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- Creators: Jiang, Hanqing
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Description
This research focuses on the benefits of using nanocomposites in aerospace structural components to prevent or delay the onset of unique composite failure modes, such as delamination. Analytical, numerical, and experimental analyses were conducted to provide a comprehensive understanding of how carbon nanotubes (CNTs) can provide additional structural integrity when they are used in specific hot spots within a structure. A multiscale approach was implemented to determine the mechanical and thermal properties of the nanocomposites, which were used in detailed finite element models (FEMs) to analyze interlaminar failures in T and Hat section stringers. The delamination that first occurs between the tow filler and the bondline between the stringer and skin was of particular interest. Both locations are considered to be hot spots in such structural components, and failures tend to initiate from these areas. In this research, nanocomposite use was investigated as an alternative to traditional methods of suppressing delamination. The stringer was analyzed under different loading conditions and assuming different structural defects. Initial damage, defined as the first drop in the load displacement curve was considered to be a useful variable to compare the different behaviors in this study and was detected via the virtual crack closure technique (VCCT) implemented in the FE analysis.
Experiments were conducted to test T section skin/stringer specimens under pull-off loading, replicating those used in composite panels as stiffeners. Two types of designs were considered: one using pure epoxy to fill the tow region and another that used nanocomposite with 5 wt. % CNTs. The response variable in the tests was the initial damage. Detailed analyses were conducted using FEMs to correlate with the experimental data. The correlation between both the experiment and model was satisfactory. Finally, the effects of thermal cure and temperature variation on nanocomposite structure behavior were studied, and both variables were determined to influence the nanocomposite structure performance.
Experiments were conducted to test T section skin/stringer specimens under pull-off loading, replicating those used in composite panels as stiffeners. Two types of designs were considered: one using pure epoxy to fill the tow region and another that used nanocomposite with 5 wt. % CNTs. The response variable in the tests was the initial damage. Detailed analyses were conducted using FEMs to correlate with the experimental data. The correlation between both the experiment and model was satisfactory. Finally, the effects of thermal cure and temperature variation on nanocomposite structure behavior were studied, and both variables were determined to influence the nanocomposite structure performance.
ContributorsHasan, Zeaid (Author) / Chattopadhyay, Aditi (Thesis advisor) / Dai, Lenore (Committee member) / Jiang, Hanqing (Committee member) / Rajadas, John (Committee member) / Liu, Yongming (Committee member) / Arizona State University (Publisher)
Created2014
Description
Advanced composites are being widely used in aerospace applications due to their high stiffness, strength and energy absorption capabilities. However, the assurance of structural reliability is a critical issue because a damage event will compromise the integrity of composite structures and lead to ultimate failure. In this dissertation a novel homogenization based multiscale modeling framework using semi-analytical micromechanics is presented to simulate the response of textile composites. The novelty of this approach lies in the three scale homogenization/localization framework bridging between the constituent (micro), the fiber tow scale (meso), weave scale (macro), and the global response. The multiscale framework, named Multiscale Generalized Method of Cells (MSGMC), continuously bridges between the micro to the global scale as opposed to approaches that are top-down and bottom-up. This framework is fully generalized and capable of modeling several different weave and braids without reformulation. Particular emphasis in this dissertation is placed on modeling the nonlinearity and failure of both polymer matrix and ceramic matrix composites.
ContributorsLiu, Guang (Author) / Chattopadhyay, Aditi (Thesis advisor) / Mignolet, Marc (Committee member) / Jiang, Hanqing (Committee member) / Li, Jian (Committee member) / Rajadas, John (Committee member) / Arizona State University (Publisher)
Created2011
Description
This thesis focuses on the continued extension, validation, and application of combined thermal-structural reduced order models for nonlinear geometric problems. The first part of the thesis focuses on the determination of the temperature distribution and structural response induced by an oscillating flux on the top surface of a flat panel. This flux is introduced here as a simplified representation of the thermal effects of an oscillating shock on a panel of a supersonic/hypersonic vehicle. Accordingly, a random acoustic excitation is also considered to act on the panel and the level of the thermo-acoustic excitation is assumed to be large enough to induce a nonlinear geometric response of the panel. Both temperature distribution and structural response are determined using recently proposed reduced order models and a complete one way, thermal-structural, coupling is enforced. A steady-state analysis of the thermal problem is first carried out that is then utilized in the structural reduced order model governing equations with and without the acoustic excitation. A detailed validation of the reduced order models is carried out by comparison with a few full finite element (Nastran) computations. The computational expedience of the reduced order models allows a detailed parametric study of the response as a function of the frequency of the oscillating flux. The nature of the corresponding structural ROM equations is seen to be of a Mathieu-type with Duffing nonlinearity (originating from the nonlinear geometric effects) with external harmonic excitation (associated with the thermal moments terms on the panel). A dominant resonance is observed and explained. The second part of the thesis is focused on extending the formulation of the combined thermal-structural reduced order modeling method to include temperature dependent structural properties, more specifically of the elasticity tensor and the coefficient of thermal expansion. These properties were assumed to vary linearly with local temperature and it was found that the linear stiffness coefficients and the "thermal moment" terms then are cubic functions of the temperature generalized coordinates while the quadratic and cubic stiffness coefficients were only linear functions of these coordinates. A first validation of this reduced order modeling strategy was successfully carried out.
