26T02:15:37Z | |
| dc.description.abstract | Generally, Structural Health Monitoring (SHM) is used to identify damage and deterioration in civil structures during their regular lifetime as well as after earthquakes. A complete SHM system incorporates different components, such as sensing system, data management, data transmission, and data analysis for reliable decision-making purposes. During this research, a new vibration-based SHM system has been developed for condition assessment of large-scale civil structures. This structural health monitoring system consists of three significant components; a new wireless smart sensor network, a new MATLAB-based data management and data analysis platform compatible with the sensor network, and a new vibration-based nonlinearity identification technique for early damage identification purposes. The wireless smart sensor network has been designed to meet the requirements for low-amplitude ambient vibration measurement and sudden event monitoring of civil structures. The designed wireless smart sensor network can record both ambient and earthquake-induced vibrations from structures using two periodic and event-triggered sampling modes. A data management and data analysis toolbox has been also developed in MATLAB Graphical User Interface Layout Toolbox. Various time-domain and frequency-domain system identification techniques have been implemented into the toolbox to extract modal parameters from the vibration measurements. In addition, a vibration-based nonlinearity identification technique has been proposed to identify nonlinearities in a dynamic system. This technique combines vibration measurements with Autoregressive Moving Average with eXogenous inputs (ARMAX) model and Fuzzy C-means clustering (FCM) algorithm to categorise the linear and nonlinear behaviours of a structure, when it is subjected to various levels of earthquake excitation. To verify the reliability of different components of the developed SHM system, a series of shaking table tests was conducted on a steel truss bridge model at AUT structural laboratory. In addition, one span of a full-scale bridge, Newmarket viaduct located in Auckland, was instrumented using the developed wireless-based SHM system to investigate the system performance in an outdoor environment. The results obtained from the laboratory and field experiments showed that the developed vibration-based SHM system has a reliable performance in terms of hardware and software for condition monitoring of large-scale structures. | en_NZ |
| dc.identifier.uri | https://hdl.handle.net/10292/13612 | |
| dc.language.iso | en | en_NZ |
| dc.publisher | Auckland University of Technology | |
| dc.rights.accessrights | OpenAccess | |
| dc.subject | Structural health monitoring | en_NZ |
| dc.subject | Wireless smart sensor network | en_NZ |
| dc.subject | Damage identification | en_NZ |
| dc.subject | Vibration measurement | en_NZ |
| dc.subject | Earthquake excitation | en_NZ |
| dc.subject | Long-span bridges | en_NZ |
| dc.title | Development of an Automated Structural Health Monitoring System Based on Wireless Sensor Network for Civil Structures | en_NZ |
| dc.type | Thesis | en_NZ |
| thesis.degree.grantor | Auckland University of Technology | |
| thesis.degree.level | Doctoral Theses | |
| thesis.degree.name | Doctor of Philosophy | en_NZ |