Objective condition assessment is essential to make better decisions for safety and serviceability of existing civil infrastructure systems. This study explores the condition of an existing transit guideway system that has been in service for thirty-five years. The structural system is composed of six-span continuous prestressed concrete bridge segments. The overall transit system incorporates a number of continuous bridges which share common design details, geometries, and loading conditions. The original analysis is based on certain simplifying assumptions such as rigid behavior over supports and simplified tendon/concrete/steel plate interaction. The current objective is to conduct a representative study for a more accurate understanding of the structural system and its behavior. The scope of the study is to generate finite element models (FEMs) to be used in static and dynamic parameter sensitivity studies, as well load rating and reliability analysis of the structure. The FEMs are used for eigenvalue analysis and simulations. Parameter sensitivity studies consider the effect of changing critical parameters, including material properties, prestress loss, and boundary and continuity conditions, on the static and dynamic structural response. Load ratings are developed using an American Association for State Highway Transportation Officials Load and Resistance Factor Rating (AASHTO LRFR) approach. The reliability of the structural system is evaluated based on the data obtained from various finite element models. Recommendations for experimental validation of the FEM are presented. This study is expected to provide information to make better decisions for operations, maintenance and safety requirements; to be a benchmark for future studies, to establish a procedure and methodology for structural condition assessment, and to contribute to the general research body of knowledge in condition assessment and structural health monitoring.

Bridges are critical components of highway networks, which provide mobility and economical vitality to a nation. Ensuring the safety and regular operation as well as accurate structural assessment of bridges is essential. Structural Identification (St-Id) can be utilized for better assessment of structures by integrating experimental and analytical technologies in support of decision-making. St-Id is defined as creating parametric or nonparametric models to characterize structural behavior based on structural health monitoring (SHM) data. In a recent study by the ASCE St-Id Committee, St-Id framework is given in six steps, including modeling, experimentation and ultimately decision making for estimating the performance and vulnerability of structural systems reliably through the improved simulations using monitoring data. In some St-Id applications, there can be challenges and considerations related to this six-step framework. For instance not all of the steps can be employed; thereby a subset of the six steps can be adapted for some cases based on the various limitations. In addition, each step has its own characteristics, challenges, and uncertainties due to the considerations such as time varying nature of civil structures, modeling and measurements. It is often discussed that even a calibrated model has limitations in fully representing an existing structure; therefore, a family of models may be well suited to represent the structure’s response and performance in a probabilistic manner. The principle objective of this dissertation is to investigate nonparametric and parametric St-Id approaches by considering uncertainties coming from different sources to better assess the structural condition for decision making. In the first part of the dissertation, a nonparametric StId approach is employed without the use of an analytical model. The new methodology, which is iv successfully demonstrated on both lab and real-life structures, can identify and locate the damage by tracking correlation coefficients between strain time histories and can locate the damage from the generated correlation matrices of different strain time histories. This methodology is found to be load independent, computationally efficient, easy to use, especially for handling large amounts of monitoring data, and capable of identifying the effectiveness of the maintenance. In the second part, a parametric St-Id approach is introduced by developing a family of models using Monte Carlo simulations and finite element analyses to explore the uncertainty effects on performance predictions in terms of load rating and structural reliability. The family of models is developed from a parent model, which is calibrated using monitoring data. In this dissertation, the calibration is carried out using artificial neural networks (ANNs) and the approach and results are demonstrated on a laboratory structure and a real-life movable bridge, where predictive analyses are carried out for performance decrease due to deterioration, damage, and traffic increase over time. In addition, a long-span bridge is investigated using the same approach when the bridge is retrofitted. The family of models for these structures is employed to determine the component and system reliability, as well as the load rating, with a distribution that incorporates various uncertainties that were defined and characterized. It is observed that the uncertainties play a considerable role even when compared to calibrated model-based predictions for reliability and load rating, especially when the structure is complex, deteriorated and aged, and subjected to variable environmental and operational conditions. It is recommended that a family-of-models approach is suitable for structures that have less redundancy, high operational importance, are deteriorated, and are performing under close capacity and demand levels

