This research investigates the performance of an existing structural damage detection method (SDIM) when only experimentally-obtained measurement information can be used to calculate the frequency response functions used to detect damage. The development of a SDIM that can accurately identify damage while processing measurements containing realistic noise levels and overcoming experimental modeling errors would provide a robust method for identifying damage in the larger, more complex structures found in practice. The existing SDIM program, GaDamDet, uses an advanced genetic algorithm, along with a two-dimensional finite element model of the structure, to identify the location and the severity of damage using the linear vibration information contained in frequency response functions (FRF) as response signatures. Datagen is a Matlab program that simulates the three-dimensional dynamic response of the four-story, two-bay by two-bay UBC test structure built at the University of British Columbia. The dynamic response of the structure can be obtained for a range of preset damage cases or for any user-defined damage case. Datagen can be used to provide the FRF measurement information for the three-dimensional test structure. Therefore, using the FRF measurements obtained from the UBC test structure allows for a more realistic evaluation of the performance of the SDIM provided by GaDamDet as the impact on performance of more realistic noise and model errors can be investigated. Previous studies evaluated the performance of the SDIM using only simulated FRF measurements obtained from a two-dimensional structural model. In addition, the disparity between the two-dimensional model used by the SDIM used to identify damage and the measurements obtained from the three-dimensional test structure is analyzed. The research results indicate that the SDIM is able to accurately detect structural damage to individually damaged members or to within a damaged floor, with few false damages identified. The SDIM provides an easy to use, visual, and accurate algorithm and its performance compares favorably to performance of the various damage detection algorithms that have been proposed by researchers to detect damage in the three-dimensional structural benchmark problem.

No description available.

Call number: LD2668 .T4 1958 R47

Various computer programs are currently used by electric utilities to determine if potential subsynchronous resonance problems exist which can impact turbine-generators. One of the most popular of these is the frequency scanning program. The representative transmission system input data for these programs are generally based on constant temperature and frequency. However, as conductor temperatures and applied frequencies fluctuate, the resistances also change. This thesis investigates the effects that resistance variations, due to temperature and frequency, have on frequency scanning results. The maximum resistance change (increased and decreased) from the standard value is determined and applied to the transmission lines in four study system cases. The frequency scan output for the modified cases is used to determine if torsional interaction has become more severe. It is found that, under extreme conditions, the net system damping can decrease by over one hundred percent.

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This thesis investigates the viability of using dynamic-based ‘damage fingerprints’ in combination with artificial neural network (ANN) techniques and principal component analysis (PCA) to identify defects in civil engineering structures. Vibration-based damage detection techniques are global methods and are based on the principle that damage alters both the physical properties, such as mass, stiffness and damping, as well as the dynamic properties of a structure. It is therefore feasible to utilise measured dynamic quantities, such as time histories, frequency response functions (FRFs) and modal parameters, from structural vibration to detect damage. Damage identification based on vibrational characteristics is essentially a form of pattern recognition problem, which looks for the discrimination between two or more signal categories, e.g., before and after a structure is damaged, or differences in damage levels or locations. Artificial neural networks are capable of pattern recognition, classification, signal processing and system identification, and are therefore an ideal tool in complementing dynamic-based damage detection techniques. Likewise, PCA has pattern recognition abilities and is capable of data reduction and noise filtering. With these characteristics, both techniques can help overcome limitations associated with previously developed vibration-based methods and assist in delivering more accurate and robust damage identification results. In this study, two types of dynamic-based damage identification methods are proposed. The first is based on the damage index (DI) method (initially proposed by Stubbs et al.), while the second approach uses changes in FRF data as damage fingerprints. The advantage of using damage patterns from the DI method, which is based on changes in modal strain energies, is that only measured mode shapes are required in the damage identification, without having to know the complete stiffness and mass matrices of the structure. The use of directly measured FRF data, which provide an abundance of information, is further beneficial as the execution of experimental modal analysis is not required, thus greatly reducing human induced errors. Both proposed methods utilise PCA and neural network techniques for damage feature extraction, data reduction and noise filtering. A hierarchical network training scheme based on network ensembles is proposed to take advantage of individual characteristics of damage patterns obtained from different sources (different vibrational modes for the DI-based method and different sensor locations for the FRF-based method). In the ensemble, a number of individual networks are trained in parallel, which optimises the network training and delivers improved damage identification outcomes. Both methods are first tested on a simple beam structure to assess their feasibility and performance. Then, the FRF-based method is applied to a more complicated structure, a two-storey framed structure, for validation purposes. The two methods are verified by numerical simulations and laboratory testing for both structures. As defects, notch type damage of different severities and locations are investigated for the beam structure. For the two-storey framed structure, three different types of structural change are studied, i.e. boundary damage, added mass changes and section reduction damage. To simulate field-testing conditions, the issue of limited sensor availability is incorporated into the analysis. For the DI-based method, sensor network limitations are compensated for by refining coarse mode shape vectors using cubic spline interpolation techniques. To simulate noise disturbances experienced during experimental testing, for the numerical simulations, measurement data are polluted with different levels of white Gaussian noise. The damage identifications of both methods are found to be accurate and reliable for all types of damage. For the DI-based method, the results show that the proposed method is capable of overcoming limitations of the original DI method associated with node point singularities and sensitivities to limited number of sensors. For the FRF-based method, excellent results are obtained for damage identification of the beam structure as well as of the two-storey framed structure. A major contribution is the training of the neural networks in a network ensemble scheme, which operates as a filtering mechanism against individual networks with poor performance. The ensemble network, which fuses results of individual networks, gives results that are in general better than the outcomes of any of the individual networks. Further, the noise filtering capabilities of PCA and neural networks demonstrate great performance in the proposed methods, especially for the FRF-based identification scheme.

