An investigation of the minimum web reinforcement requirements for slender and non-slender beams

Prescriptive shear requirements from ACI 318 (2019), AASHTO LRFD (2020), and the fib Model Code (2010) stipulate that both slender and non-slender beams must satisfy a minimum web reinforcement requirement. At the very minimum, slender beams require an area of web reinforcement equal to 0.08% of the cross-section placed in the vertical direction, while non-slender beams require up to 0.3% in both the vertical and horizontal directions. This investigation aims to evaluate these minimum web reinforcement requirements in terms of the strength and serviceability behavior of beams based on the results of experimental test data. Databases of shear tests on slender and non-slender beams with web reinforcement is compiled from existing, validated and peer-reviewed datasets. The aim of this study is accomplished through an analysis of these databases. Given the context of this investigation and available data, the results show that a minimum web reinforcement of at least: 0.08% for beams with a f′c 4,000-psi for ACI 318 (2019) is an adequate amount to ensure that the predicted shear strength will be greater than or equal to the experimental shear strength of slender beams, consistent with the recommendations of MacGregor and Hanson (1969) and Roller and Russell (1990); √(f_c^' )⁄f_v for the shear strength of slender beams for AASHTO LRFD (2020) and the fib Model Code (2010), consistent with the results of Shahrooz et al. (2011); 0.12% for the strength of non-slender beams; and 0.25% to restrain cracks widths to less than or equal to 0.016-inches for ACI 318 (2019), AASHTO LRFD (2020), and the fib Model Code (2010) for slender and non-slender beams. Based on these findings, the minimum web reinforcement for strength is consistent with that of the code for slender beams but not deep beams, and the minimum web reinforcement for serviceability is consistent with that of the code for neither slender nor non-slender beams. The prescriptive web reinforcement requirements for slender and non-slender beams do not appear to be derived from the same criteria—at least in the American building and bridge codes. The minimum web reinforcement requirement for slender beams is likely derived based on these members achieving their predicted strength, while the requirement for non-slender beams is likely derived based on these members exhibiting crack widths less than or equal to 0.016-inches while in service

Pollinator species distributions and interaction networks across local environmental gradients to continental scales

Insect pollinator species are critical for angiosperm reproduction and maintenance of human food crop populations. Insect pollination networks are complex webs of overlapping links, and mutualistic associations between these pollinators and their host plants are imperative for network stability and robustness in the face of ecosystem perturbation. Even slight changes in the structure of mutualistic associations can affect the underlying topological features of pollination systems. While pollinator and plant species richness, abundance, or overall composition of plant-pollinator interactions may shift organically over time (seasons, months) and space (environmental gradients), warming temperatures are already causing unanticipated changes in plant-pollinator communities on local scales. My dissertation focuses on the local pollination networks of the San Francisco Peaks in northern Arizona. In Chapter Two, I evaluate how species richness, abundance, and critical network properties change across three life zones (vegetation zones) of this unique elevational gradient. I also determine the critical generalist pollinator species responsible for community stability in the highest elevation life zone. In Chapter Three, I evaluate the impacts of short-term drought on the plant-pollinator communities of the San Francisco Peaks. Specifically, in the 2017-2018 winter and spring seasons, Flagstaff experienced especially dry conditions, with very minimal precipitation and a significant 43-day period in spring with no rainfall. This allowed for a unique opportunity to compare year-to-year differences in species richness, abundance, and timing of flowering/foraging periods across seasons. I also examine the potential shift in pollinator species generalization (diet breadth) in the dry year and how this may vary across life zones. However, to predict the impacts of global change on insect pollinator species diversity and distribution, studies must also be conducted at regional and global scales. In Chapter Four, I perform large-scale analyses of current USA bee data completeness using 1.923 million occurrence records for the contiguous United States. I determine clear sampling biases for certain taxa and geographic locations as well as identify undersampled areas that are likely hotspots for bee diversity. Additionally, I show that even if we were to digitize the remaining ~6 million collected-yet-undigitized bee specimens in institutions, this would not be sufficient to fill gaps, underscoring the need for more strategic sampling and monitoring programs. I conclude this dissertation by highlighting how understanding insect pollinator species distributions and their mutualistic associations is fundamental for pollinator conservation, and that this holds true across local, regional, and global scales

