ASU Electronic Theses and Dissertations
This collection includes most of the ASU Theses and Dissertations from 2011 to present. ASU Theses and Dissertations are available in downloadable PDF format; however, a small percentage of items are under embargo. Information about the dissertations/theses includes degree information, committee members, an abstract, supporting data or media.
In addition to the electronic theses found in the ASU Digital Repository, ASU Theses and Dissertations can be found in the ASU Library Catalog.
Dissertations and Theses granted by Arizona State University are archived and made available through a joint effort of the ASU Graduate College and the ASU Libraries. For more information or questions about this collection contact or visit the Digital Repository ETD Library Guide or contact the ASU Graduate College at gradformat@asu.edu.
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- Creators: Rajadas, John
- Creators: Mobasher, Barzin
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
Materials with unprecedented properties are necessary to make dramatic changes in current and future aerospace platforms. Hybrid materials and composites are increasingly being used in aircraft and spacecraft frames; however, future platforms will require an optimal design of novel materials that enable operation in a variety of environments and produce known/predicted damage mechanisms. Nanocomposites and nanoengineered composites with CNTs have the potential to make significant improvements in strength, stiffness, fracture toughness, flame retardancy and resistance to corrosion. Therefore, these materials have generated tremendous scientific and technical interest over the past decade and various architectures are being explored for applications to light-weight airframe structures. However, the success of such materials with significantly improved performance metrics requires careful control of the parameters during synthesis and processing. Their implementation is also limited due to the lack of complete understanding of the effects the nanoparticles impart to the bulk properties of composites. It is common for computational methods to be applied to explain phenomena measured or observed experimentally. Frequently, a given phenomenon or material property is only considered to be fully understood when the associated physics has been identified through accompanying calculations or simulations.
The computationally and experimentally integrated research presented in this dissertation provides improved understanding of the mechanical behavior and response including damage and failure in CNT nanocomposites, enhancing confidence in their applications. The computations at the atomistic level helps to understand the underlying mechanochemistry and allow a systematic investigation of the complex CNT architectures and the material performance across a wide range of parameters. Simulation of the bond breakage phenomena and development of the interface to continuum scale damage captures the effects of applied loading and damage precursor and provides insight into the safety of nanoengineered composites under service loads. The validated modeling methodology is expected to be a step in the direction of computationally-assisted design and certification of novel materials, thus liberating the pace of their implementation in future applications.
The computationally and experimentally integrated research presented in this dissertation provides improved understanding of the mechanical behavior and response including damage and failure in CNT nanocomposites, enhancing confidence in their applications. The computations at the atomistic level helps to understand the underlying mechanochemistry and allow a systematic investigation of the complex CNT architectures and the material performance across a wide range of parameters. Simulation of the bond breakage phenomena and development of the interface to continuum scale damage captures the effects of applied loading and damage precursor and provides insight into the safety of nanoengineered composites under service loads. The validated modeling methodology is expected to be a step in the direction of computationally-assisted design and certification of novel materials, thus liberating the pace of their implementation in future applications.
ContributorsSubramanian, Nithya (Author) / Chattopadhyay, Aditi (Thesis advisor) / Dai, Lenore (Committee member) / Jiao, Yang (Committee member) / Liu, Yongming (Committee member) / Rajadas, John (Committee member) / Arizona State University (Publisher)
Created2018
Description
Phase change materials (PCMs) are combined sensible-and-latent thermal energy storage materials that can be used to store and dissipate energy in the form of heat. PCMs incorporated into wall-element systems have been well-studied with respect to energy efficiency of building envelopes. New applications of PCMs in infrastructural concrete, e.g., for mitigating early-age cracking and freeze-and-thaw induced damage, have also been proposed. Hence, the focus of this dissertation is to develop a detailed understanding of the physic-chemical and thermo-mechanical characteristics of cementitious systems and novel coating systems for wall-elements containing PCM. The initial phase of this work assesses the influence of interface properties and inter-inclusion interactions between microencapsulated PCM, macroencapsulated PCM, and the cementitious matrix. The fact that these inclusions within the composites are by themselves heterogeneous, and contain multiple components necessitate careful application of models to predict the thermal properties. The next phase observes the influence of PCM inclusions on the fracture and fatigue behavior of PCM-cementitious composites. The compliant nature of the inclusion creates less variability in the fatigue life for these composites subjected to cyclic loading. The incorporation of small amounts of PCM is found to slightly improve the fracture properties compared to PCM free cementitious composites. Inelastic deformations at the crack-tip in the direction of crack opening are influenced by the microscale PCM inclusions. After initial laboratory characterization of the microstructure and evaluation of the thermo-mechanical performance of these systems, field scale applicability and performance were evaluated. Wireless temperature and strain sensors for smart monitoring were embedded within a conventional portland cement concrete pavement (PCCP) and a thermal control smart concrete pavement (TCSCP) containing PCM. The TCSCP exhibited enhanced thermal performance over multiple heating and cooling cycles. PCCP showed significant shrinkage behavior as a result of compressive strains in the reinforcement that were twice that of the TCSCP. For building applications, novel PCM-composites coatings were developed to improve and extend the thermal efficiency. These coatings demonstrated a delay in temperature by up to four hours and were found to be more cost-effective than traditional building insulating materials.
