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<p dir="ltr">The research in this thesis focuses on developing an alert system to detect damage or impact on bridge. It employs Raspberry Pi and accelerometers for real-time health monitoring. The methodology includes bridge model creation, testing under no damage, impact, and structural damage conditions, and data processing for vibration frequency analysis. The aim is to differentiate between normal bridge conditions, collisions, and structural damages, providing timely notifications for necessary investigations or repairs. The study addresses the challenges in bridge safety and aims to improve maintenance efficiency and reliability.</p><p><br></p>

As the nation’s infrastructure continues to age, there is a growing need for methods to safely inspect critical structures, often during operation. The failure of post-tensioned anchor rods in Tainter style flood gates presented an immediate need for new inspection capabilities for U.S. Army Corps of Engineers (USACE) managed flood control gates. In response to this need, the Sensor Integration Branch (SIB) of The U.S. Army Engineer Research and Develop Center (ERDC) developed the capability to non-destructively test (NDT) both greased and grouted cylindrical steel anchor rods using higher order guided wave ultrasonic testing. Understanding the results requires a knowledge of both guided waves and digital signal processing in order to identify the possibility of a defect. In order to both facilitate rapid defect identification and expanding ease-of-use of the equipment, the research in this thesis uses a combination of the discrete wavelet transform (DWT) and gradient boosting machine learning to build a model capable of identifying the dispersive defect responses in the rods.

As transportation infrastructure continues to age, new methods of non-contact monitoring should be evaluated and, if found suitable, employed for bridge monitoring and structural health assessment. This study highlights the use of infrasound monitoring, a geophysical technique utilizing acoustics below 20 Hz, as one possible solution for non-contact, non-line-of-sight infrastructure health monitoring. This dissertation focuses on the technique of infrasound for infrastructure monitoring (bridges are of primary interest) beginning with a literature review and an overview of current operational considerations for infrasound for infrastructure monitoring developed at the U.S. Army Engineer Research and Development Center. A meta-analysis of bridge vibrational characteristics was completed following identification of a gap in the knowledge base in this area. This completed meta-analysis compared vibrational characteristics across multiple bridge types and construction materials to determine applicability of infrasound for detection and monitoring of each bridge type. With these considerations in mind, an experimental series involving a steel, two-girder bridge in northern California was completed using infrasound to detect natural modes of the structure and validated by on-structure accelerometers. The non-contact nature of this structural assessment approach has potential to supplement traditional structural assessment techniques as affordable, remote, persistent monitoring of transportation infrastructure. Upon completion of the original experimental series, the data were used to investigate the possibility of wide area monitoring using infrasound, including possible limitations and boundaries. Overall implications for use of this technology are also discussed for a multiple infrastructure types.

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The identification of a structural system deterministically or probabilistically is a topic of considerable interest and importance for its condition assessment and prediction. Many identification approaches, however, require the input which is not always available. Specifically, it may be impossible to know the input or, alternately, the measurement of the input is much more unreliable than the dynamic state measurement. Along these lines, engineers try to extract as much information as possible from the available output data to reduce the need for knowing the input. Three new methodologies are developed here to address this challenge. Initially, the input-parameter-state estimation capabilities of a novel unscented Kalman filter, for real time monitoring applications, is examined on both linear and nonlinear systems. The unknown input is estimated in two stages within each time step. Firstly, the predicted dynamic states and the system's parameters provide an estimation of the input. Secondly, the corrected with measurements (updated) dynamic states and parameters provide a final input estimation for the current time step. Subsequently, the estimation of the dynamic states, the parameters, and the input of systems subjected to wind loading is examined using a sequential Kalman filter. The procedure considers two Kalman filters in order to estimate initially the dynamic states using kinematic constraints, and subsequently the system parameters along with the input, in an online fashion. Finally, the input-parameter-state estimation capabilities of a new residual-based Kalman filter are examined for both complete and limited output information conditions. The filter is based on the residual of the predicted and measured dynamic state output, as well as on the residual of the system model estimation. The considered sensitivity analysis is developed using a real time sensitivity matrix formulated by the filtered dynamic states.