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.

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.

Construction project teams expend substantial effort to develop scope definition during the front end planning phase of building projects but oftentimes neglect to sufficiently plan for the complexities of tribal building projects. A needs assessment conducted by the author comprising interviews with practitioners familiar with construction on tribal lands revealed the need for a front end planning (FEP) process to assess scope definition of capital projects on tribal lands. This dissertation summarizes the motivations and efforts to develop a front end planning tool for tribal building projects, the Project Definition Rating Index (PDRI) for Tribal Building Projects. The author convened a research team to review, analyze, and adapt an existing building-projects-focused FEP tool, the PDRI – Building Projects, and other resources to develop a set of 67 specific elements relevant to the planning of tribal building projects. The author supported the facilitation of seven workshops in which 20 industry professionals evaluated the element descriptions and provided element prioritization data that was statistically analyzed to develop a preliminary weighted score sheet that corresponds to the element descriptions. Given that the author was only able to collect complete data from 11 projects, definitively determining element weights was not possible. Therefore, the author leveraged a Delphi study to test the PDRI – Tribal Building Projects. Delphi study results indicate the PDRI – Tribal Building Projects element descriptions fully address the scope of tribal building projects, and 75 percent of panelists agreed they would use this tool on their next tribal project. The author also explored the PDRI – Tribal Building Projects tool through the lens of the Diné (Navajo) Philosophy of Sa’ąh Naagháí Bik’eh Hózhóón (SNBH) and the guiding principles of Nistáhákees (thinking), Nahat’á (planning), Iiná (living), and Sihasin (assurance/reflection). The results of the author’s research provides several contributions to the American Indian Studies, front end planning, and tribal building projects bodies of knowledge: 1) defining unique features of tribal projects, 2) explicitly documenting the synergies between Western and Diné ways of planning, and 3) creating a tool to assist in planning capital projects on tribal lands in the American Southwest in support of improved cost performance.

District heating plays an important role in improving energy efficiency and providing thermal heat to buildings. Instead of using water as an energy carrier to transport sensible heat, this dissertation explores the use of liquid-phase thermochemical reactions for district heating as well as thermal storage. Chapters 2 and 3 present thermodynamic and design analyses for the proposed district heating system. Chapter 4 models the use of liquid-phase thermochemical reactions for on-site solar thermal storage. In brief, the proposed district heating system uses liquid-phase thermochemical reactions to transport thermal energy from a heat source to a heat sink. The separation ensures that the stored thermochemical heat can be stored indefinitely and/or transported long distances. The reactant molecules are then pumped over long distances to the heat sink, where they are combined in an exothermic reaction to provide heat. The product of the exothermic reaction is then pumped back to the heat source for re-use. The key evaluation parameter is the system efficiency. The results demonstrate that with heat recovery, the system efficiency can be up to 77% when the sink temperature equals 25 C. The results also indicate that the appropriate chemical reaction candidates should have large reaction enthalpy and small reaction entropy. Further, the design analyses of two district heating systems, Direct District Heating (DDH) system and Indirect District Heating (IDH) system using the solvated case shows that the critical distance is 106m. When the distance is shorter than 1000,000m, the factors related to the chemical reaction at the user side and factors related to the separation process are important for the DDH system. When the distance is longer than 106m, the factors related to the fluid mechanic become more important. Because the substation of the IDH system degrades the quality of the energy, when the distance is shorter than 106m, the efficiency of the substation is significant. Lastly, I create models for on-site solar thermal storage systems using liquid-phase thermochemical reactions and hot water. The analysis shows that the thermochemical reaction is more competitive for long-duration storage applications. However, the heat recovery added to the thermochemical thermal storage system cannot help improving solar radiation absorption with high inlet temperature of the solar panel.

This is an applied research paper, where a micro campus was designed for the San Carlos Apache Community with a goal of meeting requirements for at least three petals of Living Building Challenge. The end goal was to submit recommendations for attaining the petal certifications. The process of design not only included following spatial requirements of designing the building, but also including a wider perspective of construction and energy management in it. The first step of the research was getting to know the community and their requirements and priorities. This was done in 1st semester as a part of an applied class Indigenous Project Delivery. The second part of the research was to design a micro campus for the community that is in sync with the main campus. The intent of design is to respect the community’s culture and help them pass it on to the next generation while abiding by the Living Building Challenge standards. The third step of this research was to back up the design with recommendations for petal certifications.

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.

Arizona has been rapidly expanding in both population and construction over the last 20 years, and with the hot summer climate, many homeowners experience a significant increase in their utility bills. The cost to reduce these energy bills with home renovations can become expensive. This has become increasingly apparent over the last few years with the impact that covid had on the global supply chain. Prices of materials and labor have never been higher, and with this, the price of energy continues to increase. Therefore, it is important to explore methods to make homes more energy-efficient without the price tag. In addition to benefitting the homeowner by decreasing the cost of their monthly utility bills, making homes more energy efficient will aid in the overall goal of reducing carbon emissions.

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.

The International Energy Agency (IEA) anticipates the global energy demand to grow by more than 25% by 2040, requiring more than $2 trillion a year of investment in new energy supply (IEA, 2018). With power needs increasing as populations grow and climate extremes become more routine, power companies seek to continually increase capacity, improve efficiency, and provide resilience to the power grid, such that they can meet the energy needs of the societies they serve, often while trying to minimize their carbon emissions. Despite significant research dedicated to planning for industrial projects, including power generation projects as well as the pipeline projects that enable power generation and distribute power, there are still endemic cost overruns and schedule delays in large scale power generation projects. This research explores root causes of these seemingly systemic project performance issues that plague power generation projects. Specifically, this work analyzes approximately 770 power and pipeline projects and identifies how project performance indicators (i.e., cost and schedule performance) as well as planning indicators, compare in two regulatory environments, namely nonregulated and regulated markets. This contributes explicit understanding of the relationship between project performance and regulatory environment, both quantitatively and qualitatively, to the pipeline and power project planning and construction bodies of knowledge. Following an understanding of nonregulated versus regulated markets, this research takes a deeper dive into one highly-regulated power sector, the nuclear power sector, and explores root causes for cost overruns and schedule delays. This work leverages gray literature (i.e., newspaper articles) as sources, in order to analyze projects individually (most academic literature presents data about an aggregated set of projects) and understand the public perception of risks associated with such projects. This work contributes an understanding of the risks associated with nuclear power plant construction to the nuclear power plant construction body of knowledge. Ultimately, the findings from this research support improved planning for power and pipeline projects, in turn leading to more predictable projects, in terms of cost and schedule performance, regardless of regulatory environment. This enables power providers to meet the capacity demands of a growing population within budget and schedule.