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- Genre: Doctoral Dissertation
- Status: Published
Description
The activity-based approach to travel demand analysis and modeling, which has been developed over the past 30 years, has received tremendous success in transportation planning and policy analysis issues, capturing the multi-way joint relationships among socio-demographic, economic, land use characteristics, activity participation, and travel behavior. The development of synthesizing population with an array of socio-demographic and socio-economic attributes has drawn remarkable attention due to privacy and cost constraints in collecting and disclosing full scale data. Although, there has been enormous progress in producing synthetic population, there has been less progress in the development of population evolution modeling arena to forecast future year population. The objective of this dissertation is to develop a well-structured full-fledged demographic evolution modeling system, capturing migration dynamics and evolution of person level attributes, introducing the concept of new household formations and apprehending the dynamics of household level long-term choices over time. A comprehensive study has been conducted on demography, sociology, anthropology, economics and transportation engineering area to better understand the dynamics of evolutionary activities over time and their impacts in travel behavior. This dissertation describes the methodology and the conceptual framework, and the development of model components. Demographic, socio-economic, and land use data from American Community Survey, National Household Travel Survey, Census PUMS, United States Time Series Economic Dynamic data and United States Center for Disease Control and Prevention have been used in this research. The entire modeling system has been implemented and coded using programming language to develop the population evolution module named `PopEvol' into a computer simulation environment. The module then has been demonstrated for a portion of Maricopa County area in Arizona to predict the milestone year population to check the accuracy of forecasting. The module has also been used to evolve the base year population for next 15 years and the evolutionary trend has been investigated.
ContributorsPaul, Sanjay (Author) / Pendyala, Ram M. (Thesis advisor) / Kaloush, Kamil (Committee member) / Ahn, Soyoung (Committee member) / Arizona State University (Publisher)
Created2014
Description
The ocean is vital to the health of our planet but remains virtually unexplored. Many researchers seek to understand a wide range of geological and biological phenomena by developing technologies which enable exploration of the deep-sea. The task of developing a technology which can withstand extreme pressure and temperature gradients in the deep ocean is not trivial. Of these technologies, underwater vehicles were developed to study the deep ocean, but remain large and expensive to manufacture. I am proposing the development of cost efficient miniaturized underwater vehicle (mUV) with propulsion systems to carry small measurement devices and enable deep-sea exploration. These mUV's overall size is optimized based on the vehicle parameters such as energy density, desired velocity, swimming time and propulsion performance. However, there are limitations associated with the size of the mUV which leads to certain challenges. For example, 2000 m below the sea level, the pressure is as high as 3000 psi. Therefore, certain underwater vehicle modules, such as the propulsion system, will require pressure housing to ensure the functionality of the thrust generation. In the case of a mUV swimming against the deep-sea current, a thrust magnitude is required to enable the vehicle to overcome the ocean current speed and move forward. Therefore, the size of the mUV is limited by the energy density and the propeller size. An equation is derived to miniaturize underwater vehicle while performing with a certain specifications. An inrunner three-phase permanent magnet brushless DC motor is designed and fabricated with a specific size to fit inside the mUV's core. The motor is composed of stator winding in a pressure housing and an open to water ring-propeller rotor magnet. Several ring-propellers are 3D printed and tested experimentally to determine their performances and efficiencies. A planer motion optimal trajectory for the mUV is determined to minimize the energy usage. Those studies enable the design of size optimized underwater vehicle with propulsion to carry small measurement sensors and enable underwater exploration. Developing mUV's will enable ocean exploration that can lead to significant scientific discoveries and breakthroughs that will solve current world health and environmental problems.
ContributorsMerza, Saeed A (Author) / Meldrum, Deirdre R (Thesis advisor) / Chao, Shih-hui (Committee member) / Shankar, Praveen (Committee member) / Saripalli, Srikanth (Committee member) / Berman, Spring Melody (Committee member) / Arizona State University (Publisher)
Created2014
Description
The construction industry has accepted the uncertainty that is included with every project that is initiated. Because of the existing uncertainty, best practices with risk management are commonly recommended and educated to industry participants. However, the current status of the construction industry's ability to manage risk was found to be limited, unstructured, and inadequate. Furthermore, many barriers block organizations from implementing and improving risk management practices. A significant barrier with improving risk management methods is the lack of evidence that clearly demonstrates the need to improve risk management practices. Logical explanations of the benefits of risk management doesn't provide the necessary justification or motivation needed for many organizations to dedicate resources towards improving risk management.