The results of this work prove the feasibility of PCMs as a temperature-regulating technology. Not only do PCMs reduce and control the temperature within cementitious systems without affecting the rate of early property development but they can also be used as an auto-adaptive technology capable of improving the thermal performance of building envelopes.
The results of this work prove the feasibility of PCMs as a temperature-regulating technology. Not only do PCMs reduce and control the temperature within cementitious systems without affecting the rate of early property development but they can also be used as an auto-adaptive technology capable of improving the thermal performance of building envelopes.
ContributorsAguayo, Matthew Joseph (Author) / Neithalath, Narayanan (Thesis advisor) / Rajan, Subramaniam D. (Committee member) / Mobasher, Barzin (Committee member) / Underwood, Benjamin (Committee member) / Liu, Yongming (Committee member) / Arizona State University (Publisher)
Created2018
Description
Advanced aerospace materials, including fiber reinforced polymer and ceramic matrix composites, are increasingly being used in critical and demanding applications, challenging the current damage prediction, detection, and quantification methodologies. Multiscale computational models offer key advantages over traditional analysis techniques and can provide the necessary capabilities for the development of a comprehensive virtual structural health monitoring (SHM) framework. Virtual SHM has the potential to drastically improve the design and analysis of aerospace components through coupling the complementary capabilities of models able to predict the initiation and propagation of damage under a wide range of loading and environmental scenarios, simulate interrogation methods for damage detection and quantification, and assess the health of a structure. A major component of the virtual SHM framework involves having micromechanics-based multiscale composite models that can provide the elastic, inelastic, and damage behavior of composite material systems under mechanical and thermal loading conditions and in the presence of microstructural complexity and variability. Quantification of the role geometric and architectural variability in the composite microstructure plays in the local and global composite behavior is essential to the development of appropriate scale-dependent unit cells and boundary conditions for the multiscale model. Once the composite behavior is predicted and variability effects assessed, wave-based SHM simulation models serve to provide knowledge on the probability of detection and characterization accuracy of damage present in the composite. The research presented in this dissertation provides the foundation for a comprehensive SHM framework for advanced aerospace materials. The developed models enhance the prediction of damage formation as a result of ceramic matrix composite processing, improve the understanding of the effects of architectural and geometric variability in polymer matrix composites, and provide an accurate and computational efficient modeling scheme for simulating guided wave excitation, propagation, interaction with damage, and sensing in a range of materials. The methodologies presented in this research represent substantial progress toward the development of an accurate and generalized virtual SHM framework.
