Matching Items (48)
Description
In the burgeoning field of sustainability, there is a pressing need for healthcare to understand the increased environmental and economic impact of healthcare products and services. The overall aim of this dissertation is to assess the sustainability of commonly used medical products, devices, and services as well as to identify strategies for making easy, low cost changes that result in environmental and economic savings for healthcare systems. Life cycle environmental assessments (LCAs) and life cycle costing assessments (LCCAs) will be used to quantitatively evaluate life-cycle scenarios for commonly utilized products, devices, and services. This dissertation will focus on several strategic and high impact areas that have potential for significant life-cycle environmental and economic improvements: 1) increased deployment of reprocessed medical devices in favor of disposable medical devices, 2) innovations to expand the use of biopolymers in healthcare materials and devices, and 3) assess the environmental and economic impacts of various medical devices and services in order to give healthcare administrators and employees the ability to make more informed decisions about the sustainability of their utilized materials, devices, and services.
ContributorsUnger, Scott (Author) / Landis, Amy E. (Thesis advisor) / Bilec, Melissa (Committee member) / Parrish, Kristen (Committee member) / Arizona State University (Publisher)
Created2015
Description
Commercial buildings in the United States account for 19% of the total energy consumption annually. Commercial Building Energy Consumption Survey (CBECS), which serves as the benchmark for all the commercial buildings provides critical input for EnergyStar models. Smart energy management technologies, sensors, innovative demand response programs, and updated versions of certification programs elevate the opportunity to mitigate energy-related problems (blackouts and overproduction) and guides energy managers to optimize the consumption characteristics. With increasing advancements in technologies relying on the ‘Big Data,' codes and certification programs such as the American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE), and the Leadership in Energy and Environmental Design (LEED) evaluates during the pre-construction phase. It is mostly carried out with the assumed quantitative and qualitative values calculated from energy models such as Energy Plus and E-quest. However, the energy consumption analysis through Knowledge Discovery in Databases (KDD) is not commonly used by energy managers to perform complete implementation, causing the need for better energy analytic framework.
The dissertation utilizes Interval Data (ID) and establishes three different frameworks to identify electricity losses, predict electricity consumption and detect anomalies using data mining, deep learning, and mathematical models. The process of energy analytics integrates with the computational science and contributes to several objectives which are to
1. Develop a framework to identify both technical and non-technical losses using clustering and semi-supervised learning techniques.
2. Develop an integrated framework to predict electricity consumption using wavelet based data transformation model and deep learning algorithms.
3. Develop a framework to detect anomalies using ensemble empirical mode decomposition and isolation forest algorithms.
With a thorough research background, the first phase details on performing data analytics on the demand-supply database to determine the potential energy loss reduction potentials. Data preprocessing and electricity prediction framework in the second phase integrates mathematical models and deep learning algorithms to accurately predict consumption. The third phase employs data decomposition model and data mining techniques to detect the anomalies of institutional buildings.
The dissertation utilizes Interval Data (ID) and establishes three different frameworks to identify electricity losses, predict electricity consumption and detect anomalies using data mining, deep learning, and mathematical models. The process of energy analytics integrates with the computational science and contributes to several objectives which are to
1. Develop a framework to identify both technical and non-technical losses using clustering and semi-supervised learning techniques.
2. Develop an integrated framework to predict electricity consumption using wavelet based data transformation model and deep learning algorithms.
3. Develop a framework to detect anomalies using ensemble empirical mode decomposition and isolation forest algorithms.
With a thorough research background, the first phase details on performing data analytics on the demand-supply database to determine the potential energy loss reduction potentials. Data preprocessing and electricity prediction framework in the second phase integrates mathematical models and deep learning algorithms to accurately predict consumption. The third phase employs data decomposition model and data mining techniques to detect the anomalies of institutional buildings.
ContributorsNaganathan, Hariharan (Author) / Chong, Oswald W (Thesis advisor) / Ariaratnam, Samuel T (Committee member) / Parrish, Kristen (Committee member) / Arizona State University (Publisher)
Created2017
Description
Project teams expend substantial effort to develop scope definition during the front end planning phase of large, complex projects, but oftentimes neglect to sufficiently plan for small projects. An industry survey administered by the author showed that small projects make up approximately half of all projects in the infrastructure construction sector (by count), the planning of these projects varies greatly, and that a consistent definition of “small infrastructure project” did not exist. This dissertation summarizes the motivations and efforts of Construction Industry Institute (CII) Research Team 314a to develop a non-proprietary front end planning tool specifically for small infrastructure projects, namely the Project Definition Rating Index (PDRI) for Small Infrastructure Projects. The author was a member of CII Research Team 314a, who was tasked with developing the tool in September 2015. The author, together with the research team, scrutinized and adapted an existing infrastructure-focused FEP tool, the PDRI for Infrastructure Projects, and other resources to develop a set of 40 specific elements relevant to the planning of small infrastructure projects. The author along with the research team supported the facilitation of seven separate industry workshops where 71 industry professionals evaluated the element descriptions and provided element prioritization data that was statistically analyzed and used to develop a corresponding weighted score sheet. The tool was tested on 76 completed and in-progress projects, the analysis of which showed that small infrastructure projects with greater scope definition (based on the tool’s scoring scheme) outperformed projects with lesser scope definition regarding cost performance, schedule performance, change performance, financial performance, and customer satisfaction. Moreover, the author found that users of the tool on in-progress projects agreed that the tool added value to their projects in a timeframe and manner consistent with their needs, and that they would continue using the tool in the future. The author also conducted qualitative and quantitative similarities and differences between PDRI – Infrastructure and PDRI – Small Infrastructure Projects in support of improved planning efforts for both types of projects. Finally, the author piloted a case study that introduced the PDRI into an introductory construction management course to enhance students’ learning experience.