Structural Health Monitoring (SHM) is the sensing and analysis of a structure to detect abnormal behavior, damage and deterioration during regular operations as well as under extreme loadings. SHM is designed to provide objective information for decision-making on safety and serviceability. This research focuses on the SHM of bridges by developing and integrating novel methods and techniques using sensor networks, computer vision, modeling for damage indices and statistical approaches. Effective use of traffic video synchronized with sensor measurements for decision-making is demonstrated. First, some of the computer vision methods and how they can be used for bridge monitoring are presented along with the most common issues and some practical solutions. Second, a conceptual damage index (Unit Influence Line) is formulated using synchronized computer images and sensor data for tracking the structural response under various load conditions. Third, a new index, Nd , is formulated and demonstrated to more effectively identify, localize and quantify damage. Commonly observed damage conditions on real bridges are simulated on a laboratory model for the demonstration of the computer vision method, UIL and the new index. This new method and the index, which are based on outlier detection from the UIL population, can very effectively handle large sets of monitoring data. The methods and techniques are demonstrated on the laboratory model for damage detection and all damage scenarios are identified successfully. Finally, the application of the proposed methods on a real life structure, which has a monitoring system, is presented. It is shown that these methods can be used efficiently for applications such as damage detection and load rating for decision-making. The results from this monitoring project on a movable bridge are demonstrated and presented along with the conclusions and recommendations for future work.

This study investigated the effect of sensor mass, sensor location, and delamination location of different composite structures under dynamic loading. The study pertains to research of the use of accelerometers and dynamic response as a cost-effective and reliable method of structural health monitoring in composite structures. The composite structures in this research included carbon fiber plates, carbon fiber-foam sandwich panels, and carbon-fiber honeycomb sandwich panels. The composite structures were manufactured with the use of a Tetrahedron MTP-8 heat press. All work was conducted in the Cal Poly Aerospace Structures/Composites Laboratory. Initial delaminations were placed at several locations along the specimen, including the bending mode node line locations. The free vibration of the composite structure was forced through a harmonic horizontal vibration test using an Unholtz-Dickie shake system. A sinusoidal sweep input was considered for the test. The dynamic response of the composite test specimens were measured using piezoelectric accelerometers. Measurements were taken along horizontal and vertical locations on the surfaces of the composite structures. Square inch grids were marked on the surfaces to create a meshed grid system. Accelerometer measurements were taken at the center of the grids. The VIP Sensors 1011A piezoelectric accelerometer was used to measure vibration response. The measurements were then compared to response measurements taken from a PCB Piezotronics 353B04 single access accelerometer to determine the effects of sensor mass. Deviations in bending mode natural frequency and differences in mode shape amplitude became the criteria for evaluating the effect of sensor mass, sensor location, and delamination location. Changes in damping of the time response were also studied. The experimental results were compared to numerical models created using a finite element method. The experimental results and numerical values were shown to be in good agreement. The sensor mass greatly affected the accuracy and precision of vibration response measurements in the composites structures. The smaller weight and area of the VIP accelerometer helped to minimize the decrease in accuracy and precision due to sensor mass. The effect of sensor location was found to be coupled with the effect of sensor mass and the bending mode shape. The sensor location did not affect the vibration response measurements when the sensor mass was minimized. Off-center horizontal sensor placement showed the possibility of measuring vibration torsion modes. The effect of delamination changed the bending mode shape of the composite structure, which corresponded to a change in natural frequency. The greatest effect of the delamination was seen at the bending mode node lines, where the bending mode shape was most significantly affected. The effect of delamination was also dependent on the location of the delamination and the composite structure type. The results of this study provided considerations for future research of an active structural health monitoring system of composite structures using dynamic response measurements. The considerations included sensor mass reduction, sensor placement at constraints and bond areas and the presence of damping material.