ContributorsMatney, Andrew (Author) / Mignolet, Marc (Thesis advisor) / Jiang, Hanqing (Committee member) / Spottswood, Stephen (Committee member) / Arizona State University (Publisher)
Created2011
Description
Electronic devices based on various stimuli responsive polymers are anticipated to have great potential for applications in innovative electronics due to their inherent intelligence and flexibility. However, the electronic properties of these soft materials are poor and the applications have been limited due to their weak compatibility with functional materials. Therefore, the integration of stimuli responsive polymers with other functional materials like Silicon is strongly demanded. Here, we present successful strategies to integrate environmentally sensitive hydrogels with Silicon, a typical high-performance electronic material, and demonstrate the intelligent and stretchable capability of this system. The goal of this project is to develop integrated smart devices comprising of soft stimuli responsive polymeric-substrates with conventional semiconductor materials such as Silicon, which can respond to various external stimuli like pH, temperature, light etc. Specifically, these devices combine the merits of high quality crystalline semiconductor materials and the mechanical flexibility/stretchability of polymers. Our innovative system consists of ultra-thin Silicon ribbons bonded to an intelligently stretchable substrate which is intended to interpret and exert environmental signals and provide the desired stress relief. As one of the specific examples, we chose as a substrate the standard thermo-sensitive poly(N-isopropylacrylamide) (PNIPAAm) hydrogel with fast response and large deformation. In order to make the surface of the hydrogel waterproof and smooth for high-quality Silicon transfer, we introduced an intermediate layer of poly(dimethylsiloxane) (PDMS) between the substrate and the Silicon ribbons. The optical microscope results have shown that the system enables stiff Silicon ribbons to become adaptive and drivable by the soft environmentally sensitive substrate. Furthermore, we pioneered the development of complex geometries with two different methods: one is using stereolithography to electronically control the patterns and build up their profiles layer by layer; the other is integrating different multifunctional polymers. In this report, we have designed a bilayer structure comprising of a PNIPAAm hydrogel and a hybrid hydrogel of N-isopropylacrylamide (NIPAAm) and acrylic acid (AA). Typical variable curvatures can be obtained by the hydrogels with different dimensional expansion. These structures hold interesting possibilities in the design of electronic devices with tunable curvature.
ContributorsPan, Yuping (Author) / Dai, Lenore (Thesis advisor) / Jiang, Hanqing (Thesis advisor) / Lind, Mary Laura (Committee member) / Arizona State University (Publisher)
Created2012
Description
Nanoparticles are ubiquitous in various fields due to their unique properties not seen in similar bulk materials. Among them, core-shell composite nanoparticles are an important class of materials which are attractive for their applications in catalysis, sensing, electromagnetic shielding, drug delivery, and environmental remediation. This dissertation focuses on the study of core-shell type of nanoparticles where a polymer serves as the core and inorganic nanoparticles are the shell. This is an interesting class of supramolecular building blocks and can "exhibit unusual, possibly unique, properties which cannot be obtained simply by co-mixing polymer and inorganic particles". The one-step Pickering emulsion polymerization method was successfully developed and applied to synthesize polystyrene-silica core-shell composite particles. Possible mechanisms of the Pickering emulsion polymerization were also explored. The silica nanoparticles were thermodynamically favorable to self-assemble at liquid-liquid interfaces at the initial stage of polymerization and remained at the interface to finally form the shells of the composite particles. More importantly, Pickering emulsion polymerization was employed to synthesize polystyrene/poly(N-isopropylacrylamide) (PNIPAAm)-silica core-shell nanoparticles with N-isopropylacrylamide incorporated into the core as a co-monomer. The composite nanoparticles were temperature sensitive and could be up-taken by human prostate cancer cells and demonstrated effectiveness in drug delivery and cancer therapy. Similarly, by incorporating poly-2-(N,N)-dimethylamino)ethyl methacrylate (PDMA) into the core, pH sensitive core-shell composite nanoparticles were synthesized and applied as effective carriers to release a rheological modifier upon a pH change. Finally, the research focuses on facile approaches to engineer the transition of the temperature-sensitive particles and develop composite core-shell nanoparticles with a metallic shell.