ContributorsElZomor, Mohamed A (Author) / Parrish, Kristen (Thesis advisor) / Gibson, Jr., G. Edward (Committee member) / El Asmar, Mounir (Committee member) / Arizona State University (Publisher)
Created2017
Description
In this era of high-tech computer advancements and tremendous programmable computer capabilities, construction cost estimation still remains a knowledge-intensive and experience driven task. High reliance on human expertise, and less accuracy in the decision support tools render cost estimation error prone. Arriving at accurate cost estimates is of paramount importance because it forms the basis of most of the financial, design, and executive decisions concerning the project at subsequent stages. As its unique contribution to the body of knowledge, this paper analyzes the deviations and behavior of costs associated with different construction activities involved in commercial office tenant improvement (TI) projects. The aim of this study is to obtain useful micro-level cost information of various construction activities that make up for the total construction cost of projects. Standardization and classification of construction activities have been carried out based on Construction Specifications Institute’s (CSI) MasterFormat® division items. Construction costs from 51 office TI projects completed during 2015 and 2016 are analyzed statistically to understand the trends among various construction activities involved. It was found that the interior finishes activities showed a much higher cost of construction, and a comparatively higher variation than the mechanical, electrical, and plumbing (MEP) trades. The statistical analysis also revealed a huge scope of energy saving measures that could be achieved in such TI projects because of the absence of energy management systems (EMS) found in 66% of the projects.
ContributorsGhosh, Arunabho (Author) / Grau, David (Thesis advisor) / Ayer, Steven (Committee member) / Parrish, Kristen (Committee member) / Arizona State University (Publisher)
Created2016
Description
The paper was written for the International Group for Lean Construction Conference in July 2013 in Fortaleza, Brazil.
With the advent of sustainable building ordinances in the United States and internationally, contractors are required to deliver sustainable projects but have historically not been considered partners in developing the sustainability goals and objectives for projects. Additionally, as alternative project delivery methods gain popularity, contractors have an opportunity and—in an increasing number of cases—a requirement, to take a larger role in sustainability efforts beyond the design phase. Understanding the contractor’s self-perceived role in this industry is imperative to informing their future role in the sustainable construction industry. This paper presents data and analysis of a survey of general contractors in the Phoenix, Arizona market that asked for their opinions and viewpoints regarding sustainable construction. Respondents provided feedback about corporate profitability, growth forecast, and the perceived efficiency of the U.S Green Building Council’s LEED rating system. The survey also queried contractors about current and future work breakdown structures for sustainable project delivery as well as their underlying motives for involvement in these projects.
Academics from Arizona State University worked with local industry to develop the survey in 2012 and the survey was deployed in 2013. We sent the survey to 76 contractors and received responses from 21, representing a 27.6% response rate. Respondents include representatives from general contractors, mechanical contractors, and electrical contractors, among others. This paper presents the responses from general contractors as they typically have most contact with the owner and design teams.
With the advent of sustainable building ordinances in the United States and internationally, contractors are required to deliver sustainable projects but have historically not been considered partners in developing the sustainability goals and objectives for projects. Additionally, as alternative project delivery methods gain popularity, contractors have an opportunity and—in an increasing number of cases—a requirement, to take a larger role in sustainability efforts beyond the design phase. Understanding the contractor’s self-perceived role in this industry is imperative to informing their future role in the sustainable construction industry. This paper presents data and analysis of a survey of general contractors in the Phoenix, Arizona market that asked for their opinions and viewpoints regarding sustainable construction. Respondents provided feedback about corporate profitability, growth forecast, and the perceived efficiency of the U.S Green Building Council’s LEED rating system. The survey also queried contractors about current and future work breakdown structures for sustainable project delivery as well as their underlying motives for involvement in these projects.
