Abstract

Structural Health Monitoring (SHM) plays a vital role in assessing in-situ the performance of a structure. There are several techniques available for System Identification, Damage Detection and Model Updating. SHM based on the vibration of structures has attracted the attention of researchers in many fields such as: civil, aeronautical and mechanical engineering. This research focuses on the state-of-the art methods for Vibration Testing, Modal Analysis, Model Updating, and Damage Detection in structures. The objective of the thesis is to develop efficient methods in the above areas of SHM using ambient vibration testing. To develop and verify the proposed methods, several case studies are to be developed and implemented a new technique in multi setup merging by use of Random Decrement Technique (RDT). In addition, the preprocessing methods are required for some certain tests like ambient vibration where signal to noise ratio and vibration amplitude are quite low. The RDT is a time domain procedure, where the structural responses to operational loads are transformed into random decrement functions, which are proportional to the correlation functions of a system’s operational responses, which can be considered equivalent to free vibration responses. Ambient vibration test of a structure usually produces noisy response and the existing modal identification techniques such as Frequency Domain Decomposition (FDD) and Stochastic Subspace Identifications (SSI) techniques often fail to produce accurate results in such case. Due to contamination of ambient vibration with white Gaussian noise, preprocessing may be required to clean up the vibration signal in order to detect the modal properties accurately. For Operational Modal Analysis (OMA), the multi-setup (or roving sensors) measurement techniques are required due to less number of sensors as compared to the number of degrees of freedom (DOFs) in a structure. The technique is based on selection of DOFs as reference, which is fixed during all measurements, and the rest of sensors are roved in each setup. Among all available merging techniques, PreGER have been used recently as an alternative method because the merging process have been conducted before parameter estimation. In addition to modal analysis, the measured vibration response of a structure and the results from its Finite Element (FE) model often have some differences because of the idealization and assumptions in representing the structural system, material properties, support conditions, etc., in the FE model. The methods for model updating and damage detection have been classified into Physics-based methods where a mathematical model of a structure needs to be constructed, and data-driven methods where an explicit FE model is used, only the data pattern are used for identifying the changes is a structure.The objective of this research is to develop the hybrid method which is the combination of the physics-based and data-driven methods. The Matrix Update Method (MUM) has been utilized as a physics-based method of model updating, to compare the results between developed method and MUM. Furthermore, damage in structural system is an important concern as it weakens the structure and reduces its functional capacity, and may even cause failure. There are two main challenges in damage detection in the research including baseline free method and the sensitivity analysis. Finally, the proposed merging technique RDT-PreGER shows the efficient method to reduce the number of data and remove the noise in ambient test. In addition, implementing hybrid technique in FE model updating result in providing accurate result by choosing the proper number of groups and well trained network. The result of damage detection technique show that all baseline and baseline free techniques have an uncertainty so the subtle level of damage cannot be detected properly.