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Description
The applications of Building Information Modelling (BIM) technology extend beyond performing clash detection and avoiding installation issues among subcontractors. When properly budgeted and fully integrated into the pre-construction work-flow, BIM technology can improve the accuracy of estimates and reduce material as well as labor costs. The purpose of this paper is to analyze BIM-related budgeting practices and explore options for optimizing BIM budgeting strategy as well as integrating BIM technology into an estimating strategy. The methodology chosen was a case study. A study of an electrical contractor was conducted using BIM budgeting data based on actual and estimated figures for 245 jobs completed in the years 2015-2019. A review of literature was conducted for the purpose of researching current options with regard to the implementation of BIM as part of estimation, its associated financial cost, and the challenges faced in adapting existing frameworks to meet new demands. It was observed that the current resources allocated for BIM are under-utilized on an aggregate basis. It was also observed that the budget for these resources is sometimes exceeded for larger projects and frequently, grossly under-utilized for smaller projects. There is a strong correlation between contract value and project type, suggesting that contract value is a primary predictor of BIM requirements. The review of literature suggests what employee skills are most necessary for integrating BIM with estimating: the ability to perform accurate takeoffs from BIM models, evaluate the cost of materials that are typically not modeled or appear poorly in the model, the ability to work with a variety of BIM software, the ability to know if a model is accurate, and an understanding of how the model interacts with other aspects of the construction process. It also discusses the challenges faced when adopting BIM in estimation. This paper contributes to the field of construction management by expanding the body of research for the BIM budgeting strategy in electrical contracting; an area of research which is not well developed. The data analyzed from this single electrical contractor provides the basis for an exploratory case study that contributes to the development of a conceptual framework for accurate BIM budgeting, where no such framework had previously existed.
ContributorsBalmer, Steven Thomas (Author) / Sullivan, Kenneth (Thesis advisor) / Smithwick, Jake (Committee member) / Stone, Brian (Committee member) / Arizona State University (Publisher)
Created2020
Description
Microlending aims at providing low-barrier loans to small to medium scaled family run businesses that are financially disincluded historically. These borrowers might be in third world countries where traditional financing is not accessible. Lenders can be individual investors or institutions making risky investments or willing to help people who cannot access traditional banks or do not have the credibility to get loans from traditional sources. Microlending involves a charitable cause as well where lenders are not really concerned about what and how they are paid.
This thesis aims at building a platform that will support both commercial microlending as well as charitable donation to support the real cause of microlending. The platform is expected to ensure privacy and transparency to the users in order to attract more users to use the system. Microlending involves monetary transactions, hence possible security threats to the system are discussed.
Blockchain is one of the technologies which has revolutionized financial transactions and microlending involves monetary transactions. Therefore, blockchain is viable option for microlending platform. Permissioned blockchain restricts the user admission to the platform and provides with identity management feature. This feature is required to ensure the security and privacy of various types of participants on the microlending platform.
This thesis aims at building a platform that will support both commercial microlending as well as charitable donation to support the real cause of microlending. The platform is expected to ensure privacy and transparency to the users in order to attract more users to use the system. Microlending involves monetary transactions, hence possible security threats to the system are discussed.
Blockchain is one of the technologies which has revolutionized financial transactions and microlending involves monetary transactions. Therefore, blockchain is viable option for microlending platform. Permissioned blockchain restricts the user admission to the platform and provides with identity management feature. This feature is required to ensure the security and privacy of various types of participants on the microlending platform.
ContributorsSiddharth, Sourabh (Author) / Boscovic, Dragan (Thesis advisor) / Basnal, Srividya (Thesis advisor) / Sanchez, Javier Gonzalez (Committee member) / Arizona State University (Publisher)
Created2020
Description
Robot motion planning requires computing a sequence of waypoints from an initial configuration of the robot to the goal configuration. Solving a motion planning problem optimally is proven to be NP-Complete. Sampling-based motion planners efficiently compute an approximation of the optimal solution. They sample the configuration space uniformly and hence fail to sample regions of the environment that have narrow passages or pinch points. These critical regions are analogous to landmarks from planning literature as the robot is required to pass through them to reach the goal.
