Structures can get damaged by severe events such as earthquakes and hurricanes or deteriorate over time. Therefore the need to find cost-effective and reliable inspection and monitoring solutions for ...structures such as bridges, wind turbines, and buildings is important. Structural Health Monitoring (SHM) is the process of using damage detection and characterization techniques to determine whether a structure is in a healthy state or a damaged state.Damage localization and quantification, collectively referred to as damage characterization, can be addressed as a parameter estimation problem. In this setting, the location and extent of damage are inferred from the model parameters that are estimated from features extracted from the measurements. The measurements are collected from the sensors. For success, the features from the measurements must be sensitive to the parameters and have low variability to non-damage-related changes. Eigenvalues can be measured more precisely than eigenvectors and, for this reason, are widely used as features for damage characterization.An issue in using eigenvalues only for parameter estimation is that the number of eigenvalues extracted from the measurements can often be less than the number of model parameters that are candidates for updating, making the problem under-determined. While the number of candidate parameters is large, one expects that only a few will change due to damage. A solution that has only a few non-zeros is called sparse. Sparsity has been added as a constraint to the under-determined parameter estimation problem to obtain a solution that will likely be aligned with what happens in reality. Sparsity has been exploited in the last ten years or so using a linearized approximation. In this dissertation, the error resulting from the linear approximation is examined, and approaches that consider the nonlinearity are presented.Feature selection for parameter estimation is another item that is treated in this dissertation. Output feedback control has been used to increase the sensitivity of the eigenvalues to parameters in the last twenty years. Gains have been designed to obtain closed-loop systems with eigenvalues that have more sensitivity to damage. Applying output feedback to a structure requires that the structure is equipped with controllers, which can be a limitation. Virtual output feedback, however, only requires measuring the open-loop input-output data. The closed-loop matrix is formed offline after system identification. Virtual output feedback can be used for feature selection. It is shown that replacing the open-loop eigenvalues with more sensitive closed-loop eigenvalues will also increase their variability. Notwithstanding, Virtual output feedback can still be used to use multiple sets of closed-loop eigenvalues instead of open-loop eigenvectors. This is shown to provide better conditioning in the parameter estimation problem and more robustness to noise.This dissertation presents a damage detection method based on nonlinear output feedback as the last item. Unlike the virtual output feedback, hardware and controllers are required to perform the tests in this approach. The objective is to announce whether the structure is damaged or non-damaged by observing a feature from the nonlinear system. Using nonlinear output feedback in a linear system will generate a nonlinear closed-loop system. Nonlinear systems have features that do not exist in linear systems. We used the period of a Limit Cycle (LC) for damage detection. The limit cycle is obtained by applying the nonlinear feedback law at the point of actuation in the structure. The sensitivity of the period of the limit cycle is orders of magnitude larger than the change of period in the open-loop setting while showing robustness to non-damaged related variabilities such as noise, environmental changes, and model error.
