With massive infrastructures built in US, timely condition assessment of these infrastructures becomes critical to daily traffic and economics. Due to high cost, long time consumption of direct condition assessment methods, such as closing traffic for sensor installation and monitoring, indirect bridge monitoring has become a promising method. However, the technology is in its initial stage and needs substantial refinement. In this research, virtual simulation approaches, both in 2D and 3D, are used to model the bridge and vehicle interaction through ABAQUS. Artificial Damages were embedded to the model according to different locations and different levels of intensities. With the modelled outcomes, the hypothesis of identifying damages through the responses of the vehicle will be tested. From the simulated vehicle responses, bridge frequencies and damage locations and sizes could be identified accurately through short time flourier transformation and mode shape difference.

In recent years more emphasis has been placed on in-situ condition based monitoring of engineering systems and structures. Aerospace components are manufactured from composite materials more often. Structural health monitoring (SHM) systems are required in the aerospace industry to monitor the safety and integrity of the structure and will ensure that composites reach its full potential within the industry. Damage detection techniques form an integral part of such SHM systems. With this work a damage detection technique is developed for intended eventual use on composite structures, but starting first on isotropic structures. The damage mechanism that is of interest is delamination damage in composites. A simple numerical equivalent is implemented here however. Two damage indicators, the strain cumulative damage factor (SCDF) and the strain-frequency damage level (SFDL) are introduced. The respective damage indicators are calculated from output-only strain and acceleration response data. The effectiveness of the system to detect damage in the structure is critically evaluated and compared to other damage detection techniques such as the natural frequency method. The sensitivity to damage and performance of both these indicators is examined numerically by evaluating two deterministic damage cases. The numerical study is enhanced through the use of an updated finite element model. The minimum number of sensors capable of detecting the presence and locate damage spatially is determined from numerical simulations. Monte Carlo type analysis is performed by letting the damaged area vary stochastically and calculating the respective damage indicators. The model updating procedure from measured mobility frequency response functions (FRFs) is described. The application of the technique to real structures is examined experimentally. Two test structures with two different damage scenarios are examined. The spatial location and presence of damage can be established from both the SCDF and SFDL values, respectively. The spatial location obtained from the SCDF values corresponded to the known damage location for both the numerical and experimental study. The SFDL proved to be more sensitive than the natural frequency method and could be used to calculate the level of damage within the structure. / Dissertation (MEng)--University of Pretoria, 2009. / Mechanical and Aeronautical Engineering / unrestricted

Research in damage detection and structural health monitoring in engineering systems during their service life has received increasing attention because of its importance and benefits in maintenance and rehabilitation of structure. Though the concept of vibration-based damage detection has been in existence for decades, and several procedures have been proposed to date, its practical applications remain limited, considering the increased utilization of sensors to measure structural response at multiple points. In this thesis, use of acceleration response of the structure as a method of global damage detection is explored using instantaneous frequency and stiffness degradation methods. Instantaneous frequency was estimated using continuous wavelet transform of measured acceleration response of the structure subjected to ground motion. Complex Morlet Wavelet was used in the time-frequency analysis due to its ability to provide sufficient resolution in both time and frequency domains. This ability is important in analyzing nonstationary signals like earthquake response of structure containing sharp changes in the signal. The second method, called the stiffness degradation analysis, is based on estimating the time-varying stiffness. This estimation is done by fitting a moving least-square line to the force-displacement loop for the duration of the ground motion.A four-story shear building is used as the model structure for numerical analysis. Two damage scenarios are considered: single damage instant and multiple damage instants. Both scenarios assume that the damage occurs at a single location. In the numerical simulations, damage was modeled as a reduction in the stiffness of the first floor, and accelerations were computed at floor levels using state-space model. The two methods were compared in terms of their damage detection ability and it was shown that both methods can be used in detecting damage and the time at which the damage occurs. These methods can later be extended by simultaneously considering the correlations of responses at all floor levels. This extension may enable locating the damage and quantifying the severity of the damage.