Disconnected youth in rural communities: a case study

The purpose of this study is to investigate the negative effects and impact disconnected youth have on the rural communities they live in. The study was conducted in small rural communities in Arizona, and only youth ages 16 to 24 who were not in school and not working were invited to participate. The study aimed to examine the thought processes and behaviors of disconnected youth, and pair those findings with various struggles in rural communities to find correlations of the information obtained. Both surveys and interviews of disconnected youth were the basis of the research, and this information was compiled and analyzed to provide a more accurate picture of their impact of these small rural communities. The findings of the study are reported in the final chapters

K-1 discipline support since the enactment of Arizona House Bill 2123

The purpose of this study was to explore the impact of Arizona Revised Statues 15-843(K) with Arizona elementary administrators, since the 2021-2022 school year. Additionally, this research investigated the Tier 1 and Tier 2 interventions administrators have found to be effective and ineffective to support K-1 students who have behavioral needs. Participants included 12 elementary administrators from two suburban school districts. The design of this study was a case study with a focus on qualitative data collected, analyzed, and summarized. No additional data were collected. Research Question 1 addressed how administrators’ discipline practices have changed since the revised law. Even though half of the administrators discussed no change has occurred, there was an equal number of participants who explained they had to be creative with their practices. Additionally, they expressed how they had to formalize their MTSS process and focus on early interventions and student relationships. Research Question 2 addressed what Tier 1 behavioral supports were being utilized for K-1 students. Administrators expressed having their teachers utilize a Social Emotional Curriculum to teach daily social skills as a support for K-1 students. They shared having school-wide expectations in place was supportive for the students. Additionally, having the expectations visible around campus, explicitly taught, and using positive tickets helped to support students with behavioral needs. Research Question 3 addressed identifying effective Tier 2 interventions. Administrators identified having extra staff members pull small groups of students in order to provide them with instruction on social skills. In addition, Check-in, Check-out was explained to be effective because it was a staff member, other than the teacher, who did the check-in and reflect on behavior goals with the student. When a student reaches their goal, an incentive is given. Research Question 4 asked administrators to identify ineffective Tier 2 interventions. Because there were none share by participants, there was no conclusion drawn as there was an insufficiency of data

Women on the bus: transit experiences in Flagstaff

In 2001, the Mountain Line bus system began operations in Flagstaff, AZ, after voters approved a tax increase for transit funding. This political decision impacted Flagstaff’s historical and geographic movement, embodied experiences, and everyday practices —or “constellations of mobility” (Cresswell, 2010) of older women of color and other groups at the social margins. In the United States, women are the social sector that uses transit the most (Lee et al., 2017). Yet social scientists in Northern Arizona have overlooked the Mountain Line bus as an epistemic space worth attention over two decades. A gap exists in analyzing the bus from a gender perspective. This gap highlights hidden structures of oppression and violence toward older women in urban settings. This study is grounded in a sociological framework combining Marxism, Constructivist Structuralism, and Decolonial Feminism to explore public transportation-related mobility experiences of older women of color in Flagstaff. Participant observations on all Mountain Line routes and semi-structured interviews with four female riders who identify as Hispanic and Navajo, two transit officers, and one driver indicate that older women of color in Flagstaff suffer social exclusion due to the transportation disadvantages they face. This exclusion amplifies the gender data gap on their transit needs, reinforces stigmas imposed on the bus in the United States, and results in a non-universal infrastructure characterized by racist, ageist, and misogynistic social dynamics. In this oppressive context, older women of color develop sophisticated strategies to navigate transit systems in the city, facing time control, infrastructure conditions, and safety burdens that constitute issues that impact their transit experiences

The tensile behavior, fracture, and power harvesting potential of nickel-manganese-gallium magnetic shape memory alloys