ContributorsSanyal, Sriya (Author) / Dai, Lenore L. (Thesis advisor) / Jiang, Hanqing (Committee member) / Lind, Mary L. (Committee member) / Phelan, Patrick (Committee member) / Rege, Kaushal (Committee member) / Arizona State University (Publisher)
Created2012
Description
With the maturity of advanced composites as feasible structural materials for various applications there is a critical need to solve the challenge of designing these material systems for optimal performance. However, determining superior design methods requires a deep understanding of the material-structure properties at various length scales. Due to the length-scale dependent behavior of advanced composites, multiscale modeling techniques may be used to describe the dominant mechanisms of damage and failure in these material systems. With polymer matrix fiber composites and nanocomposites it becomes essential to include even the atomic length scale, where the resin-hardener-nanofiller molecules interact, in the multiscale modeling framework. Additionally, sources of variability are also critical to be included in these models due to the important role of uncertainty in advance composite behavior. Such a methodology should be able to describe length scale dependent mechanisms in a computationally efficient manner for the analysis of practical composite structures.
In the research presented in this dissertation, a comprehensive nano to macro multiscale framework is developed for the mechanical and multifunctional analysis of advanced composite materials and structures. An atomistically informed statistical multiscale model is developed for linear problems, to estimate and scale elastic properties of carbon fiber reinforced polymer composites (CFRPs) and carbon nanotube (CNT) enhanced CFRPs using information from molecular dynamics simulation of the resin-hardener-nanofiller nanoscale system. For modeling inelastic processes, an atomistically informed coupled damage-plasticity model is developed using the framework of continuum damage mechanics, where fundamental nanoscale covalent bond disassociation information is scaled up as a continuum scale damage identifying parameter. This damage model is coupled with a nanocomposite microstructure generation algorithm to study the sub-microscale damage mechanisms in CNT/CFRP microstructures. It is further integrated in a generalized method of cells (GMC) micromechanics model to create a low-fidelity computationally efficient nonlinear multiscale method with imperfect interfaces between the fiber and matrix, where the interface behavior is adopted from nanoscale MD simulations. This algorithm is used to understand damage mechanisms in adhesively bonded composite joints as a case study for the comprehensive nano to macroscale structural analysis of practical composites structures. At each length scale sources of variability are identified, characterized, and included in the multiscale modeling framework.
In the research presented in this dissertation, a comprehensive nano to macro multiscale framework is developed for the mechanical and multifunctional analysis of advanced composite materials and structures. An atomistically informed statistical multiscale model is developed for linear problems, to estimate and scale elastic properties of carbon fiber reinforced polymer composites (CFRPs) and carbon nanotube (CNT) enhanced CFRPs using information from molecular dynamics simulation of the resin-hardener-nanofiller nanoscale system. For modeling inelastic processes, an atomistically informed coupled damage-plasticity model is developed using the framework of continuum damage mechanics, where fundamental nanoscale covalent bond disassociation information is scaled up as a continuum scale damage identifying parameter. This damage model is coupled with a nanocomposite microstructure generation algorithm to study the sub-microscale damage mechanisms in CNT/CFRP microstructures. It is further integrated in a generalized method of cells (GMC) micromechanics model to create a low-fidelity computationally efficient nonlinear multiscale method with imperfect interfaces between the fiber and matrix, where the interface behavior is adopted from nanoscale MD simulations. This algorithm is used to understand damage mechanisms in adhesively bonded composite joints as a case study for the comprehensive nano to macroscale structural analysis of practical composites structures. At each length scale sources of variability are identified, characterized, and included in the multiscale modeling framework.
ContributorsRai, Ashwin (Author) / Chattopadhyay, Aditi (Thesis advisor) / Dai, Lenore (Committee member) / Jiang, Hanqing (Committee member) / Rajadas, John (Committee member) / Fard, Masoud Yekani (Committee member) / Arizona State University (Publisher)
Created2018
Description
A new type of electronics was envisioned, namely edible electronics. Edible electronics are made by Food and Drug Administration (FDA) certified edible materials which can be eaten and digested by human body. Different from implantable electronics, test or treatment using edible electronics doesn’t require operations and perioperative complications.