Corrosion of rebar is considered the leading cause of deterioration in concrete structures. When steel rust, it occupies larger volume and creates pressure on the concrete, leading to cracking and ...spalling. As the cross-section is being compromised, the structural capacity of the reinforced concrete structure is reduced. Corrosion is a slow natural electrochemical process that affects the long-term performance. However, sometimes premature catastrophic failure occurs due to corrosion, especially in chloride environments. Moreover, the cost of repair and replacement of deteriorated structures has become a major liability for management agencies. Consequently, it is important to determine the level of corrosion at early stages to minimize maintenance costs. While visual inspection is the simplest way to identify the corrosion, it is only useful when corrosion products reach their threshold value, not at the initiation stage of corrosion. Conversely, electrochemical methods, such as Half-Cell Potential (HCP) and Linear Polarize Resistance (LPR), can locate corrosion activity, but they are quasi-non-destructive methods. On the other hand, Ground Penetrating Radar (GPR) detects corrosion through its reflective wave in the form of maximum amplitude and two-way travel time. Apart from GPR, iCOR is also able to detect corrosion activities, such as corrosion rate, and concrete electrical resistivity, in a short time without any physical connection to rebars. iCOR, moreover, uses Connectionless Electrical Pulse Response Analysis (CEPRA) method. The current study was designed to evaluate the difference in Non-Destructive Evaluation (NDE) data between non-corroded and corroded concrete beams and to quantifying the amount of corrosion that occurred in the steel rebars in concrete. A total 36 beams of 36 in. (914 mm) X 15 in. (381 mm) X 8 in. (203 mm) sizes with 4 design parameters were prepared for the present study. The parameters were concrete clear cover, rebar diameter, concrete strength and porosity, and duration of corrosion. For concrete clear cover, ranges from 1.5 in. (38 mm), 2 in. (50 mm), and 3 in. (75 mm) were considered as these are the required clear covers provided in ACI 318-19 code for interior beams (building), bridge deck and structure exposed to soil respectively. Three different rebar diameters 0.375 in. (10 mm), 0.875 in. (22 mm), and 1.25 in. (32 mm) were selected. Concrete compressive strength of 2.7 ksi (18.62 MPa) with high porosity 4.5% and 6.8 ksi (46.88 MPa) with low porosity of 1.5% were additionally selected as one of the parameters. Furthermore, corrosion durations of 10, 20, and 30 days were selected. Accelerated corrosion (induced current) technique was used to corrode steel rebars faster. As components of accelerated corrosion direct power source, anode (steel rebars), cathode (stainless steel), and electrolyte solution (5% NaCl solution) were incorporated. Next, after each 10-day interval, all of the beams were scanned with both GPR and iCOR devices. The thickness of the concrete cover, the size of the embedded rebars, the age of the building, and the concrete's compressive strength were all shown to have a substantial effect on the change of the concrete rusting. From the scan result analysis, it was evident that the reflected amplitudes rose when corrosion levels increased, concrete coverings decreased, rebar diameters increased, and concrete strength decreased. In addition, corrosion rates and potentials were decreased by increasing corrosion duration, rebar diameter, concrete strength, and concrete cover. In contrast, as the concrete resistivity increased, corrosion period, rebar diameters, and concrete clear cover increased. Furthermore, a statistical analysis was performed using a multivariable regression model to quantify the amount of corrosion. Corrosion in reinforced concrete structures can be measured using this quantitative corrosion degradation model, which represented a major advancement. This quantitative study may be used to evaluate the loss of steel rebar cross-section, allowing for the quick and nondestructive way of determination of the remaining capacity of the reinforced concrete structures.
This thesis concerns the unique flow structure around bridge piers under the wave and current conditions. The flow characteristics around piers may change depending on the flow conditions and ...geometry of the pier. These flow structures imprint themselves on the streambed as scour holes. Laboratory experiments were conducted to investigate the changes that occur in the flow pattern and in the geometric pattern of scour holes around piers in waterbodies subject to waves and currents. Physical models of two piers with different diameters (19 mm and 50 mm) were built and installed in a sediment bed. A total of 19 sets of experiments were conducted on three types of flow conditions: waves alone; current alone; and waves and current combined, in which the direction of the wave propagation was opposite the direction of the current. The effects of variables such as flow velocity, water depth, wave height, wave period, and pier size were studied to determine their influence on the geometry of scour holes. The data obtained from the laboratory experiments showed that scour holes were largest in flow with a steady current and smallest in water with only waves. The combination of waves and current produced scour depths larger than those of the waves-alone experiments but smaller than those conducted in water with only currents. Particle image velocimetry (PIV) was utilized to visualize the flow structure around piers under various flow conditions. A horizontal plane view of the flow field was analyzed and changes in vortex characteristics were investigated. Higher flow velocity produced stronger vortices in a steady current. For the waves-alone cases, when the pier was smaller in diameter, more scour was observed. The flow structure in the combined waves and current experiments was much more complex, although the flow characteristics were dominated by the current, the flow pattern around the pier was affected by the wave characteristics as well. Velocity vectors obtained from the PIV analysis showed that the mean displacement of sediment particles was in the same direction as the current. The presence of negative velocity was also observed when the wave motion was against the current. Finally, an attempt was made to relate the size of the vortices to the scour process. A clear-water flow regime was designed to ensure that the sediment movement that occurred during the experiments was caused by the pier in the sediment bed disturbing the flow structure. The results showed that the strength of wake vortices, the relative direction of the flow, and the distances of waves from the channel bottom influenced the scour pattern around the cylindrical pier and the downstream deposition pattern.