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京都大学 / 0048 / 新制・課程博士 / 博士(工学) / 甲第21080号 / 工博第4444号 / 新制||工||1691(附属図書館) / 京都大学大学院工学研究科社会基盤工学専攻 / (主査)教授 KIM Chul-Woo, 教授 杉浦 邦征, 教授 八木 知己 / 学位規則第4条第1項該当 / Doctor of Philosophy (Engineering) / Kyoto University / DFAM

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Detecting structural damage in civil structures through non-destructive means is a growing field in civil engineering. There are many viable methods, but they can often be time consuming and costly; requiring large amounts of data to be collected. By determining which data are the most optimal at detecting damage and which are not the methods can be better optimized. The objective of this thesis was to adapt an existing method of data optimization, used for damage detection in mechanical engineering applications, for use with civil structures. The existing method creates Parameter Signatures based on characteristics from the system being analyzed, from which preferred locations for recording data are determined. For civil structures this method could potentially be used to locate the preferred locations to place accelerometers such that the minimum number of accelerometers is needed to properly detect the location and severity of damage in the structure. This method was first tested on fully analytical computer model structures under perfect conditions to determine its mathematical feasibility with civil structures. It was then tested on data recorded from physical test structures under “real-world” conditions to determine its feasibility as an actual damage detection optimization procedure. Results from the analytical testing show that this is in fact a viable method for determining the preferred sensor positions in civil structures. Furthermore, these results were verified for a variety of excitation types. Physical testing was inconclusive, leading to great insight about what obstacles are impeding this method and should looked at in future research.

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A machine learning approach to damage detection is presented for a bridge structural health monitoring system, validated on the renowned Z-24 bridge benchmark dataset where a sensor instrumented, threespan bridge was realistically damaged in stages. A Bayesian autoencoder neural network is trained to reconstruct raw sensor data sequences, with uncertainty bounds in prediction. The reconstruction error is then compared with a healthy-state error distribution and the sequence determined to come from a healthy state or not. Several realistic damage stages were successfully detected, making this a viable approach in a data-based monitoring system of an operational bridge. This is a fully operational, machine learning based bridge damage detection system, that is learned directly from raw sensor data. / En maskininlärningsmetod för strukturell skadedetektering av broar presenteras. Metoden valideras på det kända referensdataset Z-24, där en sensor-instrumenterad trespannsbro stegvist skadats. Ett Bayesianskt neuralt nätverk med autoenkoders tränas till att rekonstruera råa sensordatasekvenser, med osäkerhetsgränser i förutsägningen. Rekonstrueringsavvikelsen jämförs med avvikelsesfördelningen i oskadat tillstånd och sekvensen bedöms att komma från ett skadad eller icke skadat tillstånd. Flera realistiska stegvisa skadetillstånd upptäcktes, vilket gör metoden användbar i ett databaserat skadedetektionssystem för en bro i full storlek. Detta är ett lovande steg mot ett helt operativt databaserat skadedetektionssystem.

Around the world bridges are ageing. In Europe approximately two thirds of all railway bridges are over 50 years old. As these structures age, it becomes increasingly important that they are properly maintained. If damage remains undetected this can lead to premature replacement which can have major financial and environmental costs. It is also imperative that bridges are kept safe for the people using them. Thus, it is necessary for damage to be detected as early as possible. This research investigates an unsupervised, model-free damage detection method which could be implemented for continuous structural health monitoring. The method was based on past research by Gonzalez and Karoumi (2015), Neves et al. (2017) and Chalouhi et al. (2017). An artificial neural network (ANN) was trained on accelerations from the healthy structural state. Damage sensitive features were defined as the root mean squared errors between the measured data and the ANN predictions. A baseline healthy state could then be established by presenting the trained ANN with more healthy data. Thereafter, new data could be compared with this reference state. Outliers from the reference data were taken as an indication of damage. Two outlier detection methods were used: Mahalanobis distance and the Kolmogorov-Smirnov test. A model steel bridge with a span of 5 m, width of 1 m and height of approximately 1.7 m was used to study the damage detection method. The use of an experimental model allowed damaged to be freely introduced to the structure. The structure was excited with a 12.7 kg rolling mass at a speed of approximately 2.1 m/s (corresponding to a 20.4 ton axle load moving at 47.8 km/h in full scale). Seven accelerometers were placed on the structure and their locations were determined using an optimal sensor placement algorithm. The objectives of the research were to: identify a number of single damage cases, distinguish between gradual damage cases and identify the location of damage. The proposed method showed promising results and most damage cases were detected by the algorithm. Sensor density and the method of excitation were found to impact the detection of damage. By training the ANN to predict correlations between accelerometers the sensor closest to the damage could be detected, thus successfully localising the damage. Finally, a gradual damage case was investigated. There was a general increase in the damage index for greater damage however, this did not progress smoothly and one case of ‘greater’ damage showed a decrease in the damage index.