This work proposes a deep learning approach that identifies critical regions in the environment and learns a sampling distribution to effectively sample them in high dimensional configuration spaces.
A classification-based approach is used to learn the distributions. The robot degrees of freedom (DOF) limits are binned and a distribution is generated from sampling motion plan solutions. Conditional information like goal configuration and robot location encoded in the network inputs showcase the network learning to bias the identified critical regions towards the goal configuration. Empirical evaluations are performed against the state of the art sampling-based motion planners on a variety of tasks requiring the robot to pass through critical regions. An empirical analysis of robotic systems with three to eight degrees of freedom indicates that this approach effectively improves planning performance.
This work proposes a deep learning approach that identifies critical regions in the environment and learns a sampling distribution to effectively sample them in high dimensional configuration spaces.
A classification-based approach is used to learn the distributions. The robot degrees of freedom (DOF) limits are binned and a distribution is generated from sampling motion plan solutions. Conditional information like goal configuration and robot location encoded in the network inputs showcase the network learning to bias the identified critical regions towards the goal configuration. Empirical evaluations are performed against the state of the art sampling-based motion planners on a variety of tasks requiring the robot to pass through critical regions. An empirical analysis of robotic systems with three to eight degrees of freedom indicates that this approach effectively improves planning performance.
ContributorsSrinet, Abhyudaya (Author) / Srivastava, Siddharth (Thesis advisor) / Zhang, Yu (Committee member) / Yang, Yezhou (Committee member) / Arizona State University (Publisher)
Created2020
Description
Despite the prevalence of teams in complex sociotechnical systems, current approaches to understanding workload tend to focus on the individual operator. However, research suggests that team workload has emergent properties and is not necessarily equivalent to the aggregate of individual workload. Assessment of communications provides a means of examining aspects of team workload in highly interdependent teams. This thesis set out to explore how communications are associated with team workload and performance under high task demand in all-human and human–autonomy teams in a command and control task. A social network analysis approach was used to analyze the communications of 30 different teams, each with three members operating in a command and control task environment of over a series of five missions. Teams were assigned to conditions differentiated by their composition with either a naïve participant, a trained confederate, or a synthetic agent in the pilot role. Social network analysis measures of centralization and intensity were used to assess differences in communications between team types and under different levels of demand, and relationships between communication measures, performance, and workload distributions were also examined. Results indicated that indegree centralization was greater in the all-human control teams than in the other team types, but degree centrality standard deviation and intensity were greatest in teams with a highly trained experimenter pilot. In all three team types, the intensity of communications and degree centrality standard deviation appeared to decrease during the high demand mission, but indegree and outdegree centralization did not. Higher communication intensity was associated with more efficient target processing and more successful target photos per mission, but a clear relationship between measures of performance and decentralization of communications was not found.
ContributorsJohnson, Craig Jonathon (Author) / Cooke, Nancy J. (Thesis advisor) / Gray, Robert (Committee member) / Gutzwiller, Robert S (Committee member) / Arizona State University (Publisher)
Created2020
Description
This dissertation presents a novel algorithm for recovering missing values of co-evolving time series with partial embedded network information. The idea is to connect two sources of data through a shared low dimensional latent space. The proposed algorithm, named NetDyna, is an Expectation-Maximization algorithm, and uses the Kalman filter and matrix factorization approaches to infer the missing values both in the time series and embedded network. The experimental results on real datasets, including a Motes dataset and a Motion Capture dataset, show that (1) NetDyna outperforms other state-of-the-art algorithms, especially with partially observed network information; (2) its computational complexity scales linearly with the time duration of time series; and (3) the algorithm recovers the embedded network in addition to missing time series values.
This dissertation also studies a load balancing algorithm, the so called power-of-two-choices(Po2), for many-server systems (with N servers) and focuses on the convergence of stationary distribution of Po2 in the both light and heavy traffic regimes to the solution of mean-field system. The framework of Stein’s method and state space collapse (SSC) are used to analyze both regimes.
In both regimes, the thesis first uses the argument of state space collapse to show that the probability of the state being far from the mean-field solution is small enough. By a simple Markov inequality, it is able to show that the probability is indeed very small with a proper choice of parameters.
