Matching Items (173)
Description
Concrete stands at the forefront of the construction industry as one of the most useful building materials. Economic and efficient improvements in concrete strengthening and manufacturing are widely sought to continuously improve the performance of the material. Fiber reinforcement is a significant technique in strengthening precast concrete, but manufacturing limitations are common which has led to reliance on steel reinforcement. Two-dimensional textile reinforcement has emerged as a strong and efficient alternative to both fiber and steel reinforced concrete with pultrusion manufacturing shown as one of the most effective methods of precasting concrete. The intention of this thesis project is to detail the components, functions, and outcomes shown in the development of an automated pultrusion system for manufacturing textile reinforced concrete (TRC). Using a preexisting, manual pultrusion system and current-day manufacturing techniques as a basis, the automated pultrusion system was designed as a series of five stations that centered on textile impregnation, system driving, and final pressing. The system was then constructed in the Arizona State University Structures Lab over the course of the spring and summer of 2015. After fabricating each station, a computer VI was coded in LabVIEW software to automatically drive the system. Upon completing construction of the system, plate and angled structural sections were then manufactured to verify the adequacy of the technique. Pultruded TRC plates were tested in tension and flexure while full-scale structural sections were tested in tension and compression. Ultimately, the automated pultrusion system was successful in establishing an efficient and consistent manufacturing process for continuous TRC sections.
ContributorsBauchmoyer, Jacob Macgregor (Author) / Mobasher, Barzin (Thesis director) / Neithalath, Narayanan (Committee member) / Civil, Environmental and Sustainable Engineering Programs (Contributor) / The Design School (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
Manufacture of building materials requires significant energy, and as demand for these materials continues to increase, the energy requirement will as well. Offsetting this energy use will require increased focus on sustainable building materials. Further, the energy used in building, particularly in heating and air conditioning, accounts for 40 percent of a buildings energy use. Increasing the efficiency of building materials will reduce energy usage over the life time of the building. Current methods for maintaining the interior environment can be highly inefficient depending on the building materials selected. Materials such as concrete have low thermal efficiency and have a low heat capacity meaning it provides little insulation. Use of phase change materials (PCM) provides the opportunity to increase environmental efficiency of buildings by using the inherent latent heat storage as well as the increased heat capacity. Incorporating PCM into concrete via lightweight aggregates (LWA) by direct addition is seen as a viable option for increasing the thermal storage capabilities of concrete, thereby increasing building energy efficiency. As PCM change phase from solid to liquid, heat is absorbed from the surroundings, decreasing the demand on the air conditioning systems on a hot day or vice versa on a cold day. Further these materials provide an additional insulating capacity above the value of plain concrete. When the temperature drops outside the PCM turns back into a solid and releases the energy stored from the day. PCM is a hydrophobic material and causes reductions in compressive strength when incorporated directly into concrete, as shown in previous studies. A proposed method for mitigating this detrimental effect, while still incorporating PCM into concrete is to encapsulate the PCM in aggregate. This technique would, in theory, allow for the use of phase change materials directly in concrete, increasing the thermal efficiency of buildings, while negating the negative effect on compressive strength of the material.