This dissertation bridges the food industry, material sciences, device fabrication, and biomedical engineering by demonstrating edible supercapacitors and electronic components and devices such as pH sensor.
Edible supercapacitors were fabricated using food materials from grocery store. 5 of them were connected in series to power a snake camera. Tests result showed that the current generated by supercapacitor have the ability to kill bacteria. Next more food, processed food and non-toxic level electronic materials were investigated. A “preferred food kit” was created for component fabrication based on the investigation. Some edible electronic components, such as wires, resistor, inductor, etc., were developed and characterized utilizing the preferred food kit. These components make it possible to fabricate edible electronic/device in the future work. Some edible electronic components were integrated into an edible electronic system/device. Then edible pH sensor was introduced and fabricated. This edible pH sensor can be swallowed and test pH of gastric fluid. PH can be read in a phone within seconds after the pH sensor was swallowed. As a side project, an edible double network gel electrolyte was synthesized for the edible supercapacitor.
This dissertation bridges the food industry, material sciences, device fabrication, and biomedical engineering by demonstrating edible supercapacitors and electronic components and devices such as pH sensor.
Edible supercapacitors were fabricated using food materials from grocery store. 5 of them were connected in series to power a snake camera. Tests result showed that the current generated by supercapacitor have the ability to kill bacteria. Next more food, processed food and non-toxic level electronic materials were investigated. A “preferred food kit” was created for component fabrication based on the investigation. Some edible electronic components, such as wires, resistor, inductor, etc., were developed and characterized utilizing the preferred food kit. These components make it possible to fabricate edible electronic/device in the future work. Some edible electronic components were integrated into an edible electronic system/device. Then edible pH sensor was introduced and fabricated. This edible pH sensor can be swallowed and test pH of gastric fluid. PH can be read in a phone within seconds after the pH sensor was swallowed. As a side project, an edible double network gel electrolyte was synthesized for the edible supercapacitor.
ContributorsXu, Wenwen (Author) / Jiang, Hanqing (Thesis advisor) / Dai, Lenore (Committee member) / Green, Matthew (Committee member) / Mu, Bin (Committee member) / Yu, Hongbin (Committee member) / Arizona State University (Publisher)
Created2019
Description
Environmentally responsive hydrogels are one interesting class of soft materials. Due to their remarkable responsiveness to stimuli such as temperature, pH, or light, they have attracted widespread attention in many fields. However, certain functionality of these materials alone is often limited in comparison to other materials such as silicon; thus, there is a need to integrate soft and hard materials for the advancement of environmental-ly responsive materials.
Conventional hydrogels lack good mechanical properties and have inherently slow response time, important characteristics which must be improved before the hydrogels can be integrated with silicon. In the present dissertation work, both these important attrib-utes of a temperature responsive hydrogel, poly(N-isopropylacrylamide) (PNIPAAm), were improved by adopting a low temperature polymerization process and adding a sili-cate compound, tetramethyl orthosilicate. Furthermore, the transition temperature was modulated by adjusting the media quality in which the hydrogels were equilibrated, e.g. by adding a co-solvent (methanol) or an anionic surfactant (sodium dodecyl sulfate). In-terestingly, the results revealed that, based on the hydrogels’ porosity, there were appre-ciable differences when the PNIPAAm hydrogels interacted with the media molecules.
Next, an adhesion mechanism was developed in order to transfer silicon thin film onto the hydrogel surface. This integration provided a means of mechanical buckling of the thin silicon film due to changes in environmental stimuli (e.g., temperature, pH). We also investigated how novel transfer printing techniques could be used to generate pat-terned deformation of silicon thin film when integrated on a planar hydrogel substrate. Furthermore, we explore multilayer hybrid hydrogel structures formed by the integration of different types of hydrogels that have tunable curvatures under the influence of differ-ent stimuli. Silicon thin film integration on such tunable curvature substrates reveal char-acteristic reversible buckling of the thin film in the presence of multiple stimuli.