Academics from Arizona State University worked with local industry to develop the survey in 2012 and the survey was deployed in 2013. We sent the survey to 76 contractors and received responses from 21, representing a 27.6% response rate. Respondents include representatives from general contractors, mechanical contractors, and electrical contractors, among others. This paper presents the responses from general contractors as they typically have most contact with the owner and design teams.
ContributorsHolloway, Skyler Brock (Author) / Parrish, Kristen (Thesis director) / Bashford, Howard (Committee member) / Meek, Jeremy (Committee member) / Barrett, The Honors College (Contributor) / School of Sustainability (Contributor) / W. P. Carey School of Business (Contributor) / Del E. Webb Construction (Contributor)
Created2013-05
Description
As the demand for natural resources increases with population growth, importance has been placed on environmental issues due to increasing pressure on land, water, air, and raw materials. In order to sustain the environment and natural resources, sustainable engineering and earth systems engineering and management (ESEM) is vital for future populations. The Aral Sea and the Florida Everglades are both regions that are heavily impacted by human design decisions. Comparing and analyzing the implications and outcomes of these human design decisions allows conclusions to be made regarding how earth systems engineering and management can be best accomplished. The Aral Sea, located in central Asia between Kazakhstan and Uzbekistan, is a case study of an ecosystem that has collapsed under the pressure of agricultural expansion. This has caused extensive economic, health, agricultural, and environmental impacts. The Everglades in southern Florida is a case study where the ecosystem has evolved from its original state, rather than collapsed, due to human settlement and water resource demand. In order to determine effective sustainable engineering approaches, the case studies will be evaluated using ESEM principles. These principles are used as guidance in executing better practice of sustainable engineering. When comparing the two case studies, it appears that the Everglades is an adequate representation of effective ESEM approaches, while the Aral Sea is not reflective of effective approaches. When practicing ESEM, it is critical that the principles be applied as a whole rather than individually. While the ESEM principles do not guarantee success, they offer an effective guide to dealing with the complexity and uncertainty in many of today's systems.
ContributorsRidley, Brooke Nicole (Author) / Allenby, Brad (Thesis director) / Parrish, Kristen (Committee member) / Civil, Environmental and Sustainable Engineering Programs (Contributor) / Barrett, The Honors College (Contributor)
Created2015-12
Description
An Earned Value Management System (EVMS) is an organization’s system for project/program management that integrates a defined set of associated work scopes, schedules and budgets, allowing for effective planning, performance, and management control. A mature EVMS that is compliant with standards and guidelines, and that is applied in a positive social environment is critical to the overall success of large and complex projects and programs. However, a comprehensive and up-to-date literature review revealed a lack of a data-driven and consistent rating system that can gauge the maturity and the environment surrounding EVMS implementation. Therefore, the primary objective of this dissertation focuses on the EVMS maturity and environment, and investigates their impact on project performance. The author was one of the 41 research team members whose goal was to develop the novel rating system called Integrated Project/Program Management (IP2M) Maturity and Environment Total Risk Rating (METRR). Using a multi-method research approach, the rating system was developed based on a literature review of more than 600 references, a survey with 294 responses, focus group meetings, and research charrettes with more than 100 subject matter experts from the industry. Performance data from 35 completed projects and programs representing over $21.8 billion in total cost was collected and analyzed. The data analysis showed that the projects with high EVMS maturity and good EVMS environment outperformed those with low maturity and poor environment in key project performance measures. The contributions of this work includes: (1) developing definitions for EVM, EVMS and other research related terms, (2) determining the gaps in the EVMS literature, (3) determining the EVMS state of the practice in the industry, (4) developing a scalable rating system to measure the EVMS maturity and environment, (5) providing quantified evidence on the impact of EVMS maturity and environment on project performance, and (6) providing guidance to practitioners to gauge their EVMS maturity and environment for an enhanced project and program management integration and performance.
ContributorsAramali, Vartenie Mardiros (Author) / Gibson Jr., George Edward (Thesis advisor) / El Asmar, Mounir (Committee member) / Parrish, Kristen (Committee member) / Arizona State University (Publisher)
Created2022
Description
There are relatively few available construction equipment detectors models thatuse deep learning architectures; many of these use old object detection architectures
like CNN (Convolutional Neural Networks), RCNN (Region-Based Convolutional
Neural Network), and early versions of You Only Look Once (YOLO) V1. It can be
challenging to deploy these models in practice for tracking construction equipment
while working on site.
This thesis aims to provide a clear guide on how to train and evaluate the
performance of different deep learning architecture models to detect different kinds of
construction equipment on-site using two You Only Look Once (YOLO) architecturesYOLO v5s and YOLO R to detect three classes of different construction equipment onsite, including Excavators, Dump Trucks, and Loaders. The thesis also provides a
simple solution to deploy the trained models. Additionally, this thesis describes a
specialized, high-quality dataset with three thousand pictures created to train these
models on real data by considering a typical worksite scene, various motions, varying
perspectives, and angles of construction equipment on the site.