ContributorsSharma, Breeann (Author) / Neithalath, Narayanan (Thesis advisor) / Mobasher, Barzin (Committee member) / Rajan, Subramaniam D. (Committee member) / Arizona State University (Publisher)
Created2013
Description
Fraud is defined as the utilization of deception for illegal gain by hiding the true nature of the activity. While organizations lose around $3.7 trillion in revenue due to financial crimes and fraud worldwide, they can affect all levels of society significantly. In this dissertation, I focus on credit card fraud in online transactions. Every online transaction comes with a fraud risk and it is the merchant's liability to detect and stop fraudulent transactions. Merchants utilize various mechanisms to prevent and manage fraud such as automated fraud detection systems and manual transaction reviews by expert fraud analysts. Many proposed solutions mostly focus on fraud detection accuracy and ignore financial considerations. Also, the highly effective manual review process is overlooked. First, I propose Profit Optimizing Neural Risk Manager (PONRM), a selective classifier that (a) constitutes optimal collaboration between machine learning models and human expertise under industrial constraints, (b) is cost and profit sensitive. I suggest directions on how to characterize fraudulent behavior and assess the risk of a transaction. I show that my framework outperforms cost-sensitive and cost-insensitive baselines on three real-world merchant datasets. While PONRM is able to work with many supervised learners and obtain convincing results, utilizing probability outputs directly from the trained model itself can pose problems, especially in deep learning as softmax output is not a true uncertainty measure. This phenomenon, and the wide and rapid adoption of deep learning by practitioners brought unintended consequences in many situations such as in the infamous case of Google Photos' racist image recognition algorithm; thus, necessitated the utilization of the quantified uncertainty for each prediction. There have been recent efforts towards quantifying uncertainty in conventional deep learning methods (e.g., dropout as Bayesian approximation); however, their optimal use in decision making is often overlooked and understudied. Thus, I present a mixed-integer programming framework for selective classification called MIPSC, that investigates and combines model uncertainty and predictive mean to identify optimal classification and rejection regions. I also extend this framework to cost-sensitive settings (MIPCSC) and focus on the critical real-world problem, online fraud management and show that my approach outperforms industry standard methods significantly for online fraud management in real-world settings.
ContributorsYildirim, Mehmet Yigit (Author) / Davulcu, Hasan (Thesis advisor) / Bakkaloglu, Bertan (Committee member) / Huang, Dijiang (Committee member) / Hsiao, Ihan (Committee member) / Arizona State University (Publisher)
Created2019
Description
Realizing the applications of Internet of Things (IoT) with the goal of achieving a more efficient and automated world requires billions of connected smart devices and the minimization of hardware cost in these devices. As a result, many IoT devices do not have sufficient resources to support various protocols required in many IoT applications. Because of this, new protocols have been introduced to support the integration of these devices. One of these protocols is the increasingly popular routing protocol for low-power and lossy networks (RPL). However, this protocol is well known to attract blackhole and sinkhole attacks and cause serious difficulties when using more computationally intensive techniques to protect against these attacks, such as intrusion detection systems and rank authentication schemes. In this paper, an effective approach is presented to protect RPL networks against blackhole attacks. The approach does not address sinkhole attacks because they cause low damage and are often used along blackhole attacks and can be detected when blackhole attaches are detected. This approach uses the feature of multiple parents per node and a parent evaluation system enabling nodes to select more reliable routes. Simulations have been conducted, compared to existing approaches this approach would provide better protection against blackhole attacks with much lower overheads for small RPL networks.
ContributorsSanders, Kent (Author) / Yau, Stephen S (Thesis advisor) / Huang, Dijiang (Committee member) / Sen, Arunabha (Committee member) / Arizona State University (Publisher)
Created2021
Description
Corrosion is known to have severe infrastructure integrity implications in a broad range of industries including water and wastewater treatment and reclamation. In the U.S. alone, the total losses due to corrosion in drinking water and wastewater systems can account for economic losses as high as $80 billion dollars a year. Microbially induced corrosion is a complex phenomenon which involve various phases; 1) formation of biofilms on submerged surfaces, 2) creation of micro-environmental niches associated with biofilm growth, 3) altered availability nutrients, 4) changes in the pH and oxygen concentrations. Biofilms can harbor opportunistic or pathogenic bacteria for a long time increasing the risk of pathogen exposure for the end users. The focus of this thesis research was to study the kinetics of microbially induced corrosion of various materials in water and reclaimed water systems. The specific objective was to assess the biofilms formation potential on stainless steel 304, stainless steel 316, galvanized steel, copper, cPVC, glass, carbon steel, and cast iron in water and reclaimed water systems. Experiments were conducted using bioreactor containers, each bioreactor housed four sampling boxes with eight partitions, dedicated to each material type coupon. One bioreactor was stationed at ASU, and one at Vistancia Aquifer Storage and Recovery (ASR) well; while three bioreactors were stationed at Butler facility, at pre-disinfection, post-UV and post-chlorination. From each location, one submerged sampling box was retrieved after 1, 3, 6 and 12 months. Time series of biofilm samples recovered from various types of coupons from different locations were analyzed using physical and culture-based techniques for quantification of biofilms and detection of heterotrophic plate count (HPC) bacteria, Legionella, Mycobacterium, and sulfate reducing bacteria (SRB).
