Matching Items (8)
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- All Subjects: Civil Engineering
- All Subjects: Sustainability
- Genre: Masters Thesis
- Creators: Parrish, Kristen
Description
Given the importance of buildings as major consumers of resources worldwide, several organizations are working avidly to ensure the negative impacts of buildings are minimized. The U.S. Green Building Council's (USGBC) Leadership in Energy and Environmental Design (LEED) rating system is one such effort to recognize buildings that are designed to achieve a superior performance in several areas including energy consumption and indoor environmental quality (IEQ). The primary objectives of this study are to investigate the performance of LEED certified facilities in terms of energy consumption and occupant satisfaction with IEQ, and introduce a framework to assess the performance of LEED certified buildings.
This thesis attempts to achieve the research objectives by examining the LEED certified buildings on the Arizona State University (ASU) campus in Tempe, AZ, from two complementary perspectives: the Macro-level and the Micro-level. Heating, cooling, and electricity data were collected from the LEED-certified buildings on campus, and their energy use intensity was calculated in order to investigate the buildings' actual energy performance. Additionally, IEQ occupant satisfaction surveys were used to investigate users' satisfaction with the space layout, space furniture, thermal comfort, indoor air quality, lighting level, acoustic quality, water efficiency, cleanliness and maintenance of the facilities they occupy.
From a Macro-level perspective, the results suggest ASU LEED buildings consume less energy than regional counterparts, and exhibit higher occupant satisfaction than national counterparts. The occupant satisfaction results are in line with the literature on LEED buildings, whereas the energy results contribute to the inconclusive body of knowledge on energy performance improvements linked to LEED certification. From a Micro-level perspective, data analysis suggest an inconsistency between the LEED points earned for the Energy & Atmosphere and IEQ categories, on one hand, and the respective levels of energy consumption and occupant satisfaction on the other hand. Accordingly, this study showcases the variation in the performance results when approached from different perspectives. This contribution highlights the need to consider the Macro-level and Micro-level assessments in tandem, and assess LEED building performance from these two distinct but complementary perspectives in order to develop a more comprehensive understanding of the actual building performance.
This thesis attempts to achieve the research objectives by examining the LEED certified buildings on the Arizona State University (ASU) campus in Tempe, AZ, from two complementary perspectives: the Macro-level and the Micro-level. Heating, cooling, and electricity data were collected from the LEED-certified buildings on campus, and their energy use intensity was calculated in order to investigate the buildings' actual energy performance. Additionally, IEQ occupant satisfaction surveys were used to investigate users' satisfaction with the space layout, space furniture, thermal comfort, indoor air quality, lighting level, acoustic quality, water efficiency, cleanliness and maintenance of the facilities they occupy.
From a Macro-level perspective, the results suggest ASU LEED buildings consume less energy than regional counterparts, and exhibit higher occupant satisfaction than national counterparts. The occupant satisfaction results are in line with the literature on LEED buildings, whereas the energy results contribute to the inconclusive body of knowledge on energy performance improvements linked to LEED certification. From a Micro-level perspective, data analysis suggest an inconsistency between the LEED points earned for the Energy & Atmosphere and IEQ categories, on one hand, and the respective levels of energy consumption and occupant satisfaction on the other hand. Accordingly, this study showcases the variation in the performance results when approached from different perspectives. This contribution highlights the need to consider the Macro-level and Micro-level assessments in tandem, and assess LEED building performance from these two distinct but complementary perspectives in order to develop a more comprehensive understanding of the actual building performance.
ContributorsChokor, Abbas (Author) / El Asmar, Mounir (Thesis advisor) / Chong, Oswald (Committee member) / Parrish, Kristen (Committee member) / Arizona State University (Publisher)
Created2015
Description
In recent years, 40% of the total world energy consumption and greenhouse gas emissions is because of buildings. Out of that 60% of building energy consumption is due to HVAC systems. Under current trends these values will increase in coming years. So, it is important to identify passive cooling or heating technologies to meet this need. The concept of thermal energy storage (TES), as noted by many authors, is a promising way to rectify indoor temperature fluctuations. Due to its high energy density and the use of latent energy, Phase Change Materials (PCMs) are an efficient choice to use as TES. A question that has not satisfactorily been addressed, however, is the optimum location of PCM. In other words, given a constant PCM mass, where is the best location for it in a building? This thesis addresses this question by positioning PCM to obtain maximum energy savings and peak time delay. This study is divided into three parts. The first part is to understand the thermal behavior of building surfaces, using EnergyPlus software. For analysis, a commercial prototype building model for a small office in Phoenix, provided by the U.S. Department of Energy, is applied and the weather location file for Phoenix, Arizona is also used. The second part is to justify the best location, which is obtained from EnergyPlus, using a transient grey box building model. For that we have developed a Resistance-Capacitance (RC) thermal network and studied the thermal profile of a building in Phoenix. The final part is to find the best location for PCMs in buildings using EnergyPlus software. In this part, the mass of PCM used in each location remains unchanged. This part also includes the impact of the PCM mass on the optimized location and how the peak shift varies. From the analysis, it is observed that the ceiling is the best location to install PCM for yielding the maximum reduction in HVAC energy consumption for a hot, arid climate like Phoenix.