The shape memory effect in Ni2MnGa MSMAs is driven by the magnetic field-induced or stress-induced motion of twin boundaries. The Ni2MnGa microstructure consists of tetragonal martensite variants with magnetic easy axis aligned with the short axis (of the unit cell), which aligns in either the direction of the applied magnetic field or mechanical stress causing a reorientation of the microstructure. The reorientation strain and the change in the material’s magnetization during variant reorientation, drive the development of MSMA-based applications. The overarching objective of this dissertation study is to inform the development of new MSMA-based applications and to improve the efficiency and reliability of current MSMA-based applications by studying the tensile behavior, fracture mechanics, and power harvesting potential of Ni2MnGa MSMAs. Historically, MSMAs have been studied extensively under combined compressive and/or magnetic loads, and applications only use MSMAs in compression. The tensile study investigates the strain fields developed in Ni2MnGa samples, with fine and coarse twin structures when loaded in tension and/or with a magnetic field. The strain fields are recorded using digital image correlation, which allowed for the observation of the evolution of the strain field over the entire sample, concurrent with the evolution of the sample’s twin microstructure. The results show that the twin density, the uniformity of the magneto-mechanical loading along the sample, and the presence of pinning sites are all contributing to the profile of the tensile strain field; the presence of pinning sites along the sample inhibits variant reorientation and recovery. Both samples showed no visible signs of damage during tensile testing, and the magneto-mechanical response in tension was found to be comparable to that in compression for both sample types. The fracture mechanics study involves the experimental research of the fracture mechanisms in MSMAs and the development of an MSMA fracture mechanics modeling framework; the brittle nature of Ni2MnGa MSMAs causes cracks to develop in them hampering their function in MSMA-based applications. The phase-field method is proposed for the modeling framework since this method is able to capture complex crack patterns, and Vickers microindentation is used for the experimental study to determine the fracture energy of the material and study crack evolution characteristics under magneto-mechanical loading. The Vickers microindentation results suggest that transverse magnetic fields facilitate crack growth and decrease the fracture energy of the MSMA, while the axial compressive stress impedes crack growth and increases the fracture energy. Lastly, the power harvesting study reports new power harvesting data generated with a biaxial magnetic field and a surrounding coil, and full strain field data for an MSMA subject to load similar to what is seen during power harvesting, then compares theperformance of MSMA-based power harvesters with different designs to determine which give the best output. For this comparison, a framework for evaluating the performance of (side coil and surrounding coil type) MSMA-based power harvesters reported in the literature is developed. This framework involves normalizing the results to the design characteristics of the respective harvesters. The strain maps reveal the potential for perpendicular twin boundaries that limit variant reorientation and correspondingly the harvester’s power. The power harvesting study concludes that the largest change in magnetic flux density, which is the driver for power harvesting, occurs in the side coil setup with an optimized magnetic circuit and it recommends using this configuration for future MSMA-based power harvesters for maximum power

An investigation of pause location in spoken English corpora

We all pause, but, um, do we all pause … in a similar manner? The first language (L1) of speakers is proposed as one factor that influences their pausing behavior in a target language, especially with regard to the frequency and location of the pauses (Goldman-Eisler, 1968). It is also assumed that the preference for a certain type of pause (silent or filled pauses) is related to its location in an utterance (Dumont, 2018). Given the complex relationship between these variables, more research is needed to understand language learners’ behavior better. This corpus-based study investigates the pause behavior of speakers of three L1 backgrounds from two English-spoken corpora: The Louvain International Database of Spoken English Interlanguage (LINDSEI) and The Louvain Corpus of Native English Conversation (LOCNEC). Descriptive statistics and binomial logistic regression were used for analysis. The study answers two research questions: (1) In what ways do L1 speakers of English, French, and Spanish differ in terms of the location and type of pause produced in English interviews? (2) Does L1 background and/or presence vs. absence of a clause boundary have an effect on what type of pause (silent vs. filled) is produced? The results show that the L1 Spanish group differed considerably from the other groups with regard to both frequency and location of pauses. Additionally, both L1 and the presence of a clause boundary were found to be predictors of pause type

Multilingual spoken word recognition: A megastudy approach

Bilingualism research has primarily focused on the perception and processing of individual sounds or word learning. Many studies have investigated how bilingual listeners perceive sound contrasts that don't create lexical distinctions in their native language. There is substantially less research that has investigated how word-level properties impact L2 auditory processing. The present study examines how auditory lexical processing differs between monolingual and bilingual listeners with different language backgrounds. We collected lexical decision accuracies and latencies for 26,793 words and 9600 pseudowords from the Massive Auditory Lexical Decision database from native and non-native listener responses. We compare the language backgrounds of our 1028 listeners and group them into four groups: native speakers, early, early-late, and late bilinguals. We report the findings of an analysis investigating how language background and proficiency modulate lexical effects to understand how language background influences spoken word recognition. We find differences in the effects of lexical frequency, phonological neighborhood density, and phonological uniqueness point between the different listener groups. We discuss the impact of bilingualism in a word recognition task and how these results inform models of spoken word recognition and second language acquisition

How will global warming affect decomposition and element cycling of native and invasive leaf litter in streams?