After one-year, galvanized steel had the highest concentration of HPC at 4.27 logs while copper had the lowest concentration of 3.08 logs of HPC. Bacterial growth data collected from the SRB tests was compiled to develop a numerical matrix using growth potential, biofilm formation potential and metal reduction potential of SRB isolates. This risk assessment matrix can be a useful tool for the water industry to evaluate the potential risk of MIC in their systems.
ContributorsNeal, Amber (Author) / Abbaszadegan, Morteza (Thesis advisor) / Fox, Peter (Committee member) / Alum, Absar (Committee member) / Arizona State University (Publisher)
Created2022
Description
Megapolitan cities have emerged due to unprecedented urban migration. These changes strain urban resources, especially water distribution and treatment systems. The recent rise of Legionella cases linked to water distribution systems highlights this issue.Bacterial growth and biofilm formation are influenced by factors, such as type and concentration of residual disinfectant, pipe material, water temperature. Experiments were conducted in identical model water distribution systems (WDSs) constructed of three different pipe materials: galvanized steel, copper, and cross-linked polyethylene (PEX) operated under a continuous flow rate of 15 L/min. Each model WDS includes 11 steel coupons screwed to the water distribution pipes. City of Tempe (Arizona) municipal water was used in the experimentation, with no nutrients added. Following biofilm growth, coupons were removed and processed by scrubbing biofilm into
phosphate-buffered saline (PBS). Reasoner's 2A (R2A), Trypticase Soy Agar (TSA), Brilliant, and buffered charcoal yeast extract (BCYE) agar media were used to examine biofilm samples for heterotrophic plate counts (HPC), metabolically active bacteria, E coli,
and Legionella. Simultaneously, water samples from the reservoirs of model WDSs were also collected and examined for the same bacteria.Next, an electrochlorination cell maintained free chlorine residuals in unheated PEX and copper model WDSs. These two systems maintained free chlorine residuals for one week and evaluated biofilm and bacterial kinetics. Higher water temperature increased biofilm development. Bacterial counts in biofilms were higher on new (fresh) coupons compared to the old coupons. Heterotrophic and metabolically active bacteria behaved similarly. Only control and heating systems in copper water reservoirs have Legionella spp. Biofilms
formed less on copper systems than steel and PEX systems. Initially, PEX had more HPC than copper. After electrochlorination, HPC concentration in the PEX system rapidly declined to non-detect, whereas in the copper system dropped to 0.54 log CFU/mL.
Thus, higher temperature increases biofilm growth on all pipe materials and reservoirs bacterial concentration. Electrochlorination is a potential biofilm and microbial disinfection method. This thesis topic investigated how these parameters affect the model distribution system bacterial populations and biofilm growth.
ContributorsKolahi Kouchaki, Bita (Author) / Abbaszadegan, Morteza (Thesis advisor) / Alum, Absar (Committee member) / Perreault, Francois (Committee member) / Arizona State University (Publisher)
Created2022
Description
Bacterial biofilms exist on surfaces within pressurized water systems, posing threats to water quality and causing fouling or microbial induced corrosion. Germicidal UV irradiation has shown promise in deactivating planktonic pathogens in water but challenges in delivering light to surfaces where biofilms exist have limited advancement in understanding biofilm response to UV-C light. This dissertation aims to overcome the limitation of delivering UV-C light through use of side-emitting optical fibers (SEOFs), advance capabilities to produce SEOFs and understand if a minimum UV-C irradiance can prevent biofilm formation. Two scalable manufacturing approaches were developed for producing kilometer lengths of thin (≤500-µm) and physically flexible SEOFs. One strategy involved dip-coating amine-functionalized SiO2 nanoparticles (NPs) on bare optical fiber, followed by a coating of UV-C transparent polymer (CyTop). I showed that NPs closer to the surface achieved with higher ionic strength solutions increased side-scattering of UV-C light. This phenomenon was primarily attributed to the interaction between NPs and evanescent wave energy. The second strategy omitted NPs but utilized a post-treatment to the UV-C transparent polymer that increased surface roughness on the outer fiber surface. This modification maintained the physical flexibility of the fiber while promoting side-emission of UV-C light. The side emission was due to the enhancement of refracted light energy. Both methods were successfully scaled up for potential commercial production.