Research over the past ten years has generated an increased interest in studying elastic structural instabilities as a useful response for smart applications rather than a failure. Buckling under ...axial compression is a type of structural instability that can be used for rapid geometric transformations (switching) and energy harvesting applications, if the deformations arising from buckling are properly controlled. Controlling transverse deformations due to buckling in slender elements usually needs external constraints/boundaries. Short thin-walled cylinders can experience several elastic buckling events under axial compression without additional constraints. However, predicting the post-buckling response in cylinders is very challenging, particularly far in the post-buckling regime since they are highly sensitivity to initial imperfections.The concept of cylinders with non-uniform stiffness distribution (NSD) was recently proposed to localize a cylinder’s buckling events in targeted zones. This notion has been proven effective for controlling the number of elastic buckling events, the sequence at which they occur, and the regions experiencing buckling. However, this information is not enough to design NSD cylinders for smart applications, which requires being able to predict the actual applied force for each buckling event, the end shortening of the cylinder for the buckling event, the drop in force, the drop in strain energy, and the post-buckling stiffness of the cylinder.Here, a semi-analytical model has been developed to predict the elastic post-buckling response of NSD cylinders under compression. The developed semi-analytical model is based on three general steps:• Separate the NSD cylinder into parallel segments,• Simplify and predict the response of each segment, and• Integrate the response of individual segments.The first step in predicting the elastic post-buckling response of a cylindrical segment was to simplify its geometry into a cylindrical panel with uniform thickness. Linear springs are connected to the top and bottom of the uniform cylinder to match the stiffness of the simplified segment to the actual one. Based on classical shell theory, the elastic post-buckling response of a cylindrical panel is solved as a boundary value differential equation using the pseudo-arclength method. Comparing the post-buckling response of four cylinders from the proposed semi-analytical model with the response of the same cylinders from the experiment and finite element analysis showed the effectiveness of the proposed model. Results from the proposed model predict well the axial deformation and force level corresponding to buckling events more accurately than the post-buckling stiffness.The response of cylindrical panels for a large variety of dimensions is needed to design NSD cylinders for targeted post-buckling behavior. Thus, the classic differential equation of the cylindrical panels under axial compression was solved independently of the material's cylinder radius and elastic modulus. These results allowed the development of design maps for several post-buckling responses such as axial strain and stresses corresponding to the first buckling event, force, and energy drops from the buckling event, the secondary (or post-buckling) stiffness of the panel, the radial deformation at the panel center, and the maximum von Mises stress in the panel. By using genetic programming, predictive equations were developed for each design parameter to relate it to the geometry of the panels.Three cylinders were designed using the developed design maps to validate the proposed approach. One NSD cylinder was designed to undergo several buckling events under compression at pre-defined end shortenings. A second NSD cylinder was designed to feature a post-buckling force-deformation response that plateaus at a constant force level. The third cylinder was designed to experience the same force drop at each buckling event and in identical axial end shortenings after the first event. Finite element analyses of the designed cylinders verified that using the proposed design procedure using the developed design maps provides NSD cylinders with a post-buckling response that is very close to the desired one, and the ultimate design goal can be achieved by slight modifications to the geometry of the cylinder.This study advances the knowledge on the elastic buckling and post-buckling response of slender cylindrical shells under axial compression and provides an approach to analyze and design them for a desired far post-buckling response. The proposed framework, which combines the notion of decomposing NSD cylindrical segments into linear and nonlinear springs in series, a semi-analytical model for NSD equivalent panels, and design maps for several nonlinear responses provides insight for designing these elements for smart devices and structures relying on structural instabilities. This work expands the harnessing of elastic instabilities to the area of thin-shell buckling under compression, which has received less attention in comparison to other forms of structural instability.