Nevertheless, some organizations understand the importance of risk management practices and have begun to measure their risk maturity in order to identify weaknesses and improve risk management practices. Risk maturity measures the organization's ability and perceptions towards risk management. It is possible that many of the barriers to improving risk management would not exist if increased risk maturity was found to have a positive correlation with successful project performance.
The comprehensive hypothesis of the research is that increased risk maturity improves project performance. An exploratory study was conducted on data collected to identify measurable benefits with risk management. Quantitative and qualitative data was collected on 266 construction projects over a seven year period. Multiple statistical analyses were performed on the data and found a positive correlations between risk maturity and project performance. A positive correlations was found between customer satisfaction and contractors risk maturity. Additional findings from the recorded data included the increased ability to predict risks during construction projects within an organization. These findings provide clear reasoning for organizations to devote additional resources in which improve their risk management practices.
Nevertheless, some organizations understand the importance of risk management practices and have begun to measure their risk maturity in order to identify weaknesses and improve risk management practices. Risk maturity measures the organization's ability and perceptions towards risk management. It is possible that many of the barriers to improving risk management would not exist if increased risk maturity was found to have a positive correlation with successful project performance.
The comprehensive hypothesis of the research is that increased risk maturity improves project performance. An exploratory study was conducted on data collected to identify measurable benefits with risk management. Quantitative and qualitative data was collected on 266 construction projects over a seven year period. Multiple statistical analyses were performed on the data and found a positive correlations between risk maturity and project performance. A positive correlations was found between customer satisfaction and contractors risk maturity. Additional findings from the recorded data included the increased ability to predict risks during construction projects within an organization. These findings provide clear reasoning for organizations to devote additional resources in which improve their risk management practices.
ContributorsPerrenoud, Anthony (Author) / Sullivan, Kenneth T. (Thesis advisor) / Weizel, Avi (Committee member) / Badger, William (Committee member) / Arizona State University (Publisher)
Created2014
Description
Owner organizations in the architecture, engineering, and construction (AEC) industry are presented with a wide variety of project delivery approaches. Implementation of these approaches, while enticing due to their potential to save money, reduce schedule delays, or improve quality, is extremely difficult to accomplish and requires a concerted change management effort. Research in the field of organizational behavior cautions that perhaps more than half of all organizational change efforts fail to accomplish their intended objectives. This study utilizes an action research approach to analyze change message delivery within owner organizations, model owner project team readiness and adoption of change, and identify the most frequently encountered types of resistance from lead project members. The analysis methodology included Spearman's rank order correlation, variable selection testing via three methods of hierarchical linear regression, relative weight analysis, and one-way ANOVA. Key findings from this study include recommendations for communicating the change message within owner organizations, empirical validation of critical predictors for change readiness and change adoption among project teams, and identification of the most frequently encountered resistive behaviors within change implementation in the AEC industry. A key contribution of this research is the recommendation of change management strategies for use by change practitioners.
ContributorsLines, Brian (Author) / Sullivan, Kenneth (Thesis advisor) / Wiezel, Avi (Committee member) / Badger, William (Committee member) / Arizona State University (Publisher)
Created2014
Description
Concrete is the most widely used infrastructure material worldwide. Production of portland cement, the main binding component in concrete, has been shown to require significant energy and account for approximately 5-7% of global carbon dioxide production. The expected continued increased use of concrete over the coming decades indicates this is an ideal time to implement sustainable binder technologies. The current work aims to explore enhanced sustainability concretes, primarily in the context of limestone and flow. Aspects such as hydration kinetics, hydration product formation and pore structure add to the understanding of the strength development and potential durability characteristics of these binder systems. Two main strategies for enhancing this sustainability are explored in this work: (i) the use of high volume limestone in combination with other alternative cementitious materials to decrease the portland cement quantity in concrete and (ii) the use of geopolymers as the binder phase in concrete. The first phase of the work investigates the use of fine limestone as cement replacement from the perspective of hydration, strength development, and pore structure. The nature of the potential synergistic benefit of limestone and alumina will be explored. The second phase will focus on the rheological characterization of these materials in the fresh state, as well as a more general investigation of the rheological characterization of suspensions. The results of this work indicate several key ideas. (i) There is a potential synergistic benefit for strength, hydration, and pore structure by using alumina and in portland limestone cements, (ii) the limestone in these systems is shown to react to some extent, and fine limestone is shown to accelerate hydration, (iii) rheological characteristics of cementitious suspensions are complex, and strongly dependent on several key parameters including: the solid loading, interparticle forces, surface area of the particles present, particle size distribution of the particles, and rheological nature of the media in which the particles are suspended, and (iv) stress plateau method is proposed for the determination of rheological properties of concentrated suspensions, as it more accurately predicts apparent yield stress and is shown to correlate well with other viscoelastic properties of the suspensions.