The results presented herein show that after 150 epochs of training, the YOLORP6 has the best mAP at 0.981, while the YOLO v5s mAP is 0.936. However, YOLO v5s
had the fastest and the shortest training time on Tesla P100 GPU as a processing
unit on the Google Colab notebook. The YOLOv5s needed 4 hours and 52 minutes, but
the YOLOR-P6 needed 14 hours and 35 minutes to finish the training.ii
The final findings of this study show that the YOLOv5s model is the most efficient
model to use when building an artificial intelligence model to detect construction
equipment because of the size of its weights file relative to other versions of YOLO
models- 14.4 MB for YOLOV5s vs. 288 MB for YOLOR-P6.
This hugely impacts the processing unit’s performance, which is used to predict
the construction equipment on site. In addition, the constructed database is published
on a public dataset on the Roboflow platform, which can be used later as a foundation
for future research and improvement for the newer deep learning architectures.
Contributorssabek, mohamed mamdooh (Author) / Parrish, Kristen (Thesis advisor) / Czerniawski, Thomas (Committee member) / Ayer, Steven K (Committee member) / Arizona State University (Publisher)
Created2022
Description
At least 30 datacenters either broke ground or hit the planning stages around the United States over the past two years. On such technically complex projects, Mechanical, Electrical and Plumbing (MEP) systems make up a huge portion of the construction work which makes data center market very promising for MEP subcontractors in the next years. However, specialized subcontractors such as electrical subcontractors are struggling to keep crews motivated. Due to the hard work involved in the construction industry, it is not appealing for young workers. According to The Center for Construction Research and Training, the percentages of workers aged between 16 to 19 years decreased by 67%, 20 to 24 years decreased by 49% and 25 to 34 age decreased by 32% from 1985 to 2015. Furthermore, the construction industry has been lagging other industries in combatting its decline in productivity. Electrical activities, especially cable pulling, are some of the most physically unsafe, tedious, and labor-intensive electrical process on data center projects. The motivation of this research is the need to take a closer look at how this process is being done and find improvement opportunities. This thesis focuses on one potential restructuring of the cable pulling and termination process; the goal of this restructuring is optimization for automation. Through process mapping, this thesis presents a proposed cable pulling and termination process that utilizes automation to make use of the best abilities of human and robots/machines. It will also provide a methodology for process improvement that is applicable to the electrical scope of work as well as that of other construction trades.
ContributorsHammam, MennatAllah (Author) / Parrish, Kristen (Thesis advisor) / Ayer, Steven (Committee member) / Irish, Elizabeth (Committee member) / Arizona State University (Publisher)
Created2020
Description
Virtual Reality (VR) has been used in the sphere of training and education in the construction field. Research has investigated the different applications of VR in construction-focused simulations to report its benefits and drawbacks in training and education. Although this is significant, they were not albeit explicitly studied through the lens of accreditation at undergraduate educational levels. The American Council for Construction Education (ACCE) established twenty Students Learning Outcomes (SLOs) that equip students with essential knowledge and industry-oriented technical and managerial skills that maintain quality education in undergraduate construction programs. This paper analyzes the trends in VR literature through reported benefits and unexplored learning outcomes of VR in construction training and education and investigates the ways by which these trends do or do not contribute to the learning experience by targeting the content areas associated with the ACCE’s SLOs. To accomplish this, the author reviewed 59 articles from 2014 to 2023 found through a keyword search for “Virtual” AND “Reality” AND “Construction” AND (“Training” OR “Simulation” OR “Education”) AND “Students”. The learning outcomes of the VR training reported in the 59 articles were mapped to their corresponding content areas from ACCE’s SLO(s). The results demonstrate the content areas of SLOs that were addressed in literature (1, 2, 3, 5, 6, 7, 8, 9, 10, 11, 13, 15, 16, 18, 19, and 20) and the SLOs that were not explored (4, 12, 14, and 17) due to lack of studies in some contexts. This study reveals trends and patterns of VR training, some of which exemplify benefits of addressing content areas of SLOs through virtual on-site immersion, manipulation of time, cost efficiency, and ethical measures, while others indicate unexplored learning outcomes of VR training in targeting content areas of SLOs that involve human interaction, complex quantitative calculations or require construction management tools, delivery method and stakeholders’ management, and risk management. While this research does not seek replacement of traditional trainings, it encourages consideration of VR training under the lens of ACCE’s accreditation. This research’s findings propose guidance to educational researchers on how VR training could address content areas from ACCE’s SLOs.
ContributorsElgamal, Sara (Author) / Ayer, Steven (Thesis advisor, Committee member) / Parrish, Kristen (Thesis advisor, Committee member) / Lamanna, Anthony (Committee member) / Arizona State University (Publisher)
Created2023