Structural Health Monitoring (SHM) is a promising tool for condition assessment of bridge structures. SHM of bridges can be performed for different purposes in long or short-term. A few aspects of short- and long-term monitoring of highway bridges are addressed in this research. Without quantifying environmental effects, applying vibration-based damage detection techniques may result in false damage identification. As part of a long-term monitoring project, the effect of temperature on vibrational characteristics of two continuously monitored bridges are studied. Natural frequencies of the structures are identified from ambient vibration data using the Natural Excitation Technique (NExT) along with the Eigen System Realization (ERA) algorithm. Variability of identified natural frequencies is investigated based on statistical properties of identified frequencies. Different statistical models are tested and the most accurate model is selected to remove the effect of temperature from the identified frequencies. After removing temperature effects, different damage cases are simulated on calibrated finite-element models. Comparing the effect of simulated damages on natural frequencies showed what levels of damage could be detected with this method. Evaluating traffic loads can be helpful to different areas including bridge design and assessment, pavement design and maintenance, fatigue analysis, economic studies and enforcement of legal weight limits. In this study, feasibility of using a single-span bridge as a weigh-in-motion tool to quantify the gross vehicle weights (GVW) of trucks is studied. As part of a short-term monitoring project, this bridge was subjected to four sets of high speed, live-load tests. Measured strain data are used to implement bridge weigh-in-motion (B-WIM) algorithms and calculate the corresponding velocities and GVWs. A comparison is made between calculated and static weights, and furthermore, between supposed speeds and estimated speeds of the trucks. Vibration-based techniques that use finite-element (FE) model updating for SHM of bridges are common for infrastructure applications. This study presents the application of both static and dynamic-based FE model updating of a full scale bridge. Both dynamic and live-load testing were conducted on this bridge and vibration, strain, and deflections were measured at different locations. A FE model is calibrated using different error functions. This model could capture both global and local response of the structure and the performance of the updated model is validated with part of the collected measurements that were not included in the calibration process.

This dissertation studied the structural identification and damage detection of civil engineering structures. Several issues regarding structural health monitoring were addressed. The data-driven subspace identification algorithm was investigated for modal identification of bridges using output-only data. This algorithm was tested through a numerical truss bridge with abrupt damage as well as a real concrete highway bridge with actual measurements. Stabilization diagrams were used to analyze the identified results and determine the modal characteristics. The identification results showed that this identification method is quite effective and accurate. The influence of temperature fluctuation on the frequencies of a highway concrete bridge was investigated using ambient vibration data over a one-year period of a highway bridge under health monitoring. The data were fitted by nonlinear and linear regression models, which were then analyzed. The substructure identification by using an adaptive Kalman filter was investigated by applying numerical studies of a shear building, a frame structure, and a truss structure. The stiffness and damping were identified successfully from limited acceleration responses, while the abrupt damages were identified as well. Wavelet analysis was also proposed for damage detection of substructures, and was shown to be able to approximately locate such damages. Delamination detection of concrete slabs by modal identification from the output-only data was proposed and carried out through numerical studies and experimental modal testing. It was concluded that the changes in modal characteristics can indicate the presence and severity of delamination. Finite element models of concrete decks with different delamination sizes and locations were established and proven to be reasonable. Pounding identification can provide useful early warning information regarding the potential damage of structures. This thesis proposed to use wavelet scalograms of dynamic response to identify the occurrence of pounding. Its applications in a numerical example as well as shaking table tests of a bridge showed that the scalograms can detect the occurrence of pounding very well. These studies are very useful for vibration-based structural health monitoring.

京都大学 / 0048 / 新制・課程博士 / 博士(工学) / 甲第19299号 / 工博第4096号 / 新制||工||1631(附属図書館) / 32301 / 京都大学大学院工学研究科建築学専攻 / (主査)教授 中島 正愛, 教授 川瀬 博, 教授 竹脇 出 / 学位規則第4条第1項該当 / Doctor of Philosophy (Engineering) / Kyoto University / DFAM

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In recent years, due to the increasing efficiency and availability of information technologies for collecting massive amounts of data (e.g., smart meters and sensors), a variety of advanced technologies and decision-making strategies in the civil engineering sector have shifted in leaps and bounds to a data-driven manner. While there is still no consensus in industry and academia on the latest advances, challenges, and trends in some innovative data-driven methods related to, e.g., deep learning and neural networks, it is undeniable that these techniques have been proven to be considerably effective in helping our academics and engineers solve many real-life tasks related to the smart city framework. This dissertation systematically presents the investigation and development of the cutting-edge data-driven methods related to two specific areas of civil engineering, namely, Residential Electricity Modeling (REM) and Structural Health Monitoring (SHM). For both components, the presentation of this dissertation starts with a brief review of classical data-driven methods used in particular problems, gradually progresses to an exploration of the related state-of-the-art technologies, and eventually lands on our proposed novel data-driven strategies and algorithms. In addition to the classical and state-of-the-art modeling techniques focused on these two areas, this dissertation also put great emphasis on the proposed effective feature extraction and selection approaches. These approaches are aimed to optimize model performance and to save computational resources, for achieving the ideal characterization of the information embedded in the collected raw data that is most relevant to the problem objectives, especially for the case of modeling deep neural networks. For the problems on REM, the proposed methods are validated with real recorded data from multi-family residential buildings, while for SHM, the algorithms are validated with data from numerically simulated systems as well as real bridge structures.