Then, for the state space close to the solution of mean-field model, the thesis uses Stein’s method to show that the stochastic system is close to a linear mean-field model. By characterizing the generator difference, it is able to characterize the dominant terms in both regimes. Note that for heavy traffic case, the lower and upper bound analysis of a tridiagonal matrix, which arises from the linear mean-field model, is needed. From the dominant term, it allows to calculate the coefficient of the convergence rate.
In the end, comparisons between the theoretical predictions and numerical simulations are presented.
This dissertation also studies a load balancing algorithm, the so called power-of-two-choices(Po2), for many-server systems (with N servers) and focuses on the convergence of stationary distribution of Po2 in the both light and heavy traffic regimes to the solution of mean-field system. The framework of Stein’s method and state space collapse (SSC) are used to analyze both regimes.
In both regimes, the thesis first uses the argument of state space collapse to show that the probability of the state being far from the mean-field solution is small enough. By a simple Markov inequality, it is able to show that the probability is indeed very small with a proper choice of parameters.
Then, for the state space close to the solution of mean-field model, the thesis uses Stein’s method to show that the stochastic system is close to a linear mean-field model. By characterizing the generator difference, it is able to characterize the dominant terms in both regimes. Note that for heavy traffic case, the lower and upper bound analysis of a tridiagonal matrix, which arises from the linear mean-field model, is needed. From the dominant term, it allows to calculate the coefficient of the convergence rate.
In the end, comparisons between the theoretical predictions and numerical simulations are presented.
ContributorsHairi, FNU (Author) / Ying, Lei (Thesis advisor) / Wang, Weina (Committee member) / Zhang, Junshan (Committee member) / Zhang, Yanchao (Committee member) / Arizona State University (Publisher)
Created2020
Description
Solar energy as a limitless source of energy all around the globe has been difficult to harness. This is due to the low direct solar-electric conversion efficiency which has an upper limit set to the Shockley-Queisser limit. Solar thermophotovoltaics (STPV) is a much more efficient solar energy harvesting technology as it has the potential to overcome the Shockley-Queisser limit, by converting the broad-spectrum solar irradiation into narrowband infrared spectrum radiation matched to the PV cell. Despite the potential to surpass the Shockley-Queisser limit, very few experimental results have reported high system-level efficiency.
The objective of the thesis is to study the STPV conversion performance with selective metafilm absorber and emitter paired with a commercial GaSb cell at different solar concentrations. Absorber and Emitter metafilm thickness was optimized and fabricated. The optical properties of fabricated metafilms showed good agreement with the theoretically determined properties. The experimental setup was completed and validated by measuring the heat transfer rate across the test setup and comparing it with theoretical calculations. A novel method for maintaining the gap between the emitter and PV cell was developed using glass microspheres. Theoretical calculations show that the use of the glass of microspheres introduces negligible conduction loss across the gap compared to the radiation heat transfer, which is confirmed by experimental heat transfer measurement. This research work will help enhance the fundamental understanding and the development of the high-efficiency solar thermophotovoltaic system.
The objective of the thesis is to study the STPV conversion performance with selective metafilm absorber and emitter paired with a commercial GaSb cell at different solar concentrations. Absorber and Emitter metafilm thickness was optimized and fabricated. The optical properties of fabricated metafilms showed good agreement with the theoretically determined properties. The experimental setup was completed and validated by measuring the heat transfer rate across the test setup and comparing it with theoretical calculations. A novel method for maintaining the gap between the emitter and PV cell was developed using glass microspheres. Theoretical calculations show that the use of the glass of microspheres introduces negligible conduction loss across the gap compared to the radiation heat transfer, which is confirmed by experimental heat transfer measurement. This research work will help enhance the fundamental understanding and the development of the high-efficiency solar thermophotovoltaic system.
ContributorsNayal, Avinash (Author) / Wang, Liping (Thesis advisor) / Wang, Robert (Committee member) / Milcarek, Ryan (Committee member) / Arizona State University (Publisher)
Created2020
Description
Energy projects have the potential to provide critical services for human well-being and help eradicate poverty. However, too many projects fail because their approach oversimplifies the problem to energy poverty: viewing it as a narrow problem of access to energy services and technologies. This thesis presents an alternative paradigm for energy project development, grounded in theories of socio-energy systems, recognizing that energy and poverty coexist as a social, economic, and technological problem.