Experimental platforms were created to study biofilm responses to UV light on metal or flexible plastic pipe (1/4” ID) surfaces. Delivering UV-C light via SEOFs with irradiances >8 µW/cm2 inhibited biofilm accumulation. Neither UV-A nor UV-B light inhibited biofilm growth. At very low UV-C irradiance (<3 µW/cm2), biofilms were not inhibited. Functional genomic analysis revealed that biofilms irradiated by insufficient UV-C irradiance upregulated various essential genes related to DNA repair, energy metabolism, quorum sensing, mobility, and EPS synthesis. When net UV-C biofilm inactivation rates exceeded the biofilm growth rate, biofilms were inhibited. Insights gained from this dissertation work shed light on the prospective applications of UV-C technology in addressing biofilm challenges within water infrastructure across multiple sectors, from potable water to healthcare applications.
ContributorsZhao, Zhe (Author) / Westerhoff, Paul (Thesis advisor) / Rittmann, Bruce (Committee member) / Abbaszadegan, Morteza (Committee member) / Álvarez, Pedro (Committee member) / Arizona State University (Publisher)
Created2023
Description
Water quality assessment is essential for maintaining healthy ecosystems and protecting human health. Data interrogation and exploratory data analysis techniques are used to analyze the spatial and temporal variability of water quality parameters, identifying correlations, and to better understand the factors that impacts microbial and chemical quality of water. The seasonal dynamics of microbiome in surface waters were investigated to identify the factors driving these dynamics. Initial investigation analyzed two decades of regional water quality data from 20 various locations in Central Arizona, USA. Leveraging advanced data science techniques, the study uncovered correlations between crucial parameters, including dissolved organic carbon (DOC), ultraviolet absorbance (UVA), and specific ultraviolet absorbance (SUVA). These findings provide foundational insights into the dynamic of overall water quality. A comprehensive 12-month surface water sample collection and study was conducted to investigate potential bias in bacterial detection using EPA approved Membrane Filtration (MF) technique. The results underscore that while MF excels in recovering bacteria of public health significance, it exhibits biases, particularly against small and spore-forming bacteria and Archaea, such as Bacilli, Mollicutes, Methylacidiphilae, and Parvarchaea. This emphasizes the importance of complementing standard microbiology approaches to mitigate technological biases and enhance the accuracy of microbial water quality testing, especially for emerging pathogens. Furthermore, a complementary study of microbial dynamics within a model drinking water distribution systems (DWDSs) using treated water from the same source water as the above study. The influence of pipe material and water temperature on the microbiome and trace element composition was investigated. The research unveiled a preferential link between pipe material and trace elements, with water temperature significantly impacting the microbiome to a higher degree than the chemical composition of water. Notably, Legionellaceae and Mycobacteriaceae were found to be prevalent in warmer waters, highlighting the substantial influence of water temperature on the microbiome, surpassing that of pipe material. These studies provide comprehensive insights into the spatial and temporal variability of water quality parameters. Analyzing microbial data in depth is crucial in detecting bacterial species within a monitoring program for adjusting operational conditions to reduce the presence of microbial pathogens and enhance the quality of drinking water.