Finally, different approaches of incorporating visible light response in PNIPAAm are discussed. Specifically, a chemical chromophore- spirobenzopyran was synthesized and integrated through chemical cross-linking into the PNIPAAm hydrogels. Further, methods of improving the light response and mechanical properties were also demonstrat-ed. Interestingly, such a system was shown to have potential application as light modulated topography altering system
Conventional hydrogels lack good mechanical properties and have inherently slow response time, important characteristics which must be improved before the hydrogels can be integrated with silicon. In the present dissertation work, both these important attrib-utes of a temperature responsive hydrogel, poly(N-isopropylacrylamide) (PNIPAAm), were improved by adopting a low temperature polymerization process and adding a sili-cate compound, tetramethyl orthosilicate. Furthermore, the transition temperature was modulated by adjusting the media quality in which the hydrogels were equilibrated, e.g. by adding a co-solvent (methanol) or an anionic surfactant (sodium dodecyl sulfate). In-terestingly, the results revealed that, based on the hydrogels’ porosity, there were appre-ciable differences when the PNIPAAm hydrogels interacted with the media molecules.
Next, an adhesion mechanism was developed in order to transfer silicon thin film onto the hydrogel surface. This integration provided a means of mechanical buckling of the thin silicon film due to changes in environmental stimuli (e.g., temperature, pH). We also investigated how novel transfer printing techniques could be used to generate pat-terned deformation of silicon thin film when integrated on a planar hydrogel substrate. Furthermore, we explore multilayer hybrid hydrogel structures formed by the integration of different types of hydrogels that have tunable curvatures under the influence of differ-ent stimuli. Silicon thin film integration on such tunable curvature substrates reveal char-acteristic reversible buckling of the thin film in the presence of multiple stimuli.
Finally, different approaches of incorporating visible light response in PNIPAAm are discussed. Specifically, a chemical chromophore- spirobenzopyran was synthesized and integrated through chemical cross-linking into the PNIPAAm hydrogels. Further, methods of improving the light response and mechanical properties were also demonstrat-ed. Interestingly, such a system was shown to have potential application as light modulated topography altering system
ContributorsChatterjee, Prithwish (Author) / Dai, Lenore L. (Thesis advisor) / Jiang, Hanqing (Thesis advisor) / Lind, Mary Laura (Committee member) / Yu, Hongyu (Committee member) / Yu, Hongbin (Committee member) / Arizona State University (Publisher)
Created2015
Description
All structures suffer wear and tear because of impact, excessive load, fatigue, corrosion, etc. in addition to inherent defects during their manufacturing processes and their exposure to various environmental effects. These structural degradations are often imperceptible, but they can severely affect the structural performance of a component, thereby severely decreasing its service life. Although previous studies of Structural Health Monitoring (SHM) have revealed extensive prior knowledge on the parts of SHM processes, such as the operational evaluation, data processing, and feature extraction, few studies have been conducted from a systematical perspective, the statistical model development.
The first part of this dissertation, the characteristics of inverse scattering problems, such as ill-posedness and nonlinearity, reviews ultrasonic guided wave-based structural health monitoring problems. The distinctive features and the selection of the domain analysis are investigated by analytically searching the conditions of the uniqueness solutions for ill-posedness and are validated experimentally.
Based on the distinctive features, a novel wave packet tracing (WPT) method for damage localization and size quantification is presented. This method involves creating time-space representations of the guided Lamb waves (GLWs), collected at a series of locations, with a spatially dense distribution along paths at pre-selected angles with respect to the direction, normal to the direction of wave propagation. The fringe patterns due to wave dispersion, which depends on the phase velocity, are selected as the primary features that carry information, regarding the wave propagation and scattering.
The following part of this dissertation presents a novel damage-localization framework, using a fully automated process. In order to construct the statistical model for autonomous damage localization deep-learning techniques, such as restricted Boltzmann machine and deep belief network, are trained and utilized to interpret nonlinear far-field wave patterns.
Next, a novel bridge scour estimation approach that comprises advantages of both empirical and data-driven models is developed. Two field datasets from the literature are used, and a Support Vector Machine (SVM), a machine-learning algorithm, is used to fuse the field data samples and classify the data with physical phenomena. The Fast Non-dominated Sorting Genetic Algorithm (NSGA-II) is evaluated on the model performance objective functions to search for Pareto optimal fronts.
The first part of this dissertation, the characteristics of inverse scattering problems, such as ill-posedness and nonlinearity, reviews ultrasonic guided wave-based structural health monitoring problems. The distinctive features and the selection of the domain analysis are investigated by analytically searching the conditions of the uniqueness solutions for ill-posedness and are validated experimentally.