First, it shows that social, economic, and energy insecurity creates a complex energy-poverty nexus, undermining equitable, fair, and sustainable energy futures in marginalized communities. Indirect and access-based measures of energy poverty are a mismatch for the complexity of the energy-poverty nexus. The thesis, using the concept of social value of energy, develops a methodology for systematically mapping benefits, burdens and externalities of the energy system, illustrated using empirical investigations in communities in Nepal, India, Brazil, and Philippines. The thesis argues that key determinants of the energy-poverty nexus are the functional and economic capabilities of users, stressors and resulting thresholds of capabilities characterizing the energy and poverty relationship. It proposes ‘energy thriving’ as an alternative standard for evaluating project outcomes, requiring energy systems to not only remedy human well-being deficits but create enabling conditions for discovering higher forms of well-being.
Second, a novel, experimental approach to sustainability interventions is developed, to improve the outcomes of energy projects. The thesis presents results from a test bed for community sustainability interventions established in the village of Rio Claro in Brazil, to test innovative project design strategies and develop a primer for co-producing sustainable solutions. The Sustainable Rio Claro 2020 initiative served as a longitudinal experiment in participatory collective action for sustainable futures.
Finally, results are discussed from a collaborative project with grassroots practitioners to understand the energy-poverty nexus, map the social value of energy and develop energy thriving solutions. Partnering with local private and non-profit organizations in Uganda, Bolivia, Nepal and Philippines, the project evaluated and refined methods for designing and implementing innovative energy projects using the theoretical ideas developed in the thesis, subsequently developing a practitioner toolkit for the purpose.
First, it shows that social, economic, and energy insecurity creates a complex energy-poverty nexus, undermining equitable, fair, and sustainable energy futures in marginalized communities. Indirect and access-based measures of energy poverty are a mismatch for the complexity of the energy-poverty nexus. The thesis, using the concept of social value of energy, develops a methodology for systematically mapping benefits, burdens and externalities of the energy system, illustrated using empirical investigations in communities in Nepal, India, Brazil, and Philippines. The thesis argues that key determinants of the energy-poverty nexus are the functional and economic capabilities of users, stressors and resulting thresholds of capabilities characterizing the energy and poverty relationship. It proposes ‘energy thriving’ as an alternative standard for evaluating project outcomes, requiring energy systems to not only remedy human well-being deficits but create enabling conditions for discovering higher forms of well-being.
Second, a novel, experimental approach to sustainability interventions is developed, to improve the outcomes of energy projects. The thesis presents results from a test bed for community sustainability interventions established in the village of Rio Claro in Brazil, to test innovative project design strategies and develop a primer for co-producing sustainable solutions. The Sustainable Rio Claro 2020 initiative served as a longitudinal experiment in participatory collective action for sustainable futures.
Finally, results are discussed from a collaborative project with grassroots practitioners to understand the energy-poverty nexus, map the social value of energy and develop energy thriving solutions. Partnering with local private and non-profit organizations in Uganda, Bolivia, Nepal and Philippines, the project evaluated and refined methods for designing and implementing innovative energy projects using the theoretical ideas developed in the thesis, subsequently developing a practitioner toolkit for the purpose.