ContributorsBorkowski, Luke (Author) / Chattopadhyay, Aditi (Thesis advisor) / Liu, Yongming (Committee member) / Mignolet, Marc (Committee member) / Papandreou-Suppappola, Antonia (Committee member) / Rajadas, John (Committee member) / Arizona State University (Publisher)
Created2015
Description
The motivation of this work is based on development of new construction products with strain hardening cementitious composites (SHCC) geared towards sustainable residential applications. The proposed research has three main objectives: automation of existing manufacturing systems for SHCC laminates; multi-level characterization of mechanical properties of fiber, matrix, interface and composites phases using servo-hydraulic and digital image correlation techniques. Structural behavior of these systems were predicted using ductility based design procedures using classical laminate theory and structural mechanics. SHCC sections are made up of thin sections of matrix with Portland cement based binder and fine aggregates impregnating continuous one-dimensional fibers in individual or bundle form or two/three dimensional woven, bonded or knitted textiles. Traditional fiber reinforced concrete (FRC) use random dispersed chopped fibers in the matrix at a low volume fractions, typically 1-2% to avoid to avoid fiber agglomeration and balling. In conventional FRC, fracture localization occurs immediately after the first crack, resulting in only minor improvement in toughness and tensile strength. However in SHCC systems, distribution of cracking throughout the specimen is facilitated by the fiber bridging mechanism. Influence of material properties of yarn, composition, geometry and weave patterns of textile in the behavior of laminated SHCC skin composites were investigated. Contribution of the cementitious matrix in the early age and long-term performance of laminated composites was studied with supplementary cementitious materials such as fly ash, silica fume, and wollastonite. A closed form model with classical laminate theory and ply discount method, coupled with a damage evolution model was utilized to simulate the non-linear tensile response of these composite materials. A constitutive material model developed earlier in the group was utilized to characterize and correlate the behavior of these structural composites under uniaxial tension and flexural loading responses. Development and use of analytical models enables optimal design for application of these materials in structural applications. Another area of immediate focus is the development of new construction products from SHCC laminates such as angles, channels, hat sections, closed sections with optimized cross sections. Sandwich composites with stress skin-cellular core concept were also developed to utilize strength and ductility of fabric reinforced skin in addition to thickness, ductility, and thermal benefits of cellular core materials. The proposed structurally efficient and durable sections promise to compete with wood and light gage steel based sections for lightweight construction and panel application
ContributorsDey, Vikram (Author) / Mobasher, Barzin (Thesis advisor) / Rajan, Subramaniam D. (Committee member) / Neithalath, Narayanan (Committee member) / Underwood, Benjamin (Committee member) / Liu, Yongming (Committee member) / Arizona State University (Publisher)
Created2016
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
Description
Composite materials are now beginning to provide uses hitherto reserved for metals in structural systems such as airframes and engine containment systems, wraps for repair and rehabilitation, and ballistic/blast mitigation systems. These structural systems are often subjected to impact loads and there is a pressing need for accurate prediction of deformation, damage and failure. There are numerous material models that have been developed to analyze the dynamic impact response of polymer matrix composites. However, there are key features that are missing in those models that prevent them from providing accurate predictive capabilities. In this dissertation, a general purpose orthotropic elasto-plastic computational constitutive material model has been developed to predict the response of composites subjected to high velocity impacts. The constitutive model is divided into three components – deformation model, damage model and failure model, with failure to be added at a later date. The deformation model generalizes the Tsai-Wu failure criteria and extends it using a strain-hardening-based orthotropic yield function with a non-associative flow rule. A strain equivalent formulation is utilized in the damage model that permits plastic and damage calculations to be uncoupled and capture the nonlinear unloading and local softening of the stress-strain response. A diagonal damage tensor is defined to account for the directionally dependent variation of damage. However, in composites it has been found that loading in one direction can lead to damage in multiple coordinate directions. To account for this phenomena, the terms in the damage matrix are semi-coupled such that the damage in a particular coordinate direction is a function of the stresses and plastic strains in all of the coordinate directions. The overall framework is driven by experimental tabulated temperature and rate-dependent stress-strain data as well as data that characterizes the damage matrix and failure. The developed theory has been implemented in a commercial explicit finite element analysis code, LS-DYNA®, as MAT213. Several verification and validation tests using a commonly available carbon-fiber composite, Toyobo’s T800/F3900, have been carried and the results show that the theory and implementation are efficient, robust and accurate.
ContributorsHoffarth, Canio (Author) / Rajan, Subramaniam D. (Thesis advisor) / Goldberg, Robert (Committee member) / Neithalath, Narayanan (Committee member) / Mobasher, Barzin (Committee member) / Liu, Yongming (Committee member) / Arizona State University (Publisher)
Created2016
Stochastic multiscale modeling and statistical characterization of complex polymer matrix composites
Description
There are many applications for polymer matrix composite materials in a variety of different industries, but designing and modeling with these materials remains a challenge due to the intricate architecture and damage modes. Multiscale modeling techniques of composite structures subjected to complex loadings are needed in order to address the scale-dependent behavior and failure. The rate dependency and nonlinearity of polymer matrix composite materials further complicates the modeling. Additionally, variability in the material constituents plays an important role in the material behavior and damage. The systematic consideration of uncertainties is as important as having the appropriate structural model, especially during model validation where the total error between physical observation and model prediction must be characterized. It is necessary to quantify the effects of uncertainties at every length scale in order to fully understand their impact on the structural response. Material variability may include variations in fiber volume fraction, fiber dimensions, fiber waviness, pure resin pockets, and void distributions. Therefore, a stochastic modeling framework with scale dependent constitutive laws and an appropriate failure theory is required to simulate the behavior and failure of polymer matrix composite structures subjected to complex loadings. Additionally, the variations in environmental conditions for aerospace applications and the effect of these conditions on the polymer matrix composite material need to be considered. The research presented in this dissertation provides the framework for stochastic multiscale modeling of composites and the characterization data needed to determine the effect of different environmental conditions on the material properties. The developed models extend sectional micromechanics techniques by incorporating 3D progressive damage theories and multiscale failure criteria. The mechanical testing of composites under various environmental conditions demonstrates the degrading effect these conditions have on the elastic and failure properties of the material. The methodologies presented in this research represent substantial progress toward understanding the failure and effect of variability for complex polymer matrix composites.