This thesis investigates the viability of using dynamic-based ‘damage fingerprints’ in combination with artificial neural network (ANN) techniques and principal component analysis (PCA) to identify defects in civil engineering structures. Vibration-based damage detection techniques are global methods and are based on the principle that damage alters both the physical properties, such as mass, stiffness and damping, as well as the dynamic properties of a structure. It is therefore feasible to utilise measured dynamic quantities, such as time histories, frequency response functions (FRFs) and modal parameters, from structural vibration to detect damage. Damage identification based on vibrational characteristics is essentially a form of pattern recognition problem, which looks for the discrimination between two or more signal categories, e.g., before and after a structure is damaged, or differences in damage levels or locations. Artificial neural networks are capable of pattern recognition, classification, signal processing and system identification, and are therefore an ideal tool in complementing dynamic-based damage detection techniques. Likewise, PCA has pattern recognition abilities and is capable of data reduction and noise filtering. With these characteristics, both techniques can help overcome limitations associated with previously developed vibration-based methods and assist in delivering more accurate and robust damage identification results. In this study, two types of dynamic-based damage identification methods are proposed. The first is based on the damage index (DI) method (initially proposed by Stubbs et al.), while the second approach uses changes in FRF data as damage fingerprints. The advantage of using damage patterns from the DI method, which is based on changes in modal strain energies, is that only measured mode shapes are required in the damage identification, without having to know the complete stiffness and mass matrices of the structure. The use of directly measured FRF data, which provide an abundance of information, is further beneficial as the execution of experimental modal analysis is not required, thus greatly reducing human induced errors. Both proposed methods utilise PCA and neural network techniques for damage feature extraction, data reduction and noise filtering. A hierarchical network training scheme based on network ensembles is proposed to take advantage of individual characteristics of damage patterns obtained from different sources (different vibrational modes for the DI-based method and different sensor locations for the FRF-based method). In the ensemble, a number of individual networks are trained in parallel, which optimises the network training and delivers improved damage identification outcomes. Both methods are first tested on a simple beam structure to assess their feasibility and performance. Then, the FRF-based method is applied to a more complicated structure, a two-storey framed structure, for validation purposes. The two methods are verified by numerical simulations and laboratory testing for both structures. As defects, notch type damage of different severities and locations are investigated for the beam structure. For the two-storey framed structure, three different types of structural change are studied, i.e. boundary damage, added mass changes and section reduction damage. To simulate field-testing conditions, the issue of limited sensor availability is incorporated into the analysis. For the DI-based method, sensor network limitations are compensated for by refining coarse mode shape vectors using cubic spline interpolation techniques. To simulate noise disturbances experienced during experimental testing, for the numerical simulations, measurement data are polluted with different levels of white Gaussian noise. The damage identifications of both methods are found to be accurate and reliable for all types of damage. For the DI-based method, the results show that the proposed method is capable of overcoming limitations of the original DI method associated with node point singularities and sensitivities to limited number of sensors. For the FRF-based method, excellent results are obtained for damage identification of the beam structure as well as of the two-storey framed structure. A major contribution is the training of the neural networks in a network ensemble scheme, which operates as a filtering mechanism against individual networks with poor performance. The ensemble network, which fuses results of individual networks, gives results that are in general better than the outcomes of any of the individual networks. Further, the noise filtering capabilities of PCA and neural networks demonstrate great performance in the proposed methods, especially for the FRF-based identification scheme.

Thesis (M.E.)--University of Adelaide, 1984. / Includes bibliographical references (B1-B2).

Thesis (Ph. D.)--University of Texas at Austin, 2004. / Supervisor: Jeffrey K. Bennighof. Vita. Includes bibliographical references.

Thesis (Ph. D.)--University of Texas at Austin, 2004. / Supervisor: Jeffrey K. Bennighof. Vita. Includes bibliographical references.