This thesis focuses on different methods to analyze power transmission systems with computer software to aid in detection of faulty or damaged systems. It is split into three sections. The first section involves utilizing finite element software to analyze gear stiffness and stresses. A quasi-static and dynamic analysis are done on two sets of fixed axis spur gears and a planetary gear system using ABAQUS to analyze the stress, strain and gear mesh stiffness variation. In the second section, the vibrational patterns produced by a simple bevel gear system are investigated by an experiment and by dynamic modeling in ADAMS. Using a Fast Fourier Transform (FFT) on the dynamic contact forces, a comprehensive frequency-domain analysis will reveal unique vibration spectra at distinct frequencies around the gear mesh frequencies, their super- and sub- harmonics, and their side-band modulations. ADAMS simulation results are then compared with the experimental results. Constraints, bearing resistant torques, and other key parameters are applied as closely as possible to real operating conditions. The third section looks closely at the dynamic contact forces of a practical two-stage planetary gear. Using the same FFT approach in the second section, a frequency-domain analysis will reveal distinct frequencies around both the first-stage and the second-stage gear mesh frequencies, and their harmonics. In addition, joint time-frequency analysis (JTFA) will be applied to damaged and undamaged planetary gear systems with transient start-up conditions to observe how the frequency contents of the contact force evolve over time.

Structural Health Monitoring (SHM) is a non-destructive evaluation tool that assesses the functionality of structural systems that are used in the civil, mechanical and aerospace engineering practices. A much desirable objective of a SHM system is to provide a continuous monitoring service at a minimal cost with ability to identify problems even in inaccessible structural components. In this dissertation, several such approaches that utilize the measured dynamic response of structural systems are presented to detect, locate, and quantify the damages that are likely to occur in structures. In this study, the structural damage is identified as a reduction in the stiffness characteristics of the structural elements. The primary focus of this study is on the utilization of measured dynamic strains for damage identification in the framed structures which are composed of interconnected beam elements. Although linear accelerations, being more convenient to measure, are commonly used in most SHM practices, herein the strains being more sensitive to elemental damage are considered. Two different approaches are investigated and proposed to identify the structural element stiffness properties. Both approaches are mode-based, requiring first the identification of system modes from the measured strain responses followed by the identification of the element stiffness coefficients. The first approach utilizes the Eigen equation of the finite element model of the structure, while the second approach utilizes the changes caused by the damage in the structural curvature flexibilities. To reduce size of the system which is primarily determined by the number of sensors deployed for the dynamic data collection, measurement sensitivity-based sensor selection criterion is observed to be effective and thus used. The mean square values of the measurements with respect to the stiffness coefficients of the structural elements are used as the effective measures of the measurement sensitivities at different sensor locations. Numerical simulations are used to evaluate the proposed identification approaches as well as to validate the sensitivity-based optimal sensor deployment approach. / Ph. D. / All modern societies depend heavily on civil infrastructure systems such as transportation systems, power generation and transmission systems, and data communication systems for their day-to-day activities and survival. It has become extremely important that these systems are constantly watched and maintained to ensure their functionality. All these infrastructure systems utilize structural systems of different forms such as buildings, bridges, airplanes, data communication towers, etc. that carry the service and environmental loads that are imposed on them. These structural systems deteriorate over time because of natural material degradation. They can also get damaged due to excessive load demands and unknown construction deficiencies. It is necessary that condition of these structural systems is known at all times to maintain their functionality and to avoid sudden breakdowns and associated ensuing problems. This condition assessment of structural systems, now commonly known as structural health monitoring, is commonly done by visual onsite inspections manually performed at pre-decided time intervals such as on monthly and yearly basis. The length of this inspection time interval usually depends on the relative importance of the structure towards the functionality of the larger infrastructure system. This manual inspection can be highly time and resource consuming, and often ineffective in catching structural defects that are inaccessible and those that occur in between the scheduled inspection times and dates. However, the development of new sensors, new instrumentation techniques, and large data transfer and processing methods now make it possible to do this structural health monitoring on a continuous basis. The primary objective of this study is to utilize the measured dynamic or time varying strains on structural components such as beams, columns and other structural members to detect the location and level of a damage in one or more structural elements before they become serious. This detection can be done on a continuous basis by analyzing the available strain response data. This approach is expected to be especially helpful in alerting the owner of a structure by identifying the iv occurrence of a damage, if any, immediately after an unanticipated occurrence of a natural event such as a strong earthquake or a damaging wind storm.

