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Description
An orthotropic elasto-plastic damage material model (OEPDMM) suitable for impact analysis of composite materials has been developed through a joint research project funded by the Federal Aviation Administration (FAA) and the National Aeronautics and Space Administration (NASA). The developed material model has been implemented into LS-DYNA®, a commercial finite element program. The material model is modular comprising of deformation, damage and failure sub-models. The deformation sub-model captures the rate and/or temperature dependent elastic and inelastic behavior via a visco-elastic-plastic formulation. The damage sub-model predicts the reduction in the elastic stiffness of the material. The failure sub-model predicts when there is no more load carrying capacity in the finite element and erosion of the element from the finite element model. Most of the input parameters required to drive OEPDMM are in the form of tabulated data. The deformation sub-model is driven by a set of tabulated stress-strain data for a given strain-rate and temperature combination. The damage sub-model is driven by tabulated damage parameter-strain data. Two failure sub-models have been implemented – Puck Failure Model and Generalized Tabulated Failure Model. Puck Failure Model requires scalar parameters as input whereas, the Generalized Tabulated Failure Model is driven by a set of equivalent failure strain tabulated data. The work presented here focuses on the enhancements made to OEPDMM with emphasis on the background, development, and implementation of the failure sub-models. OEPDMM is verified and validated using a carbon/epoxy fiber reinforced composite. Two validation tests are used to evaluate the failure sub-model implementation - a stacked-ply test carried out at room temperature under quasi-static tensile and compressive loadings, and several high-speed impact tests where there is significant damage and material failure of the impacted panel. Results indicate that developed procedures provide the analyst with a reasonable and systematic approach to building predictive impact simulation models.
ContributorsLoukham, Shyamsunder (Author) / Rajan, Subramaniam SR (Thesis advisor) / Neithalath, Narayanan NN (Committee member) / Mobasher, Barzin BM (Committee member) / Hoover, Christian CH (Committee member) / Liu, Yongming YL (Committee member) / Arizona State University (Publisher)
Created2020
Description
First year college students have been identified as a vulnerable population for weight gain and the onset of overweight and obesity. Research regarding the gut microbiome has identified differences in the microbial composition of overweight and obese individuals compared to normal weight individuals. Dietary components like dietary fibers, act as prebiotics, or fermentable substrate, that the gut microbiota use for metabolic functions including the production of short-chain fatty acids. The objective of this longitudinal, observational study was to assess changes in the gut microbiota over time in relation to changes in fiber consumption in healthy college students at a large a southwestern university (n=137). Anthropometric and fecal samples were collected at the beginning and end of the fall and spring semesters between August 2015 and May 2016. Both alpha, within sample, diversity and beta, between sample, diversity of participant gut microbes were assessed longitudinally using non-parametric pairwise (pre-post) comparisons and linear mixed effect (LME) models which also adjusted for covariates and accounted for time as a random effect. Alpha and beta diversity were also explored using LME first difference metrics and LME first distance metrics, respectively, to understand rates of change over time in microbial richness/phylogeny and community structure. Pre-post comparisons of Shannon Diversity and Faith’s PD were not significantly different within participant groups of fiber change (Shannon diversity, p=0.96 and Faith’s PD, p=0.66). Beta diversity pairwise comparisons also did not differ by fiber consumption groups (Unweighted UniFrac p=0.182 and Bray Curtis p=0.657). Similarly, none of the LME models suggested significant associations between dietary fiber consumption and metrics of alpha and beta diversity. Overall, data from this study indicates that small changes in fiber consumption among a free-living population did not have an impact on gut microbial richness, phylogeny or community structure. This may have been due to the low intake (~15 g/d) of fiber. Further study is needed to fully elucidate the role that fiber plays in the diversity and composition of the gut microbiota, especially when delivered from a variety of food sources rather than fiber supplements.
ContributorsLolley, Sarah (Author) / Whisner, Corrie (Thesis advisor) / Sears, Dorothy (Committee member) / Shepard, Christina (Committee member) / Arizona State University (Publisher)
Created2020
Description
Imagery data has become important for civil infrastructure operation and
maintenance because imagery data can capture detailed visual information with high
frequencies. Computer vision can be useful for acquiring spatiotemporal details to
support the timely maintenance of critical civil infrastructures that serve society. Some
examples include: irrigation canals need to maintain the leaking sections to avoid water
loss; project engineers need to identify the deviating parts of the workflow to have the
project finished on time and within budget; detecting abnormal behaviors of air traffic
controllers is necessary to reduce operational errors and avoid air traffic accidents.
