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Navabian, N (2020) Development of an automated structural health monitoring system based on wireless sensor network for civil structures, Unpublished PhD Thesis, , Auckland University of Technology.
- Type: Thesis
- Keywords: reliability; bridge; data management; decision making; deterioration; monitoring; measurement; vibration; earthquake; experiment
- ISBN/ISSN:
- URL: https://hdl.handle.net/10292/13612
- 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.