Cantilevered overhead sign structures (COSS) are susceptible to fatigue at many of their connection details, in particular the connection between the pole and mast-arm, due to stresses caused by ...natural wind gusts, truck-induced wind gusts, and galloping. There have been cases of structures within the Kansas Department of Transportation (KDOT) inventory, failing at the gusseted box connection, which is the connection detail utilized by KDOT to connect the pole and mast-arm. Behavior and fatigue life of these connection details are not well understood, which limits the ability to identify which structures should be considered for repair, retrofit, or replacement.This research was focused on analyzing behavior of the box connection used in COSS. A finite element analysis investigation was conducted on 21 different COSS based on KDOT designs to investigate the effects of changing geometry of the structure and the impact of switching from a gusseted box connection to a ring-stiffened box connection.Findings from the finite element analyses included: 1) thickening the mast-arm and pole aids in decreasing stresses experienced by the box connection, 2) as pole thickness increases, peak demands shift to the mast-arm socket connection for out-of-plane loading and to the baseplate socket connection for in-plane loading, and 3) utilization of the ring-stiffened box connection decreased stresses at the box connection for both out-of-plane and in-plane loading.
This dissertation develops uncertainty quantification methodologies for modeling, response analysis and optimization of diverse dynamical systems. Two distinct application platforms are considered ...pertaining to engineering dynamics and precision medicine. First, the recently developed Wiener path integral (WPI) technique for determining, accurately and in a computationally efficient manner, the stochastic response of diverse dynamical systems is employed for solving a high-dimensional, nonlinear system of stochastic differential equations governing the dynamics of a representative model of electrostatically coupled micromechanical oscillators. Compared to alternative modeling and solution treatments in the literature, the current development exhibits the following novelties: a) typically adopted linear, or higher-order polynomial, approximations of the nonlinear electrostatic forces are circumvented; and b) stochastic modeling is employed, for the first time, by considering a random excitation component representing the effect of diverse noise sources on the system dynamics. Further, the WPI technique is enhanced and extended based on a Bayesian compressive sampling (CS) treatment. Specifically, sparse expansions for the system response joint PDF are utilized. Next, exploiting the localization capabilities of the WPI technique for direct evaluation of specific PDF points leads to an underdetermined linear system of equations for the expansion coefficients. Furthermore, relying on a Bayesian CS solution formulation yields a posterior distribution for the expansion coefficient vector. In this regard, a significant advantage of the herein-developed methodology relates to the fact that the uncertainty of the response PDF estimates obtained by the WPI technique is quantified. Also, an adaptive scheme is proposed based on the quantified uncertainty of the estimates for the optimal selection of PDF sample points. This yields considerably fewer boundary value problems to be solved as part of the WPI technique, and thus, the associated computational cost is significantly reduced. Second, modeling and analysis of the physiological mechanism of dynamic cerebral autoregulation (DCA) is pursued based on the concept of diffusion maps. Specifically, a state-space description of DCA dynamics is considered based on arterial blood pressure (ABP), cerebral blood flow velocity (CBFV), and their time derivatives. Next, an eigenvalue analysis of the Markov matrix of a random walk on a graph over the dataset domain yields a low-dimensional representation of the intrinsic dynamics. Further dimension reduction is made possible by accounting only for the two most significant eigenvalues. The value of their ratio indicates whether the underlying system is governed by active or hypoactive dynamics, indicating healthy or impaired DCA function, respectively. The reliability of the technique is assessed by considering healthy individuals and patients with unilateral carotid artery stenosis or occlusion. It is shown that the proposed ratio of eigenvalues can be used as a reliable and robust biomarker for assessing how active the intrinsic dynamics of the autoregulation is and for indicating healthy versus impaired DCA function. Further, an alternative joint time-frequency analysis methodology based on generalized harmonic wavelets is utilized for assessing DCA performance in patients with preeclampsia within one week postpartum, which is associated with an increased risk for postpartum maternal cerebrovascular complications. The results are compared with normotensive postpartum individuals and healthy non-pregnant female volunteers and suggest a faster, but less effective response of the cerebral autoregulatory mechanism in the first week postpartum, regardless of preeclampsia diagnosis.
