Abstract

Structural Health Monitoring (SHM) implies monitoring the performance of structures using sensors to get an advance warning of the loss of structural capacity or potential collapse. Wireless-sensor based monitoring system is found to be advantageous over traditional wire-based system because of their ease of implementation, cost, flexibility and maintenance. However, power supply is an important concern for wireless sensors used in monitoring of civil engineering structures. While there are different efficient power usage methods and power supply solutions available for wireless sensors, their applications to SHM systems for civil infrastructure are not standardized. Energy harvesting by means of converting energy from the surrounding environment provides a desirable solution to address the issue of finite power source for wireless sensors. There are several sources of renewable energy that can be harnessed to generate electrical energy for the sensors. Use of energy harvesters for SHM applications are advancing these days. This research proposes the utilization of energy harvesters as damage detectors in addition to supplying power to the wireless nodes. This will ensure higher accuracy in the damage detection and capture sensor faults as well. This proposed concept is evaluated using pre-stressed concrete box bridge and steel truss bridge models in this thesis. This is a step towards increasing reliability of data acquisition. Instrumented structures are monitored by analyzing the responses recorded by deployed sensors in the form of signals. Processing these signals accurately is another important aspect of SHM to diagnose anomalies in structural behavior. The vibration signature of a structure changes with damage. In order to detect damage effectively, preservation of non-linear and non-stationary features of real structural responses is important. Decomposition of the signals into Intrinsic Mode Functions (IMF) by Empirical Mode Decomposition (EMD) and application of Hilbert-Huang Transform (HHT) addresses the time-varying instantaneous properties of the structural response. The energy distribution among different vibration modes of the intact and damaged structure depicted by Marginal Hilbert Spectrum (MHS) can detect location and severity of the damage. This research investigates this damage detection method by experimental modal testing of a steel frame structure and numerical simulation testing of a steel beam. The performance of Damage Indices (DI) from the energy distribution curves of the undamaged and damaged structure are assessed. It is found that MHS can effectively identify the presence and location of damage in the structure using its vibration response at undamaged and damaged conditions.