Identifying the outliers of the civil infrastructure can help engineers focus on targeted
areas. However, large amounts of imagery data bring the difficulty of information
overloading. Anomaly detection combined with contextual knowledge could help address
such information overloading to support the operation and maintenance of civil
infrastructures.
Some challenges make such identification of anomalies difficult. The first challenge is
that diverse large civil infrastructures span among various geospatial environments so
that previous algorithms cannot handle anomaly detection of civil infrastructures in
different environments. The second challenge is that the crowded and rapidly changing
workspaces can cause difficulties for the reliable detection of deviating parts of the
workflow. The third challenge is that limited studies examined how to detect abnormal
behaviors for diverse people in a real-time and non-intrusive manner. Using video andii
relevant data sources (e.g., biometric and communication data) could be promising but
still need a baseline of normal behaviors for outlier detection.
This dissertation presents an anomaly detection framework that uses contextual
knowledge, contextual information, and contextual data for filtering visual information
extracted by computer vision techniques (ADCV) to address the challenges described
above. The framework categorizes the anomaly detection of civil infrastructures into two
categories: with and without a baseline of normal events. The author uses three case
studies to illustrate how the developed approaches can address ADCV challenges in
different categories of anomaly detection. Detailed data collection and experiments
validate the developed ADCV approaches.
maintenance because imagery data can capture detailed visual information with high
frequencies. Computer vision can be useful for acquiring spatiotemporal details to
support the timely maintenance of critical civil infrastructures that serve society. Some
examples include: irrigation canals need to maintain the leaking sections to avoid water
loss; project engineers need to identify the deviating parts of the workflow to have the
project finished on time and within budget; detecting abnormal behaviors of air traffic
controllers is necessary to reduce operational errors and avoid air traffic accidents.
Identifying the outliers of the civil infrastructure can help engineers focus on targeted
areas. However, large amounts of imagery data bring the difficulty of information
overloading. Anomaly detection combined with contextual knowledge could help address
such information overloading to support the operation and maintenance of civil
infrastructures.
Some challenges make such identification of anomalies difficult. The first challenge is
that diverse large civil infrastructures span among various geospatial environments so
that previous algorithms cannot handle anomaly detection of civil infrastructures in
different environments. The second challenge is that the crowded and rapidly changing
workspaces can cause difficulties for the reliable detection of deviating parts of the
workflow. The third challenge is that limited studies examined how to detect abnormal
behaviors for diverse people in a real-time and non-intrusive manner. Using video andii
relevant data sources (e.g., biometric and communication data) could be promising but
still need a baseline of normal behaviors for outlier detection.
This dissertation presents an anomaly detection framework that uses contextual
knowledge, contextual information, and contextual data for filtering visual information
extracted by computer vision techniques (ADCV) to address the challenges described
above. The framework categorizes the anomaly detection of civil infrastructures into two
categories: with and without a baseline of normal events. The author uses three case
studies to illustrate how the developed approaches can address ADCV challenges in
different categories of anomaly detection. Detailed data collection and experiments
validate the developed ADCV approaches.
ContributorsChen, Jiawei (Author) / Tang, Pingbo (Thesis advisor) / Ayer, Steven (Committee member) / Yang, Yezhou (Committee member) / Arizona State University (Publisher)
Created2020
Description
In the United States, two-thirds of adults are considered hypertensive orprehypertensive. In addition, chronic illness, such as hypertension, cardiovascular disease, and type II diabetes, results in $3.5 trillion in annual healthcare cost and is the primary cause of disability and death. As a result, many individuals seek cheaper and simpler alternatives to combat their conditions. In this exploratory analysis, a study assessing nitrate intake and its effects on vascular function in 39 young adult males was investigated for underlying metabolic variations through a liquid chromatography – mass spectrometry-based large-scale targeted metabolomics approach. A two-way repeated measures ANOVA was used, and 18 significant metabolites were discovered across the time, treatment, and time & treatment groups, including prostaglandin E2 (p<0.001), stearic acid (p=0.002), caprylic acid (p=0.016), pentadecanoic acid (p=0.027), and heptadecanoic acid (p=0.005). In addition, log-transformed principal component analysis and orthogonal partial least squares – discriminant analysis models demonstrated distinct separation among the treatment, control, and time variables. Moreover, pathway and enrichment analyses validated the effect of nitrate intake on the metabolite sets and its possible function in fatty acid oxidation. This better understanding of altered metabolic pathways may help explicate the benefits of nitrate on vascular function and reveal any unknown mechanisms of its supplementation.