Based on the distinctive features, a novel wave packet tracing (WPT) method for damage localization and size quantification is presented. This method involves creating time-space representations of the guided Lamb waves (GLWs), collected at a series of locations, with a spatially dense distribution along paths at pre-selected angles with respect to the direction, normal to the direction of wave propagation. The fringe patterns due to wave dispersion, which depends on the phase velocity, are selected as the primary features that carry information, regarding the wave propagation and scattering.
The following part of this dissertation presents a novel damage-localization framework, using a fully automated process. In order to construct the statistical model for autonomous damage localization deep-learning techniques, such as restricted Boltzmann machine and deep belief network, are trained and utilized to interpret nonlinear far-field wave patterns.
Next, a novel bridge scour estimation approach that comprises advantages of both empirical and data-driven models is developed. Two field datasets from the literature are used, and a Support Vector Machine (SVM), a machine-learning algorithm, is used to fuse the field data samples and classify the data with physical phenomena. The Fast Non-dominated Sorting Genetic Algorithm (NSGA-II) is evaluated on the model performance objective functions to search for Pareto optimal fronts.
ContributorsKim, Inho (Author) / Chattopadhyay, Aditi (Thesis advisor) / Jiang, Hanqing (Committee member) / Liu, Yongming (Committee member) / Mignolet, Marc (Committee member) / Rajadas, John (Committee member) / Arizona State University (Publisher)
Created2016
Description
There is a concerted effort in developing robust systems health monitoring/management (SHM) technology as a means to reduce the life cycle costs, improve availability, extend life and minimize downtime of various platforms including aerospace and civil infrastructure. The implementation of a robust SHM system requires a collaborative effort in a variety of areas such as sensor development, damage detection and localization, physics based models, and prognosis models for residual useful life (RUL) estimation. Damage localization and prediction is further complicated by geometric, material, loading, and environmental variabilities. Therefore, it is essential to develop robust SHM methodologies by taking into account such uncertainties. In this research, damage localization and RUL estimation of two different physical systems are addressed: (i) fatigue crack propagation in metallic materials under complex multiaxial loading and (ii) temporal scour prediction near bridge piers. With little modifications, the methodologies developed can be applied to other systems.
Current practice in fatigue life prediction is based on either physics based modeling or data-driven methods, and is limited to predicting RUL for simple geometries under uniaxial loading conditions. In this research, crack initiation and propagation behavior under uniaxial and complex biaxial fatigue loading is addressed. The crack propagation behavior is studied by performing extensive material characterization and fatigue testing under in-plane biaxial loading, both in-phase and out-of-phase, with different biaxiality ratios. A hybrid prognosis model, which combines machine learning with physics based modeling, is developed to account for the uncertainties in crack propagation and fatigue life prediction due to variabilities in material microstructural characteristics, crack localization information and environmental changes. The methodology iteratively combines localization information with hybrid prognosis models using sequential Bayesian techniques. The results show significant improvements in the localization and prediction accuracy under varying temperature.
For civil infrastructure, especially bridges, pier scour is a major failure mechanism. Currently available techniques are developed from a design perspective and provide highly conservative scour estimates. In this research, a fully probabilistic scour prediction methodology is developed using machine learning to accurately predict scour in real-time under varying flow conditions.
Current practice in fatigue life prediction is based on either physics based modeling or data-driven methods, and is limited to predicting RUL for simple geometries under uniaxial loading conditions. In this research, crack initiation and propagation behavior under uniaxial and complex biaxial fatigue loading is addressed. The crack propagation behavior is studied by performing extensive material characterization and fatigue testing under in-plane biaxial loading, both in-phase and out-of-phase, with different biaxiality ratios. A hybrid prognosis model, which combines machine learning with physics based modeling, is developed to account for the uncertainties in crack propagation and fatigue life prediction due to variabilities in material microstructural characteristics, crack localization information and environmental changes. The methodology iteratively combines localization information with hybrid prognosis models using sequential Bayesian techniques. The results show significant improvements in the localization and prediction accuracy under varying temperature.
For civil infrastructure, especially bridges, pier scour is a major failure mechanism. Currently available techniques are developed from a design perspective and provide highly conservative scour estimates. In this research, a fully probabilistic scour prediction methodology is developed using machine learning to accurately predict scour in real-time under varying flow conditions.
ContributorsNeerukatti, Rajesh Kumar (Author) / Chattopadhyay, Aditi (Thesis advisor) / Jiang, Hanqing (Committee member) / Liu, Yongming (Committee member) / Rajadas, John (Committee member) / Yekani Fard, Masoud (Committee member) / Arizona State University (Publisher)
Created2016