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There is a need in exploring structural health monitoring technologies for the composite structures particularly aged Composite Overwrapped Pressure Vessels (COPVs) for the current and future implementation of COPVs for space missions. In this study, the research was conducted in collaboration with NASA Kennedy Space Center and also NASA Marshall Space and Flight Center engineers. COPVs have been used to store inert gases like helium (for propulsion) and nitrogen (for life support) under varying degrees of pressure onboard the orbiter since the beginning of the Space Shuttle Program. After the Columbia accident, the COPVs were re-examined and different studies (e.g. Laser profilometry inspection, NDE utilizing Raman Spectroscopy) have been conducted and can be found in the literature. To explore some of the unique in-house developed hardware and algorithms for monitoring COPVs, this project is carried out with the following general objectives: 1) Investigate the obtaining indices/features related to the performance and/or condition of pressure vessels 2) Explore different sensing technologies and Structural Health Monitoring (SHM) systems 3) Explore different types of data analysis methodologies to detect damage with particular emphasis on statistical analysis, cross-correlation analysis and Auto Regressive model with eXogeneous input (ARX) models 4) Compare differences in various types of pressure vessels First an introduction to theoretical pressure vessels, which are used to compare to actual test specimens, is presented. Next, a background review of the test specimens including their applications and importance is discussed. Subsequently, a review of related SHM applications to this study is presented. The theoretical background of the data analysis methodologies used to detect damage in this study are provided and these methodologies are applied in the laboratory using Composite Overwrapped Pressure Vessels (COPVs) to determine the effectiveness of these techniques. Next another study on the Air Force Research Laboratory (AFRL) Tank that is carried out in collaboration with NASA KSC and NASA MSFC is presented with preliminary results. Finally the results and interpretations of both studies are summarized and discussed.

Inverse analysis of nonlinear dynamic systems is an important area of research in the eld of structural health monitoring for civil engineering structures. Structural damage usually involves localized nonlinear behaviors of dynamic systems that evolve into different classes of nonlinearity as well as change system parameter values. Numerous parametric modal analysis techniques (e.g., eigensystem realization algorithm and subspace identification method) have been developed for system identification of multi-degree-of-freedom dynamic systems. However, those methods are usually limited to linear systems and known for poor sensitivity to localized damage. On the other hand, non-parametric identification methods (e.g., artificial neural networks) are advantageous to identify time-varying nonlinear systems due to unpredictable damage. However, physical interpretation of non-parametric identification results is not as straightforward as those of the parametric methods. In this study, the Multidegree-ofFreedom Restoring Force Method (MRFM) is employed as a semi-parametric nonlinear identi- fication method to take the advantages of both the parametric and non-parametric identification methods. The MRFM is validated using two realistic experimental nonlinear dynamic tests: (i) largescale shake table tests using building models with different foundation types, and (ii) impact test using wind blades. The large-scale shake table test was conducted at Tongji University using 1:10 scale 12-story reinforced concrete building models tested on three different foundations, including pile, box and fixed foundation. The nonlinear dynamic signatures of the building models collected from the shake table tests were processed using MRFM (i) to investigate the effects of foundation types on nonlinear behavior of the superstructure and (ii) to detect localized damage during the shake table tests. Secondly, the MRFM was applied to investigate the applicability of this method to wind turbine blades. Results are promising, showing a high level of nonlinearity of the system and how the MRFM can be applied to wind-turbine blades. Fuiii ture studies were planned for the comparison of physical characteristic of this blade with blades created made of other material.

Structural Health Monitoring (SHM) is employed to track and evaluate damage and deterioration during regular operation as well as after extreme events for aerospace, mechanical and civil structures. A complete SHM system incorporates performance metrics, sensing, signal processing, data analysis, transmission and management for decision-making purposes. Damage detection in the context of SHM can be successful by employing a collection of robust and practical damage detection methodologies that can be used to identify, locate and quantify damage or, in general terms, changes in observable behavior. In this study, different damage detection methods are investigated for global condition assessment of structures. First, different parametric and non-parametric approaches are re-visited and further improved for damage detection using vibration data. Modal flexibility, modal curvature and un-scaled flexibility based on the dynamic properties that are obtained using Complex Mode Indicator Function (CMIF) are used as parametric damage features. Second, statistical pattern recognition approaches using time series modeling in conjunction with outlier detection are investigated as a non-parametric damage detection technique. Third, a novel methodology using ARX models (Auto-Regressive models with eXogenous output) is proposed for damage identification. By using this new methodology, it is shown that damage can be detected, located and quantified without the need of external loading information. Next, laboratory studies are conducted on different test structures with a number of different damage scenarios for the evaluation of the techniques in a comparative fashion. Finally, application of the methodologies to real life data is also presented along with the capabilities and limitations of each approach in light of analysis results of the laboratory and real life data.