ContributorsAloraini, Saleh (Author) / Abbaszadegan, Morteza (Thesis advisor) / Fox, Peter (Committee member) / Perreault, Francois (Committee member) / Alum, Absar (Committee member) / Arizona State University (Publisher)
Created2023
Description
This dissertation focused on studying risks associated with emerging drinking water contaminants and tradeoffs related to water management interventions. The built environment impacts health, as humans on average spend ~90% of their time indoors. Federal regulations generally focus on drinking water at the water treatment plant and within the distribution system as opposed to when it enters buildings after crossing the property line. If drinking water is not properly managed in buildings, it can be a source or amplifier of microbial and chemical contaminants. Unlike regulations for chemical contaminants that are risk-based, for pathogens, regulations are either based on recommended treatment technologies or designated as zero, which is not achievable in practice. Practice-based judgments are typically made at the building level to maintain water quality. This research focuses on two drinking water opportunistic pathogens of public health concern, Legionella pneumophila and Mycobacterium avium complex (MAC). Multiple aspects of drinking water quality in two green buildings were monitored in tandem with water management interventions. Additionally, a quantitative microbial risk assessment framework was used to predict risk-based critical concentrations of MAC for drinking water-related exposures in the indoor environment corresponding to a 1 in 10,000 annual infection target risk benchmark. The overall goal of this work was to inform the development of water management plans and guidelines for buildings that will improve water quality in the built environment and promote better public health. It was determined that a whole building water softening system with ion exchange softening resin and expansion tanks were unexplored reservoirs for the colonization of L. pneumophila. Furthermore, it was observed that typical water management interventions such as flushing and thermal disinfection did not always mitigate water quality issues. Thus, there was a need to implement several atypical interventions such as equipment replacement to improve the building water quality. This work has contributed comprehensive field studies and models that have highlighted the need for additional niches, facility management challenges, and risk tradeoffs for focus in water safety plans. The work also informs additional risk-based water quality policy approaches for reducing drinking water risks.
ContributorsJoshi, Sayalee (Author) / Hamilton, Kerry A (Thesis advisor) / Abbaszadegan, Morteza (Committee member) / Conroy-Ben, Otakuye (Committee member) / Halden, Rolf (Committee member) / Arizona State University (Publisher)
Created2023
Description
High-temperature mechanical behaviors of metal alloys and underlying microstructural variations responsible for such behaviors are essential areas of interest for many industries, particularly for applications such as jet engines. Anisotropic grain structures, change of preferred grain orientation, and other transformations of grains occur both during metal powder bed fusion additive manufacturing processes, due to variation of thermal gradient and cooling rates, and afterward during different thermomechanical loads, which parts experience in their specific applications, could also impact its mechanical properties both at room and high temperatures. In this study, an in-depth analysis of how different microstructural features, such as crystallographic texture, grain size, grain boundary misorientation angles, and inherent defects, as byproducts of electron beam powder bed fusion (EB-PBF) AM process, impact its anisotropic mechanical behaviors and softening behaviors due to interacting mechanisms. Mechanical testing is conducted for EB-PBF Ti6Al4V parts made at different build orientations up to 600°C temperature. Microstructural analysis using electron backscattered diffraction (EBSD) is conducted on samples before and after mechanical testing to understand the interacting impact that temperature and mechanical load have on the activation of certain mechanisms. The vertical samples showed larger grain sizes, with an average of 6.6 µm, a lower average misorientation angle, and subsequently lower strength values than the other two horizontal samples. Among the three strong preferred grain orientations of the α phases, <1 1 2 ̅ 1> and <1 1 2 ̅ 0> were dominant in horizontally built samples, whereas the <0 0 0 1> was dominant in vertically built samples. Thus, strong microstructural variation, as observed among different EB-PBF Ti6Al4V samples, mainly resulted in anisotropic behaviors. Furthermore, alpha grain showed a significant increase in average grain size for all samples with the increasing test temperature, especially from 400°C to 600°C, indicating grain growth and coarsening as potential softening mechanisms along with temperature-induced possible dislocation motion. The severity of internal and external defects on fatigue strength has been evaluated non-destructively using quantitative methods, i.e., Murakami’s square root of area parameter model and Basquin’s model, and the external surface defects were rendered to be more critical as potential crack initiation sites.
ContributorsMian, Md Jamal (Author) / Ladani, Leila (Thesis advisor) / Razmi, Jafar (Committee member) / Shuaib, Abdelrahman (Committee member) / Mobasher, Barzin (Committee member) / Nian, Qiong (Committee member) / Arizona State University (Publisher)
Created2022