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Doctor of Philosophy / Department of Civil Engineering / Asadollah Esmaeily / Civil structures are susceptible to damages over their service lives due to aging, environmental loading, fatigue and excessive response. Such deterioration significantly affects the performance and safety of structure. Therefore, it is necessary to monitor the structural performance, detect and assess damages at the earliest possible stage in order to reduce the life-cycle cost of structure and improve its reliability. Over the last two decades, extensive research has been conducted on structural health monitoring and damage detection. In this study, a signal-based pattern-recognition method was applied to detect structural damages with a single or limited number of input/output signals. This method is based on the extraction of sensitive features of the structural response under a known excitation that present a unique pattern for any particular damage scenario. Frequency-based features and time-frequency-based features of the acceleration response were extracted from the measured vibration signals by Fast Fourier Transform (FFT) and Continuous Wavelet Transform (CWT) to form one-dimensional or two-dimensional patterns, respectively. Three pattern recognition algorithms were investigated when performing pattern-matching: (1) correlation, (2) least square distance, and (3) Cosh spectral distance. To demonstrate the validity and accuracy of the method, numerical and experimental studies were conducted on a simple small-scale three-story steel building. In addition, the efficiency of the features extracted by Wavelet Packet Transform (WPT) was examined in the experimental study. The results show that the features of the signal for different damage scenarios can be uniquely identified by these transformations. Suitable correlation algorithm can then be used to identify the most probable damage scenario. The proposed method is suitable for structural health monitoring, especially for the online monitoring applications. Meanwhile, the choice of wavelet function affects the resolution of the detection process and is discussed in the “experimental study part” of this report.

A blackboard-based system which learns recognition rules for objects from a set of training examples, and then identifies and locates these objects in test images, is presented. The system is designed to use data from a feature matcher developed at R.S.R.E. Malvern which finds the best matches for a set of feature patterns in an image. The feature patterns are selected to correspond to typical object parts which occur with relatively consistent spatial relationships and are sufficient to distinguish the objects to be identified from one another. The learning element of the system develops two separate sets of rules, one to identify possible object instances and the other to attach probabilities to them. The search for possible object instances is exhaustive; its scale is not great enough for pruning to be necessary. Separate probabilities are established empirically for all combinations of features which could represent object instances. As accurate probabilities cannot be obtained from a set of preselected training examples, they are updated by feedback from the recognition process. The incorporation of rule induction and feedback into the blackboard system is achieved by treating the induced rules as data to be held on a secondary blackboard. The single recognition knowledge source effectively contains empty rules which this data can be slotted into, allowing it to be used to recognise any number of objects - there is no need to develop a separate knowledge source for each object. Additional object-specific background information to aid identification can be added by the user in the form of background checks to be carried out on candidate objects. The system has been tested using synthetic data, and successfully identified combinations of geometric shapes (squares, triangles etc.). Limited tests on photographs of vehicles travelling along a main road were also performed successfully.

Positron Emission Tomography (PET) serves a unique and important role in medical research because it permits non-invasive quantitative study of biological processes using radionuclides of naturally occuring elements. In the last decade, the imaging properties of PET have improved significantly because of better understanding of the design principles and introduction of novel concepts. One such development has been that of the 'block' detector system, consisting of an array of scintillation crystals coupled to a relatively small number of photomultiplier tubes (PMTs). Identification of the particular element in the block is made by comparing the outputs from the PMTs. The block provides the basic unit of the detector rings in modern PET cameras. The prototype block detector system employed in this study incorporates the CTI 831 detector module (49.47 mm wide by 53.36 mm tall by 30 mm deep). This is segmented into a matrix of 8 by 4 crystal elements, 5.62 mm(transaxially) by 12.86 mm (axially) and 30 mm (deep), and coupled to four square PMTs. The drive towards improvement of image quality in PET has prompted the development of even smaller crystals, promising 'high resolution' multiplane imaging. While these detectors have significant advantages over other detectors, the aim of this study was to investigate the physical performance of this specific block detector and to assess how its limitations will affect the information obtained from it. The system investigated offered a coincidence time resolution of 5.8 ±0.3 ns FWHM for a pair of block detectors, an individual crystal energy resolution of 19 % ±3 FWHM at 511 keV, maximum intrinsic efficiency of 45.7% ±0.5 and a column transaxial resolution of 4.2 ±0.4 mm FWHM, offering important immediate advantages. However, the drawback in the current implementation scheme is the nonuniformity across the detector face. The variation of efficiency, energy and spatial resolution for the individual detector crystals across the face of the detector block were investigated, the factors contributing to these variations were identified and suggestions for reducing their effects were made. For example inter-detector scattering was found to be a problem that leads to mispositioning of detected events. Different techniques for evaluating the amount, distribution and consequently the removal of inter-detector scattering were established. Finally these block detectors offer other possibilities like gamma-gamma coincidence imaging, attractive for adaption to neutron induced gamma ray emission tomography (NIGET). This would reduce the long scanning times presently required. However, the 'electronic collimation' provided by the coincidence detection of the two annihilation photons along the line of response between the opposing detectors is lost. Imaging of the cascade gamma rays necessitates the use of physical collimation in order to define a plane through the object. This will however reduce the absolute efficiency of the system from 2.28*10-2t 2*104to 7.3*10-6± 3"10-7w hen collimation is used on both sides but increases the spatial resolution from 5.7 ±0.2 to 2.4 ±0.2 mm. However, if a collimator is used on one side only, the spatial resolution (3.8 ±0.2 mm) obtained is comparable to that of a Ge detector with a1 mm diameter hole collimator and the absolute efficiency of the system (1.1 *10-4±3`10'5) is many times better.

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