ContributorsBiswas, Saurabh (Author) / Miller, Clark A. (Thesis advisor) / Wiek, Arnim (Committee member) / Janssen, Marcus A (Committee member) / Arizona State University (Publisher)
Created2020
Description
Short-notice disasters such as hurricanes involve uncertainties in many facets, from the time of its occurrence to its impacts’ magnitude. Failure to incorporate these uncertainties can affect the effectiveness of the emergency responses. In the case of a hurricane event, uncertainties and corresponding impacts during a storm event can quickly cascade. Over the past decades, various storm forecast models have been developed to predict the storm uncertainties; however, access to the usage of these models is limited. Hence, as the first part of this research, a data-driven simulation model is developed with aim to generate spatial-temporal storm predicted hazards for each possible hurricane track modeled. The simulation model identifies a means to represent uncertainty in storm’s movement and its associated potential hazards in the form of probabilistic scenarios tree where each branch is associated with scenario-level storm track and weather profile. Storm hazards, such as strong winds, torrential rain, and storm surges, can inflict significant damage on the road network and affect the population’s ability to move during the storm event. A cascading network failure algorithm is introduced in the second part of the research. The algorithm takes the scenario-level storm hazards to predict uncertainties in mobility states over the storm event. In the third part of the research, a methodology is proposed to generate a sequence of actions that simultaneously solve the evacuation flow scheduling and suggested routes which minimize the total flow time, or the makespan, for the evacuation process from origins to destinations in the resulting stochastic time-dependent network. The methodology is implemented for the 2017 Hurricane Irma case study to recommend an evacuation policy for Manatee County, FL. The results are compared with evacuation plans for assumed scenarios; the research suggests that evacuation recommendations that are based on single scenarios reduce the effectiveness of the evacuation procedure. The overall contributions of the research presented here are new methodologies to: (1) predict and visualize the spatial-temporal impacts of an oncoming storm event, (2) predict uncertainties in the impacts to transportation infrastructure and mobility, and (3) determine the quickest evacuation schedule and routes under the uncertainties within the resulting stochastic transportation networks.
ContributorsGita, Ketut (Author) / Mirchandani, Pitu (Thesis advisor) / Maciejewski, Ross (Committee member) / Sefair, Jorge (Committee member) / Zhou, Xuesong (Committee member) / Arizona State University (Publisher)
Created2020
Description
The manufacturing process for electronic systems involves many players, from chip/board design and fabrication to firmware design and installation.
In today's global supply chain, any of these steps are prone to interference from rogue players, creating a security risk.
Manufactured devices need to be verified to perform only their intended operations since it is not economically feasible to control the supply chain and use only trusted facilities.
It is becoming increasingly necessary to trust but verify the received devices both at production and in the field.
Unauthorized hardware or firmware modifications, known as Trojans,
can steal information, drain the battery, or damage battery-driven embedded systems and lightweight Internet of Things (IoT) devices.
Since Trojans may be triggered in the field at an unknown instance,
it is essential to detect their presence at run-time.
However, it isn't easy to run sophisticated detection algorithms on these devices
due to limited computational power and energy, and in some cases, lack of accessibility.
Since finding a trusted sample is infeasible in general, the proposed technique is based on self-referencing to remove any effect of environmental or device-to-device variations in the frequency domain.
In particular, the self-referencing is achieved by exploiting the band-limited nature of Trojan activity using signal detection theory.
When the device enters the test mode, a predefined test application is run on the device
repetitively for a known period. The periodicity ensures that the spectral electromagnetic power of the test application concentrates at known frequencies, leaving the remaining frequencies within the operating bandwidth at the noise level. Any deviations from the noise level for these unoccupied frequency locations indicate the presence of unknown (unauthorized) activity. Hence, the malicious activity can differentiate without using a golden reference or any knowledge of the Trojan activity attributes.
The proposed technique's effectiveness is demonstrated through experiments with collecting and processing side-channel signals, such as involuntarily electromagnetic emissions and power consumption, of a wearable electronics prototype and commercial system-on-chip under a variety of practical scenarios.
In today's global supply chain, any of these steps are prone to interference from rogue players, creating a security risk.
Manufactured devices need to be verified to perform only their intended operations since it is not economically feasible to control the supply chain and use only trusted facilities.
It is becoming increasingly necessary to trust but verify the received devices both at production and in the field.
Unauthorized hardware or firmware modifications, known as Trojans,
can steal information, drain the battery, or damage battery-driven embedded systems and lightweight Internet of Things (IoT) devices.
Since Trojans may be triggered in the field at an unknown instance,
it is essential to detect their presence at run-time.
However, it isn't easy to run sophisticated detection algorithms on these devices
due to limited computational power and energy, and in some cases, lack of accessibility.
Since finding a trusted sample is infeasible in general, the proposed technique is based on self-referencing to remove any effect of environmental or device-to-device variations in the frequency domain.