ContributorsVance, Kirk (Author) / Neithalath, Narayanan (Thesis advisor) / Rajan, Subramaniam D. (Committee member) / Mobasher, Barzin (Committee member) / Chawla, Nikhilesh (Committee member) / Marzke, Robert (Committee member) / Arizona State University (Publisher)
Created2014
Description
Damage detection in heterogeneous material systems is a complex problem and requires an in-depth understanding of the material characteristics and response under varying load and environmental conditions. A significant amount of research has been conducted in this field to enhance the fidelity of damage assessment methodologies, using a wide range of sensors and detection techniques, for both metallic materials and composites. However, detecting damage at the microscale is not possible with commercially available sensors. A probable way to approach this problem is through accurate and efficient multiscale modeling techniques, which are capable of tracking damage initiation at the microscale and propagation across the length scales. The output from these models will provide an improved understanding of damage initiation; the knowledge can be used in conjunction with information from physical sensors to improve the size of detectable damage. In this research, effort has been dedicated to develop multiscale modeling approaches and associated damage criteria for the estimation of damage evolution across the relevant length scales. Important issues such as length and time scales, anisotropy and variability in material properties at the microscale, and response under mechanical and thermal loading are addressed. Two different material systems have been studied: metallic material and a novel stress-sensitive epoxy polymer.
For metallic material (Al 2024-T351), the methodology initiates at the microscale where extensive material characterization is conducted to capture the microstructural variability. A statistical volume element (SVE) model is constructed to represent the material properties. Geometric and crystallographic features including grain orientation, misorientation, size, shape, principal axis direction and aspect ratio are captured. This SVE model provides a computationally efficient alternative to traditional techniques using representative volume element (RVE) models while maintaining statistical accuracy. A physics based multiscale damage criterion is developed to simulate the fatigue crack initiation. The crack growth rate and probable directions are estimated simultaneously.
Mechanically sensitive materials that exhibit specific chemical reactions upon external loading are currently being investigated for self-sensing applications. The "smart" polymer modeled in this research consists of epoxy resin, hardener, and a stress-sensitive material called mechanophore The mechanophore activation is based on covalent bond-breaking induced by external stimuli; this feature can be used for material-level damage detections. In this work Tris-(Cinnamoyl oxymethyl)-Ethane (TCE) is used as the cyclobutane-based mechanophore (stress-sensitive) material in the polymer matrix. The TCE embedded polymers have shown promising results in early damage detection through mechanically induced fluorescence. A spring-bead based network model, which bridges nanoscale information to higher length scales, has been developed to model this material system. The material is partitioned into discrete mass beads which are linked using linear springs at the microscale. A series of MD simulations were performed to define the spring stiffness in the statistical network model. By integrating multiple spring-bead models a network model has been developed to represent the material properties at the mesoscale. The model captures the statistical distribution of crosslinking degree of the polymer to represent the heterogeneous material properties at the microscale. The developed multiscale methodology is computationally efficient and provides a possible means to bridge multiple length scales (from 10 nm in MD simulation to 10 mm in FE model) without significant loss of accuracy. Parametric studies have been conducted to investigate the influence of the crosslinking degree on the material behavior. The developed methodology has been used to evaluate damage evolution in the self-sensing polymer.
For metallic material (Al 2024-T351), the methodology initiates at the microscale where extensive material characterization is conducted to capture the microstructural variability. A statistical volume element (SVE) model is constructed to represent the material properties. Geometric and crystallographic features including grain orientation, misorientation, size, shape, principal axis direction and aspect ratio are captured. This SVE model provides a computationally efficient alternative to traditional techniques using representative volume element (RVE) models while maintaining statistical accuracy. A physics based multiscale damage criterion is developed to simulate the fatigue crack initiation. The crack growth rate and probable directions are estimated simultaneously.