ContributorsPatterson, Jeffrey (Author) / Gu, Haiwei (Thesis advisor) / Johnston, Carol (Committee member) / Sweazea, Karen (Committee member) / Arizona State University (Publisher)
Created2020
Description
Ultra High Performance (UHP) cementitious binders are a class of cement-based materials with high strength and ductility, designed for use in precast bridge connections, bridge superstructures, high load-bearing structural members like columns, and in structural repair and strengthening. This dissertation aims to elucidate the chemo-mechanical relationships in complex UHP binders to facilitate better microstructure-based design of these materials and develop machine learning (ML) models to predict their scale-relevant properties from microstructural information.To establish the connection between micromechanical properties and constitutive materials, nanoindentation and scanning electron microscopy experiments are performed on several cementitious pastes. Following Bayesian statistical clustering, mixed reaction products with scattered nanomechanical properties are observed, attributable to the low degree of reaction of the constituent particles, enhanced particle packing, and very low water-to-binder ratio of UHP binders. Relating the phase chemistry to the micromechanical properties, the chemical intensity ratios of Ca/Si and Al/Si are found to be important parameters influencing the incorporation of Al into the C-S-H gel.
ML algorithms for classification of cementitious phases are found to require only the intensities of Ca, Si, and Al as inputs to generate accurate predictions for more homogeneous cement pastes. When applied to more complex UHP systems, the overlapping chemical intensities in the three dominant phases – Ultra High Stiffness (UHS), unreacted cementitious replacements, and clinker – led to ML models misidentifying these three phases. Similarly, a reduced amount of data available on the hard and stiff UHS phases prevents accurate ML regression predictions of the microstructural phase stiffness using only chemical information. The use of generic virtual two-phase microstructures coupled with finite element analysis is also adopted to train MLs to predict composite mechanical properties. This approach applied to three different representations of composite materials produces accurate predictions, thus providing an avenue for image-based microstructural characterization of multi-phase composites such UHP binders. This thesis provides insights into the microstructure of the complex, heterogeneous UHP binders and the utilization of big-data methods such as ML to predict their properties. These results are expected to provide means for rational, first-principles design of UHP mixtures.
ML algorithms for classification of cementitious phases are found to require only the intensities of Ca, Si, and Al as inputs to generate accurate predictions for more homogeneous cement pastes. When applied to more complex UHP systems, the overlapping chemical intensities in the three dominant phases – Ultra High Stiffness (UHS), unreacted cementitious replacements, and clinker – led to ML models misidentifying these three phases. Similarly, a reduced amount of data available on the hard and stiff UHS phases prevents accurate ML regression predictions of the microstructural phase stiffness using only chemical information. The use of generic virtual two-phase microstructures coupled with finite element analysis is also adopted to train MLs to predict composite mechanical properties. This approach applied to three different representations of composite materials produces accurate predictions, thus providing an avenue for image-based microstructural characterization of multi-phase composites such UHP binders. This thesis provides insights into the microstructure of the complex, heterogeneous UHP binders and the utilization of big-data methods such as ML to predict their properties. These results are expected to provide means for rational, first-principles design of UHP mixtures.
ContributorsFord, Emily Lucile (Author) / Neithalath, Narayanan (Thesis advisor) / Rajan, Subramaniam D. (Committee member) / Mobasher, Barzin (Committee member) / Chawla, Nikhilesh (Committee member) / Hoover, Christian G. (Committee member) / Maneparambil, Kailas (Committee member) / Arizona State University (Publisher)
Created2020
Description
The alternative project delivery methods (APDMs) today are being increasingly used by owner organizations in the architecture, engineering, and construction (AEC) industry. Yet the adoption of these methods can be extremely difficult to accomplish and requires significant change management efforts. To facilitate the APDM adoption, this research aimed to better understand how AEC owner organizations have changed from only using the design-bid-build method to also successfully implementing APDMs from an organizational change perspective. This research utilized a literature review, survey and interviews to fulfill the research objectives. The dissertation follows a three paper format. The first paper focuses on identifying organizational change management (OCM) practices that, when effectively executed, lead to increased success rates of adopting APDMs in owner AEC organizations. The results of the first paper indicated that the five OCM practices with the strongest correlations to successful APDM adoption were realistic timeframe, effective change agents, workload adjustments, senior-leadership commitment, and sufficient change-related training. The second paper focuses on investigating AEC employees’ reactions to the adoption of APDMs. The findings of the second paper revealed that employees in AEC organizations react favorably to adopting a change in their project delivery systems. The findings further revealed that increasing the use of OCM practices is related to decreased employee resistance to change. The third paper aimed to provide guidelines detailing on how to lead APDM adoption. The findings of the third paper indicated that there was a general sequence of four implementation phases, which were preparing and planning, pilot project testing, expanding to the intended scale, and sustaining and evaluating. The phases include specific OCM practices that increase the probability of successful APDM adoption. The dissertation results can help in guiding the senior managers of construction organizations and OCM consultants to effectively implement APDMs for the first time in the construction sector.