ContributorsPrem Anand Jayaprabha, Jyothis Anand (Author) / Phelan, Patrick (Thesis advisor) / Wang, Robert (Committee member) / Parrish, Kristen (Committee member) / Arizona State University (Publisher)
Created2018
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
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
Description
In recent years, many school districts, community colleges, and universities in California have implemented energy management-as-a-service (EMaaS). The purpose of this study was to analyzes how EMaaS has been realized in California schools, including how performance expectations and service guarantees have been met, how value is created and captured, and which trends are emerging in the pay-for-performance models. This study used a qualitative research design to identify patterns in the collected data and allow theories to be drawn from the emergent categories and themes. Ten in-depth interviews were conducted with a diverse pool of facility managers, energy practitioners, superintendents, and associate superintendents working with EMaaS. Four themes emerged (1) peak shaving overperformance, (2) low risk/reward, (3) performance exactly as expected, and (4) hope in future flexibility. This study reveals medium to high levels of performance satisfaction from the customers of cloud-enabled and battery-based EMaaS in California schools. Value has been captured primarily through peak shaving and intelligent bill management. Large campuses with higher peaks are especially good at delivering energy savings, and in some instances without pairing batteries and solar. Where demand response participation is permitted by the utility companies, the quality of demand response performance is mixed, with performance being exactly as expected to slightly less than expected. The EMaaS business model is positioned to help California schools implement and achieve many of their future sustainability goals in a cost-effective way.
ContributorsHawkins, Spencer (Author) / Sullivan, Kenneth (Thesis advisor) / Parrish, Kristen (Thesis advisor) / Standage, Richard (Committee member) / Arizona State University (Publisher)
Created2020
Description
Due to extreme summer temperatures that regularly reach 122°F (50°C), cooling energy requirements have been responsible for 70% of peak demand and 45% of total electricity consumption in Kuwait. It is estimated that 50%-60% of electric power is consumed by the residential sector, mostly in detached villas. This study analyzes the potential impact of energy efficiency measures (EEM) and renewable energy (RE) measures on the electric energy requirements of an existing villa built in 2004. Using architectural plans, interview data, and the eQUEST building energy simulation tool, a building energy model (BEM) was developed for a villa calibrated with hourly energy use data for the year 2014. Although the modeled villa consumed less energy than an average Kuwaiti villa of the same size, 26% energy reductions were still possible under compliance with 2018 building codes. Compliance with 2010 and 2014 building codes, however, would have increased energy use by 19% and 3% respectively. Furthermore, survey data of 150 villas was used to generate statistics on rooftop solar area availability. Accordingly, it was found that 78% of the survey sample’s average total rooftop area was not suitable for rooftop solar systems due to shading and other obstacles. The integration of a solar canopy circumvents this issue and also functions as a shading device for outdoor activities and as a protective cover for AC units and water tanks. Combining the highest modeled EEMs and RE measures on the villa, the energy use intensity (EUI) would be reduced to 15 kWh/m2/year from a baseline value of 127 kWh/m2/year, close to net zero. Finally, it was determined that EEMs were able to reduce the entire demand profile whereas RE measures were most effective at reducing demand around mid-day hours. In future studies, more effort should be spent on collecting hourly data from multiple villas to assist in the development of a detailed hourly bottom-up residential energy modeling methodology.
ContributorsAlyakoob, Ali (Author) / Reddy, Agami T (Thesis advisor) / Addison, Marlin (Committee member) / Parrish, Kristen (Committee member) / Arizona State University (Publisher)
Created2020