Mechanically sensitive materials that exhibit specific chemical reactions upon external loading are currently being investigated for self-sensing applications. The "smart" polymer modeled in this research consists of epoxy resin, hardener, and a stress-sensitive material called mechanophore The mechanophore activation is based on covalent bond-breaking induced by external stimuli; this feature can be used for material-level damage detections. In this work Tris-(Cinnamoyl oxymethyl)-Ethane (TCE) is used as the cyclobutane-based mechanophore (stress-sensitive) material in the polymer matrix. The TCE embedded polymers have shown promising results in early damage detection through mechanically induced fluorescence. A spring-bead based network model, which bridges nanoscale information to higher length scales, has been developed to model this material system. The material is partitioned into discrete mass beads which are linked using linear springs at the microscale. A series of MD simulations were performed to define the spring stiffness in the statistical network model. By integrating multiple spring-bead models a network model has been developed to represent the material properties at the mesoscale. The model captures the statistical distribution of crosslinking degree of the polymer to represent the heterogeneous material properties at the microscale. The developed multiscale methodology is computationally efficient and provides a possible means to bridge multiple length scales (from 10 nm in MD simulation to 10 mm in FE model) without significant loss of accuracy. Parametric studies have been conducted to investigate the influence of the crosslinking degree on the material behavior. The developed methodology has been used to evaluate damage evolution in the self-sensing polymer.
ContributorsZhang, Jinjun (Author) / Chattopadhyay, Aditi (Thesis advisor) / Dai, Lenore (Committee member) / Jiang, Hanqing (Committee member) / Papandreou-Suppappola, Antonia (Committee member) / Rajadas, John (Committee member) / Arizona State University (Publisher)
Created2014
Description
Multi-touch tablets and smart phones are now widely used in both workplace and consumer settings. Interacting with these devices requires hand and arm movements that are potentially complex and poorly understood. Experimental studies have revealed differences in performance that could potentially be associated with injury risk. However, underlying causes for performance differences are often difficult to identify. For example, many patterns of muscle activity can potentially result in similar behavioral output. Muscle activity is one factor contributing to forces in tissues that could contribute to injury. However, experimental measurements of muscle activity and force for humans are extremely challenging. Models of the musculoskeletal system can be used to make specific estimates of neuromuscular coordination and musculoskeletal forces. However, existing models cannot easily be used to describe complex, multi-finger gestures such as those used for multi-touch human computer interaction (HCI) tasks. We therefore seek to develop a dynamic musculoskeletal simulation capable of estimating internal musculoskeletal loading during multi-touch tasks involving multi digits of the hand, and use the simulation to better understand complex multi-touch and gestural movements, and potentially guide the design of technologies the reduce injury risk. To accomplish these, we focused on three specific tasks. First, we aimed at determining the optimal index finger muscle attachment points within the context of the established, validated OpenSim arm model using measured moment arm data taken from the literature. Second, we aimed at deriving moment arm values from experimentally-measured muscle attachments and using these values to determine muscle-tendon paths for both extrinsic and intrinsic muscles of middle, ring and little fingers. Finally, we aimed at exploring differences in hand muscle activation patterns during zooming and rotating tasks on the tablet computer in twelve subjects. Towards this end, our musculoskeletal hand model will help better address the neuromuscular coordination, safe gesture performance and internal loadings for multi-touch applications.
ContributorsYi, Chong-hwan (Author) / Jindrich, Devin L. (Thesis advisor) / Artemiadis, Panagiotis K. (Thesis advisor) / Phelan, Patrick (Committee member) / Santos, Veronica J. (Committee member) / Huang, Huei-Ping (Committee member) / Arizona State University (Publisher)
Created2014
Description
In this research work, a novel control system strategy for the robust control of an unmanned ground vehicle is proposed. This strategy is motivated by efforts to mitigate the problem for scenarios in which the human operator is unable to properly communicate with the vehicle. This novel control system strategy consisted of three major components: I.) Two independent intelligent controllers, II.) An intelligent navigation system, and III.) An intelligent controller tuning unit. The inner workings of the first two components are based off the Brain Emotional Learning (BEL), which is a mathematical model of the Amygdala-Orbitofrontal, a region in mammalians brain known to be responsible for emotional learning. Simulation results demonstrated the implementation of the BEL model to be very robust, efficient, and adaptable to dynamical changes in its application as controller and as a sensor fusion filter for an unmanned ground vehicle. These results were obtained with significantly less computational cost when compared to traditional methods for control and sensor fusion. For the intelligent controller tuning unit, the implementation of a human emotion recognition system was investigated. This system was utilized for the classification of driving behavior. Results from experiments showed that the affective states of the driver are accurately captured. However, the driver's affective state is not a good indicator of the driver's driving behavior. As a result, an alternative method for classifying driving behavior from the driver's brain activity was explored. This method proved to be successful at classifying the driver's behavior. It obtained results comparable to the common approach through vehicle parameters. This alternative approach has the advantage of directly classifying driving behavior from the driver, which is of particular use in UGV domain because the operator's information is readily available. The classified driving mode was used tune the controllers' performance to a desired mode of operation. Such qualities are required for a contingency control system that would allow the vehicle to operate with no operator inputs.