Hydrological models are powerful tools that simulate the natural hydrological cycle and natural processes like surface runoff, groundwater flow, and evapotranspiration, which are needed to be ...understood and quantified for a wide range of applications like water resource management, climate change impact assessment, flood studies and water quality assessment. Errors and uncertainties are bound to creep into the modelling process because of various reasons like incomplete understanding and representation of the natural phenomena, model errors, approximation errors, and parameter uncertainties. This study aims to efficiently quantify the parameter uncertainty by making use of surrogate modeling techniques. There is an inherent trade-off between model complexity and the parameter uncertainty i.e., parameter uncertainty usually increases if complex hydrological model with high model accuracy is employed, but the required computational effort would increase significantly. Considering this tradeoff, one lumped, conceptual model (HYMOD) and one semi-distributed, process-based model (SWAT) for a small (179 km2) and mid-sized (2318 km2) watershed are considered for this study. Hydrological modelling processes are frequently hampered by computationally costly simulations. Consequently, modellers can opt a surrogate model which is a machine learning model that approximates another model but requires less computational effort. This thesis uses Polynomial Chaos Expansion (PCE) method of surrogacy which represents an accurate approximation of the model as the sum of carefully selected polynomials, each separately weighted. The aim of this study is to use Polynomial Chaos Expansion to 1) Improve the calibration and uncertainty assessment procedure for HYMOD, and 2) Quantify parameter uncertainties in SWAT
Ultra-High Performance Concrete (UHPC) is an advanced concrete material with superior mechanical strength, high tensile ductility, and exceptional durability, including negligible permeability. ...Despite these excellent material properties, the use of UHPC in structural applications is limited because of the high cost of commercially available UHPC products. Therefore, developing non-proprietary UHPC mixtures using local materials is a viable way to reduce the initial cost of UHPC. In this research, sustainable and cost-effective UHPC mixtures were developed using locally sourced materials so that UHPC may be more affordable to a wider variety of applications. Specifically, locally available Type I-II cement, local sand with a top size of 4.75 mm (0.187 inch), high range water reducing admixture, domestic steel fibers, and Class F fly ash were used in this research. These material selections improved the sustainability of UHPC. Two final mixtures (plain and fiber reinforced) were recommended as the UHPC mixtures. The greatest compressive strengths obtained in this research were 138.1 MPa (20,030 psi) for plain UHPC and 165.8 MPa (24,030 psi) for fiber reinforced mixture. The greatest flexural strengths were 12.95 MPa (1,230 psi) and 14.35 MPa (2,080 psi) for plain and fiber reinforced UHPC, respectively. After developing the UHPC mixtures, physical properties: drying shrinkage and permeable porosities of plain and fiber reinforced mixtures were investigated. The drying shrinkage of plain UHPC was greater than that of fiber reinforced UHPC by 27%. The lowest permeable porosities obtained were 2.4% and 2.6% for plain and fiber reinforced mixtures, respectively. In addition to mechanical and physical properties, the durability of UHPC mixtures was also studied. Rapid chloride permeability testing was performed on plain UHPC which showed very low chloride ion penetrability. Resistance of plain and fiber reinforced UHPC mixtures to freeze-thaw cycles was studied and observed no damage due to frost action with a durability factor of 100. Additionally, sulfate resistance of the final two mixtures was evaluated by submerging the specimens under 5% sodium sulfate for one year. The maximum expansions in plain and fiber reinforced specimens were 0.07% and 0.06%, respectively, which indicated excellent resistance to sulfate attack.