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<p dir="ltr">In pursuit of enhanced fuel economy, the automotive industry is exploring the substitution of metal components with lightweight polymer composites. These components must withstand elevated static loading and crash performance, while ideally offering added functionalities and reduced weight. To tackle these challenges, this research presents an innovative manufacturing method aimed at reducing costs and cycle times associated with continuous fiber polymer composites. This method involves producing a linear thermoplastic composite rod known as M-TOW (Multi-tow), which can be molded into intricate shapes to serve as tailored structural reinforcement in hybrid-molded parts. The research encompasses the processing of M-TOW, with a focus on predicting consolidation using Darcy’s law, integrating functional components for thermal and electrical conductivity using overbraided metallic wire or sensing using optical fibers, and its application in real-world scenarios. These advancements showcase the versatility and potential of M-TOW in high-rate continuous fiber manufacturing, paving the way for multifunctional hybrid molded structures.<br><br></p>

The continuous integration of wireless communication systems in medical and healthcare applications has made the actualisation of reliable healthcare applications and services for patient care and smart home a reality. Diverse indoor approaches are sought to improve the quality of living and consequently longevity. The research centres on the development of smart healthcare solutions using various indoor technologies and techniques for active and assisted living. At first, smart health solutions for ambient and wearable assisted living in smart homes are sought. This requires a detailed study of indoor localisation. Different indoor localisation technologies including acoustic, magnetic, optical and radio frequency are evaluated and compared. From the evaluation, radio frequency-based technologies, with interest in wireless fidelity (Wi-Fi) and radio frequency identification (RFID) are isolated for smart healthcare. The research focus is sought on auto-identification technologies, with design considerations and performance constraints evaluated. Moreover, the design of various antennas for different indoor technologies to achieve innovative healthcare solutions is of interest. First, a meander line passive RFID tag antenna resonating at the European ultra-high frequency is designed, simulated and evaluated. Second, a frequency-reconfigurable patch antenna with the capability to resonate at ten distinct frequencies to support Wi-Fi and worldwide interoperability for microwave access applications is designed and simulated. Afterwards, a low-profile, lightweight, textile patch antenna using denim material substrate is designed and experimentally verified. It is established that, by loading proper rectangular slots and introducing strip lines, substantial size antenna miniaturisation is achieved. Further, novel wearable and ambient methodologies to further ameliorate smart healthcare and smart homes are developed. Machine learning and deep learning methods using multivariate Gaussian and Long short-term memory recurrent neural network are used to experimentally validate the viability of the new approaches. This work follows the construction of the SmartWall of passive RFID tags to achieve non-invasive data acquisition that is highly unobtrusive. / Tertiary Education Trust Fund (TETFund) of the Federal Government of Nigeria

This work is focusing to study the structural behavior of Composite Overwrapped Pressure Vessels (COPVs). These COPVs are found in many engineering applications. In the aerospace field, they are installed onto spaceships and aid the reorientation of the spacecraft in very far and airless, therefore frictionless, orbits to save energy and fuel. The intent of this research is to analyze the difference in performance of both perfectly intact and purposely damaged tanks. Understanding both the source and location of a structural fault will help NASA engineers predict the performance of COPVs subject to similar conditions, which could prevent failures of important missions. The structural behavior of six tanks is investigated by means of experimental modal analysis. Knowledge of statistical signal processing methods allows to sort out and extract meaningful features from the data as to gain understanding of the performance of the structures. Structural identification is carried out using Narrow Band and Broad Band algorithms. A comparison through correlation tables and figures presents the differences in natural frequencies, mode shapes and damping ratios of all structures. A careful analysis displays the deviation of these modal parameters in the damaged tanks, highlighting the evident structural defects.