Global warming and introduced species have the potential to alter the functioning and structure of headwater streams. Testing how temperature affects detrital food webs is challenging because it is difficult to manipulate temperature in a field setting. Here, we evaluate how microbes and shredders decompose two morphologically distinct, riparian tree species, Populus fremontii (native) and Tamarix sp. (nonnative). We built an artificial stream facility to manipulate temperature while maintaining natural diurnal and seasonal patterns of most stream variables (light, nutrients, dissolved oxygen). We employed a factorial design that included 3 temperature treatments (ambient, + 3.7°C, and + 6.6°C), 2 leaf types and 2 decomposition mediators (microbes and shredders + microbes). Decomposition rate and microbial biomass were influenced by leaf type and temperature, with P. fremontii decomposing more rapidly and supporting almost twice the microbial biomass found in Tamarix sp. Temperature increased decomposition rate by 9.1 to 16.5% in the +3.7°C treatments relative to ambient temperatures. Additional increases in temperature did not accelerate decomposition rate for either species. Litter packs containing shredders decomposed more rapidly than litter packs with only microbes. However, shredder contribution to leaf litter decomposition was relatively low in both leaf types, ranging from 1.97% to 10.45%. A laboratory experiment measured leaching rates with 2 temperatures and 2 time periods (24h and 48h). P. fremontii leached significantly more DOC than Tamarix sp. and Tamarix sp. leached significantly more TN than P. fremontii. In both litter types, approximately 43% to 48% of initial mass was lost after 48 hours of leaching. Temperature did not affect mass loss due to leaching. These results show that increases in water temperature may lead to rapid depletion of labile leaf litter resources and that the disproportionately high leaching of TN found in Tamarix sp. leaf litter may prevent microbes and shredders from accessing compounds containing nitrogen

Physics informed neural networks to solve forward and inverse fluid flow and heat transfer problems

This dissertation proposes novel approaches to address challenges in solving fluid flow and transport problems in heterogeneous systems using deep learning methods.The first approach is a multi-fidelity modeling approach that combines data generated by a low-fidelity computational fluid dynamics (CFD) solution strategy and data-free physics- informed neural networks (PINN) to obtain improved accuracy. High-fidelity models of multiphysics fluid flow processes are often computationally expensive. On the other hand, less accurate low-fidelity models could be efficiently executed to provide an approximation to the solution. Multi-fidelity approaches combine high-fidelity and low-fidelity data and/or models to obtain a desirable balance between computational efficiency and accuracy. In the proposed approach, transfer learning based on low-fidelity CFD data is used to initial- ize PINN, which is then used to obtain the final results without any high-fidelity training data. Several partial differential equations are solved to predict velocity and temperature in different fluid flow, heat transfer, and porous media transport problems. The proposed approach significantly improves the accuracy of low-fidelity CFD data and also improves the convergence speed and accuracy of PINN. The second approach is an ensemble PINN (ePINN) method that is proposed to solve the uniqueness issue of inverse problems. In inverse modeling, measurement data are used to estimate unknown parameters that vary in space. However, due to the spatial variability of these unknown parameters in heterogeneous systems (e.g., permeability or diffusivity), the inverse problem is ill-posed and infinite solutions are possible. PINN has become a popular approach for solving inverse problems but is sensitive to hyperparameters and can produce unrealistic patterns. The ePINN approach uses an ensemble of parallel neural networks that are initialized with a meaningful pattern of the unknown parameter. These parallel networks provide a basis that is fed into a main neural network that is trained using PINN. It is shown that an appropriately selected set of patterns can guide PINN in producing more realistic results that are relevant to the problem of interest. The proposed ePINN approach increases the accuracy in inverse problems and mitigates the challenges associated with non-uniqueness. The third approach is a novel method called ensemble deep operator neural network (eDeepONet), which is designed to solve the solution operators of partial differential equa- tions (PDEs) using deep neural networks. eDeepONet involves training multiple sub-DeepONets on smaller subsets of the dataset, which are then combined in a fully connected neural network to predict the final solution. eDeepONet reduces the complexity of the training process, improves convergence, and provides more accurate solutions compared to the tra- ditional DeepONet approach. Additionally, eDeepONet is designed to handle parametric PDE equations and does not require explicit knowledge of the PDE equation or its bound- ary conditions, making it more flexible and applicable in a wider range of applications. The effectiveness of eDeepONet in enhancing prediction accuracy and improving convergence is demonstrated on a 2D diffusion problem. Overall, the proposed approaches demonstrate the potential of deep learning methods in solving challenging fluid flow and transport problems in homogeneous and heterogeneous systems. The multi-fidelity approach improves the accuracy of low-fidelity data and reduces computational cost. The ePINN approach mitigates the challenges associated with non- uniqueness in inverse problems. The eDeepONet approach reduces the complexity of the training process, improves convergence, and provides more accurate solutions for PDEs. These advances in deep learning methods have the potential to revolutionize our ability to model and predict fluid flow and transport in a wide range of applications