ContributorsAldossari, Khaled Medath (Author) / Sullivan, Kenneth T. (Thesis advisor) / Hurtado, Kristen C (Committee member) / Standage, Richard (Committee member) / Arizona State University (Publisher)
Created2020
Description
The construction industry generates tremendous amounts of data every day. Data can inform practitioners to increase their project performance as well as the quality of the resulting built environment. The data gathered from each stage has unique characteristics, and processing them to the appropriate information is critical. However, it is often difficult to measure the impact of the research across project phases (i.e., planning, design, construction, operation and maintenance, and end-of-life). The goal of this dissertation is to present how industry data can be used to make an impact on construction practices and test a suite of methods to measure the impact of construction research across project phases. The dissertation provides examples of impactful research studies for each project phase to demonstrate the collection and utilization of data generated from each stage and to assess the potential tangible impact on construction industry practices. The completed studies presented both quantitative and qualitative analyses. The first study focuses on the planning phase and provides a practice to improve frond end planning (FEP) implementation by developing the project definition rating index (PDRI) maturity and accuracy total rating system (MATRS). The second study uses earned value management system (EVMS) information from the design and construction phases to support reliable project control and management. The dissertation then provides a third study, this time focusing on the operations phase and comparing the impact of project delivery methods using the international roughness index (IRI). Lastly, the end-of-life or decommissioning phase is tackled through a study that gauges the monetary impact of the circular economy concept applied to reuse construction and demolition (C&D) waste. This dissertation measures the impact of the research according to the knowledge mobilization (KMb) theory, which illustrates the value of the work to the public and to practitioners.
ContributorsCho, Namho (Author) / El Asmar, Mounir (Thesis advisor) / Gibson, George (Committee member) / Kaloush, Kamil (Committee member) / Arizona State University (Publisher)
Created2020
Description
Travel time is the main transportation system performance measure used by the planning community to evaluate the impacts of traffic congestion on infrastructure investment projects and policy development plans. Planners rely on the travel demand model tool estimates for the selection and prioritization of critical and sensitive projects to meet the fiscally constraint requirements imposed by the Federal Highway Administration (FHWA) on their transportation improvement programs (TIP). While travel demand model estimates have been successfully implemented in the evaluation of project scenarios or alternatives, the application of the methods used in the travel demand model to generate these estimates continues to present a critical challenge, particularly to modelers who have to produce a validated model upon which traffic predictions can be made. The various volume-delay functions (VDFs) including the Bureau of Public Roads (BPR) function, used in the travel demand model to relate traffic volume to travel time, are developed based on system-wide attributes. BPR function in its polynomial form is computationally efficient and simple for implementation in a transport planning software. The planning community has long recognized that the BPR function cannot capture traffic flow dynamics and queue evolution processes. Besides, it has difficulties in using the average travel time measure to describe an oversaturated bottleneck with high density but low throughput. This dissertation aims to propose a simplified and yet effective point-queue based modeling approach built on the cumulative vehicle arrival concept, and the polynomial equation formula, based on Newell’s method, to estimate travel time at a corridor level using real-world speed and count measurements. A traffic state estimation (TSE) method is also proposed to characterize data into various states, such as congested state and uncongested state, using Markov Chain to capture current traffic pattern and Bayesian Classifier to infer congestion effects. As the testbed for the case study, the research selects the Phoenix freeway corridor with year-round traffic data collected from embedded traffic loop detectors. The results and effectiveness of the proposed methods are discussed to shed light on the calibration of link performance function, which is an analytical building block for system-wide performance evaluation.