In particular, the self-referencing is achieved by exploiting the band-limited nature of Trojan activity using signal detection theory.
When the device enters the test mode, a predefined test application is run on the device
repetitively for a known period. The periodicity ensures that the spectral electromagnetic power of the test application concentrates at known frequencies, leaving the remaining frequencies within the operating bandwidth at the noise level. Any deviations from the noise level for these unoccupied frequency locations indicate the presence of unknown (unauthorized) activity. Hence, the malicious activity can differentiate without using a golden reference or any knowledge of the Trojan activity attributes.
The proposed technique's effectiveness is demonstrated through experiments with collecting and processing side-channel signals, such as involuntarily electromagnetic emissions and power consumption, of a wearable electronics prototype and commercial system-on-chip under a variety of practical scenarios.
ContributorsKarabacak, Fatih (Author) / Ozev, Sule (Thesis advisor) / Ogras, Umit Y. (Thesis advisor) / Christen, Jennifer Blain (Committee member) / Kitchen, Jennifer (Committee member) / Arizona State University (Publisher)
Created2020
Description
Oxidoreductases catalyze transformations important in both bioenergetics and microbial technologies. Nonetheless, questions remain about how to tune them to modulate properties such as preference for catalysis in the oxidative or reductive direction, the potential range of activity, or coupling of multiple reactions. Using protein film electrochemistry, the features that control these properties are defined by comparing the activities of five [FeFe]-hydrogenases and two thiosulfate reductases. Although [FeFe]-hydrogenases are largely described as hydrogen evolution catalysts, the catalytic bias of [FeFe]-hydrogenases, i.e. the ratio of maximal reductive to oxidative activities, spans more than six orders of magnitude. At one extreme, two [FeFe]-hdyrogenases, Clostridium pasteuriaunum HydAII and Clostridium symbiosum HydY, are far more active for hydrogen oxidation than hydrogen evolution. On the other extreme, Clostridium pasteurianum HydAI and Clostridium acetobutylicum HydA1 have a neutral bias, in which both proton reduction and hydrogen oxidation are efficient. By investigating a collection of site-directed mutants, it is shown that the catalytic bias of [FeFe]-hydrogenases is not trivially correlated with the identities of residues in the primary or secondary coordination sphere. On the other hand, the catalytic bias of Clostridium acetobutylicum HydAI can be modulated via mutation of an amino acid residue coordinating the terminal [FeS] cluster. Simulations suggest that this change in catalytic bias may be linked to the reduction potential of the cluster.
Two of the enzymes examined in this work, Clostridium pasteurianum HydAIII and Clostridium symbiosum HydY, display novel catalytic properties. HydY is exclusively a hydrogen oxidizing catalyst, and it couples this activity to peroxide reduction activity at a rubrerythrin center in the same enzyme. On the other hand, CpIII operates only in a narrow potential window, inactivating at oxidizing potentials. This suggests it plays a novel physiological role that has not yet been identified. Finally, the electrocatalytic properties of Pyrobaculum aerophilum thiosulfate reductase with either Mo or W in the active site are compared. In both cases, the onset of catalysis corresponds to reduction of the active site. Overall, the Mo enzyme is more active, and reduces thiosulfate with less overpotential.
Two of the enzymes examined in this work, Clostridium pasteurianum HydAIII and Clostridium symbiosum HydY, display novel catalytic properties. HydY is exclusively a hydrogen oxidizing catalyst, and it couples this activity to peroxide reduction activity at a rubrerythrin center in the same enzyme. On the other hand, CpIII operates only in a narrow potential window, inactivating at oxidizing potentials. This suggests it plays a novel physiological role that has not yet been identified. Finally, the electrocatalytic properties of Pyrobaculum aerophilum thiosulfate reductase with either Mo or W in the active site are compared. In both cases, the onset of catalysis corresponds to reduction of the active site. Overall, the Mo enzyme is more active, and reduces thiosulfate with less overpotential.
ContributorsWilliams, Samuel Garrett (Author) / Jones, Anne K (Thesis advisor) / Hayes, Mark A. (Committee member) / Trovitch, Ryan J (Committee member) / Arizona State University (Publisher)
Created2020