ContributorsVargas-Clara, Alvaro (Author) / Redkar, Sangram (Thesis advisor) / McKenna, Anna (Committee member) / Cooke, Nancy J. (Committee member) / Arizona State University (Publisher)
Created2015
Description
In enzyme induced carbonate precipitation (EICP), calcium carbonate (CaCO3) precipitation is catalyzed by plant-derived urease enzyme. In EICP, urea hydrolyzes into ammonia and inorganic carbon, altering geochemical conditions in a manner that promotes carbonate mineral precipitation. The calcium source in this process comes from calcium chloride (CaCl2) in aqueous solution. Research work conducted for this dissertation has demonstrated that EICP can be employed for a variety of geotechnical purposes, including mass soil stabilization, columnar soil stabilization, and stabilization of erodible surficial soils. The research presented herein also shows that the optimal ratio of urea to CaCl2 at ionic strengths of less than 1 molar is approximately 1.75:1. EICP solutions of very high initial ionic strength (i.e. 6 M) as well as high urea concentrations (> 2 M) resulted in enzyme precipitation (salting-out) which hindered carbonate precipitation. In addition, the production of NH4+ may also result in enzyme precipitation. However, enzyme precipitation appeared to be reversible to some extent. Mass soil stabilization was demonstrated via percolation and mix-and-compact methods using coarse silica sand (Ottawa 20-30) and medium-fine silica sand (F-60) to produce cemented soil specimens whose strength improvement correlated with CaCO3 content, independent of the method employed to prepare the specimen. Columnar stabilization, i.e. creating columns of soil cemented by carbonate precipitation, using Ottawa 20-30, F-60, and native AZ soil was demonstrated at several scales beginning with small columns (102-mm diameter) and culminating in a 1-m3 soil-filled box. Wind tunnel tests demonstrated that surficial soil stabilization equivalent to that provided by thoroughly wetting the soil can be achieved through a topically-applied solution of CaCl2, urea, and the urease enzyme. The topically applied solution was shown to form an erosion-resistant CaCO3 crust on fine sand and silty soils. Cementation of erodible surficial soils was also achieved via EICP by including a biodegradable hydrogel in the stabilization solution. A dilute hydrogel solution extended the time frame over which the precipitation reaction could occur and provided improved spatial control of the EICP solution.
ContributorsHamdan, Nasser M (Author) / Kavazanjian Jr., Edward (Thesis advisor) / Rittmann, Bruce (Thesis advisor) / Shock, Everett (Committee member) / Arizona State University (Publisher)
Created2015
Description
Traffic congestion is a major externality in modern transportation systems with negative economic, environmental and social impacts. Freeway bottlenecks are one of the key elements besides the demand for travel by automobiles that determine the extent of congestion. The primary objective of this research is to provide a better understanding of factors for variations in bottleneck discharge rates. Specifically this research seeks to (i) develop a methodology comparable to the rigorous methods to identify bottlenecks and measure capacity drop and its temporal (day to day) variations in a region, (ii) understand the variations in discharge rate of a freeway weaving bottleneck with a HOV lane and (iii) understand the relationship between lane flow distribution and discharge rate on a weaving bottleneck resulted from a lane drop and a busy off-ramp. In this research, a methodology has been developed to de-noise raw data using Discrete Wavelet Transforms (DWT). The de-noised data is then used to precisely identify bottleneck activation and deactivation times, and measure pre-congestion and congestion flows using Continuous Wavelet Transforms (CWT). To this end a methodology which could be used efficiently to identify and analyze freeway bottlenecks in a region in a consistent, reproducible manner was developed. Using this methodology, 23 bottlenecks have been identified in the Phoenix metropolitan region, some of which result in long queues and large delays during rush-hour periods. A study of variations in discharge rate of a freeway weaving bottleneck with a HOV lane showed that the bottleneck discharge rate diminished by 3-25% upon queue formations, however, the discharge rate recovered shortly thereafter upon high-occupancy-vehicle (HOV) lane activation and HOV lane flow distribution (LFD) has a significant effect on the bottleneck discharge rate: the higher the HOV LFD, the lower the bottleneck discharge rate. The effect of lane flow distribution and its relationship with bottleneck discharge rate on a weaving bottleneck formed by a lane drop and a busy off-ramp was studied. The results showed that the bottleneck discharge rate and lane flow distribution are linearly related and higher utilization of the median lane results in higher bottleneck discharge rate.
ContributorsKandala, Srinivasa Srivatsav (Author) / Ahn, Soyoung (Thesis advisor) / Pendyala, Ram (Committee member) / Kaloush, Kamil (Committee member) / Arizona State University (Publisher)
Created2014