ContributorsBelezamo, Baloka (Author) / Zhou, Xuesong (Thesis advisor) / Pendyala, Ram (Committee member) / Lou, Yingyan (Committee member) / Arizona State University (Publisher)
Created2020
Description
This document presents the assessment of the swelling behavior of expansive clay stabilized with bio-based silica gel and subjected to wetting and drying cycles. The expansive clay used in this research was obtained from Anthem, Arizona. Rice husk is a rich silica by-product of rice production with commercial uses and applications in the industry. Rice husk ash from two different sources -California (named ASU) and India- were subjected to chemical characterization. Fourier Transform Infra-red Spectroscopy was used to verify the functional groups of the gel formed. Results showed differences between the ashes from different sources and confirmed the presence of silica structure bonds. X-Ray Diffraction (XRF) results showed that the ASU ash contained more amorphous silica than the Indian ash.One dimensional swell and consolidation tests were performed to investigate the volume change behavior of the untreated and silica gel treated remoulded samples. The free swell of the clay decreased from 12.3% (untreated sample) to 7.2% (ASU sample) and 11.4% (Indian sample). The effect of the wet and dry cycles on the swelling and consolidation characteristics of the untreated clay demonstrated that the treatment is irreversible after three cycles. Swelling of clay treated with ASU ash was reduced after the first cycle, while that of the clay treated with Indian ash was reduced after three cycles. This was due to the gelation time difference between treatments. Scanning Electron Microscopy images showed that the structure of the untreated clay was discontinuous, flaky and without aggregations whereas particles in the treated samples were aggregated and new bonds were created, decreasing the surface area. The X-Ray Diffraction (XRF) results showed that the main mineral responsible for expansive behavior of the clay studied was illite. The d-spacing of the illite decreased from 4.47Å for the untreated clay to 3.33Å for the treated clay. This study demonstrates a promising technique for clay swelling reduction and a more sustainable solution than that available to current practicing engineering.
ContributorsBogere, Limon (Author) / Zapata, Claudia E (Thesis advisor) / Kavazanjian, Edward (Committee member) / Khodadaditirkolaei, Hamed (Committee member) / Arizona State University (Publisher)
Created2020
Description
Bio-modification of asphalt binder brings significant benefits in terms of increasing sustainable and environmental practices, stabilizing prices, and decreasing costs. However, bio-modified asphalt binders have shown varying performance regarding susceptibility to moisture damage; some bio-oil modifiers significantly increase asphalt binder's susceptibility to moisture damage. This variability in performance is largely due to the large number of bio-masses available for use as sources of bio-oil, as well as the type of processing procedure followed in converting the bio-mass into a bio-oil for modifying asphalt binder. Therefore, there is a need for a method of properly evaluating the potential impact of a bio-oil modifier for asphalt binder on the overall performance of asphalt pavement, in order to properly distinguish whether a particular bio-oil modifier increases or decreases the moisture susceptibility of asphalt binder. Therefore, the goal of this study is a multi-scale investigation of bio-oils with known chemical compositions to determine if there is a correlation between a fundamental property of a bio-oil and the resulting increase or decrease in moisture susceptibility of a binder when it is modified with the bio-oil. For instance, it was found that polarizability of asphalt constituents can be a promising indicator of moisture susceptibility of bitumen. This study will also evaluate the linkage of the fundamental property to newly developed binder-level test methods. It was found that moisture-induced shear thinning of bitumen containing glass beads can differentiate moisture susceptible bitumen samples. Based on the knowledge determined, alternative methods of reducing the moisture susceptibility of asphalt pavement will also be evaluated. It was shown that accumulation of acidic compounds at the interface of bitumen and aggregate could promote moisture damage. It was further found that detracting acidic compounds from the interface could be done by either of neutralizing active site of stone aggregate to reduce affinity for acids or by arresting acidic compounds using active mineral filler. The study results showed there is a strong relation between composition of bitumen and its susceptibility to moisture. This in turn emphasize the importance of integrating knowledge of surface chemistry and bitumen composition into the pavement design and evaluation.
ContributorsOldham, Daniel Joshua (Author) / Fini, Elham F (Thesis advisor) / Kaloush, Kamil (Committee member) / Deng, Shuguang (Committee member) / Mallick, Rajib B (Committee member) / Louie, Stacey M (Committee member) / Parast, Mahour M (Committee member) / Arizona State University (Publisher)
Created2020