Current U.S. energy policy, which includes wind and solar incentives, favors the increased use of renewables for power generation. Renewable energy incentives and falling technology costs support ...robust growth in renewable energy resources as shares of coal and nuclear power decrease. It is projected by the U.S. Energy Information Agency that renewable generation will supply 44% of U.S. electricity by 2050. For wind energy, the International Electrotechnical Commission (IEC) defines four IEC Wind Classes – Class I (High Wind), Class II (Medium Wind), Class III (Low Wind), and Class IV (Very Low Wind). Most utility-scale wind farms are constructed at Class II or higher sites where average annual wind speeds at hub height are 8.5 m/s or greater. To expand wind power to states like Florida, which is typically third in the nation in overall electricity consumption, it will be necessary to develop wind power at an increasing number of low and very low wind sites. One way this development can take place is by implementing new technologies. The Department of Energy (DOE), in Enabling Wind Power Nationwide, states that “near-future turbines” – defined as conceptual wind turbines with rotor diameters of 124 m, turbine nameplate capacities of 1.8 MW, and hub heights of 140 m – have the potential to bring wind energy to all 50 states. This research examined ten years of hourly wind speed data collected at Florida airports to determine temporal and spatial characteristics of the wind resource. It was found that for low and very low wind regions with large numbers of calm or sub-threshold wind speed measurements, Weibull probability distribution functions tend to overestimate wind energy production. Three characteristic histogram shapes were identified to improve estimates. This information was used to determine the wind energy potential for near-future turbines and to develop a conceptual structural support system. In low and very low wind regions, reduced shear turbulence and wind thrust help to reduce fatigue damage on the turbine rotors, components, and support systems. For locations like Florida, located in coastal regions of the Atlantic Hurricane Basin with an average of 12 named storms each year, these advantages are partially offset, as design is typically governed by load cases that include tropical cyclone winds. For turbines supported on shallow circular or octagonal foundations, common in the industry, it is essential to predict the settlement and tilt of an eccentrically loaded foundation with a soil gap to ensure stability. Numerical modeling was performed for foundation diameter-to-thickness ratios of 5, 10, 15, and 20 to verify a new solution procedure that uses existing elastic settlement equations with the equivalent bearing area definition found in Design Guide for Wind Turbines. Differences in modeled and calculated settlements had an MSE of less than 0.15 for all cases. Conservative estimates of differential settlement were found using a 35% increase factor.
A city resilient to hurricanes would not break but will bend from their associated hazards (e.g., wind, rain and surge). This city bending and bouncing back (to some extent) result in increasingly ...significant losses due to the intensified hurricane wind-rain-surge extremes under changing climate and the continued escalation of coastal population density. Therefore, both long-term efforts (e.g., hurricane mitigation and climate adaptation) and short-term efforts (e.g., pre-hurricane preparedness, during-hurricane response and post-hurricane repair) are urgently needed to enhance city resilience by collectively improving the ability of a city to adapt to changing conditions, and to withstand and recover rapidly from disruptions caused by hurricane hazards. This study focuses on the short-term efforts to systematically advance hurricane resilience of urban built environment, where the problem of optimizing stakeholders’ action plans for resilience enhancement is mathematically formulated as a Markov decision process and approached by the deep reinforcement learning algorithms. The component-level preparedness in pre-hurricane phase is first implemented on individual hurricane-sensitive structures to reduce their failure probabilities and enhance city robustness. Specifically, a deep reinforcement learning-based framework of aerodynamic shape and control optimization is built as the foundation of future morphing tall buildings and long-span bridges to mitigate structural response under approaching hurricane wind. Then, the system-level response in during-hurricane phase is performed within an isolated critical infrastructure network to utilize its redundancy and hence allow the city to operate at a lower capacity. Specifically, a deep reinforcement learning-based traffic management system of interdependent transportation infrastructure components is constructed for coordinated relocation of vehicles to minimize travel time and injuries/fatalities under predicted hurricane damages caused by wind, rain and storm surge. Furthermore, the system-of-system-level repair in post-hurricane phase is conducted on interconnected critical infrastructure systems to utilize stakeholders’ resourcefulness and hence enhance restoration rapidity of city functionality. Specifically, a deep reinforcement learning-based repair scheduling system for coupled traffic-electric power networks is designed for planning collaborative actions to accelerate recovery for observed hurricane damages. To improve the training efficiency and model interpretability of deep reinforcement learning algorithms, the knowledge-enhanced machine learning methodology is adopted in this study for effective risk-informed decision making. Finally, a multi-phase multi-scale resilience enhancement framework is developed to investigate the contribution of the individual short-term efforts (involving different structure/infrastructure types in various hurricane life-cycle phases) to the overall resilience performance.