ContributorsJohnston, Joel Philip (Author) / Chattopadhyay, Aditi (Thesis advisor) / Liu, Yongming (Committee member) / Jiang, Hanqing (Committee member) / Dai, Lenore (Committee member) / Rajadas, John (Committee member) / Arizona State University (Publisher)
Created2016
Description
As the demand of sustainable construction materials increases, use of fibers and textiles as partial or full reinforcement in concrete members present a tremendous opportunity. Proper characterization techniques and design guides for hybrid materials are therefore needed. This dissertation presents a comprehensive study on serviceability-based design of strain softening and strain hardening materials. Multiple experimental procedures are developed to document the nature of single crack localization and multiple cracking mechanisms in various fiber and fabric reinforced cement-based composites. In addition, strain rate effects on the mechanical properties are examined using a high speed servo-hydraulic tension test equipment.
Significant hardening and degradation parameters such as stiffness, crack spacing, crack width, localized zone size are obtained from tensile tests using digital image correlation (DIC) technique. A tension stiffening model is used to simulate the tensile response that addresses the cracking and localization mechanisms. The model is also modified to simulate the sequential cracking in joint-free slabs on grade reinforced by steel fibers, where the lateral stiffness of slab and grade interface and stress-crack width response are the most important model parameters.
Parametric tensile and compressive material models are used to formulate generalized analytical solutions for flexural behaviors of hybrid reinforced concrete (HRC) that contains both rebars and fibers. Design recommendations on moment capacity, minimum reinforcement ratio etc. are obtained using analytical equations. The role of fiber in reducing the amount of conventional reinforcement is revealed. The approach is extended to T-sections and used to model Ultra High Performance Concrete (UHPC) beams and girders.
The analytical models are extended to structural members subjected to combined axial and bending actions. Analytical equations to address the P-M diagrams are derived. Closed-form equations that generate the interaction diagram of HRC section are presented which may be used in the design of multiple types of applications.
The theoretical models are verified by independent experimental results from literature. Reliability analysis using Monte Carlo simulation (MCS) is conducted for few design problems on ultimate state design. The proposed methodologies enable one to simulate the experiments to obtain material parameters and design structural members using generalized formulations.
Significant hardening and degradation parameters such as stiffness, crack spacing, crack width, localized zone size are obtained from tensile tests using digital image correlation (DIC) technique. A tension stiffening model is used to simulate the tensile response that addresses the cracking and localization mechanisms. The model is also modified to simulate the sequential cracking in joint-free slabs on grade reinforced by steel fibers, where the lateral stiffness of slab and grade interface and stress-crack width response are the most important model parameters.
Parametric tensile and compressive material models are used to formulate generalized analytical solutions for flexural behaviors of hybrid reinforced concrete (HRC) that contains both rebars and fibers. Design recommendations on moment capacity, minimum reinforcement ratio etc. are obtained using analytical equations. The role of fiber in reducing the amount of conventional reinforcement is revealed. The approach is extended to T-sections and used to model Ultra High Performance Concrete (UHPC) beams and girders.
The analytical models are extended to structural members subjected to combined axial and bending actions. Analytical equations to address the P-M diagrams are derived. Closed-form equations that generate the interaction diagram of HRC section are presented which may be used in the design of multiple types of applications.
The theoretical models are verified by independent experimental results from literature. Reliability analysis using Monte Carlo simulation (MCS) is conducted for few design problems on ultimate state design. The proposed methodologies enable one to simulate the experiments to obtain material parameters and design structural members using generalized formulations.
ContributorsYao, Yiming (Author) / Mobasher, Barzin (Thesis advisor) / Underwood, Benjamin (Committee member) / Neithalath, Narayanan (Committee member) / Rajan, Subramaniam D. (Committee member) / Liu, Yongming (Committee member) / Arizona State University (Publisher)
Created2016