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Large-scale civil infrastructure systems are critical for the functioning and development of any society. However, these systems are often vulnerable to degradation and the effects of aging, necessitating consistent monitoring and maintenance. Current methods for infrastructure maintenance primarily rely on human intervention and need the implementation of advanced sensing and

Large-scale civil infrastructure systems are critical for the functioning and development of any society. However, these systems are often vulnerable to degradation and the effects of aging, necessitating consistent monitoring and maintenance. Current methods for infrastructure maintenance primarily rely on human intervention and need the implementation of advanced sensing and computing technologies in field operations and maintenance (O&M) tasks. This research aimed to address these gaps and provide novel contributions. Specifically, the objectives of this study were to leverage artificial intelligence models to enhance point cloud noise processing, to automate tree species detection using Mask R-CNN, and to integrate imagery data and LiDAR datasets for real-time terrain analysis. First, the study proposed leverages neural networks to eliminate unwanted noise from point cloud datasets, enhancing the accuracy and reliability of infrastructure data. Secondly, the research integrated Mask R-CNN into automated tree species detection. This component offers an efficient solution to identify and classify vegetation surrounding infrastructure, enabling infrastructure managers to devise proactive vegetation management strategies, thereby reducing risks associated with tree-related incidents. Lastly, the study fused image and LiDAR datasets to support real-time terrain analysis. This integrated approach provides a comprehensive understanding of terrain characteristics, allowing infrastructure managers to assess slope, elevation, and other relevant factors, facilitating proactive maintenance interventions and mitigating risks associated with erosion. These contributions collectively underscore the potential of artificial intelligence models in advancing the operations and maintenance practices of large civil infrastructure systems. By leveraging these models, infrastructure managers can optimize decision-making processes, streamline maintenance efforts, and enhance critical infrastructure networks' overall resilience and sustainability.
ContributorsPaladugu, Bala Sai Krishna (Author) / Grau, David (Thesis advisor) / Ernzen, James (Committee member) / Standage, Richard (Committee member) / Arizona State University (Publisher)
Created2023
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ABSTRACT Upon joining Arizona State University in July 2017, the author, a registered architect, inherited the oversight of the University Project Design Guidelines. During the following four years, revisions were made to the Project Design Guidelines and implemented for ongoing and future new construction and renovation work at

ABSTRACT Upon joining Arizona State University in July 2017, the author, a registered architect, inherited the oversight of the University Project Design Guidelines. During the following four years, revisions were made to the Project Design Guidelines and implemented for ongoing and future new construction and renovation work at all five Arizona State University campuses. During this time, it became evident that many projects were not following guidelines resulting in costly rework, or hastily submitted variance requests to avoid or replace the design guidelines, typically during, versus prior to, construction. Tracking of these variance requests began in Summer 2020 identifying some commonly requested variance items for discussion by the Project Guidelines Steering Committee. In June 2021, a progressive design-build solicitation was held for a new campus building. During the interview process it was evident that not all parties on the design-build team (owner, architect and general contractor) had the same understanding of the role, importance, or reasoning for project design guidelines. The confusion demonstrated during the variance and interview process made the author curious as to the overall sentiment of design standards in the industry. What areas of project guidelines are emphasized by universities? Is there a correlation between guideline information and the greatest/least amount of construction costs? Can universities be better served by focusing on a comprehensive understanding and implementation of project design guidelines that impact the greatest construction cost of the project?
ContributorsLisiewski II, Joseph Vincent (Author) / Sullivan, Kenneth (Thesis advisor) / Hurtato, Kristen (Committee member) / Standage, Richard (Committee member) / Arizona State University (Publisher)
Created2022
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Description
The construction industry is the backbone of any country’s economy. It is a primary source of foreign investments, creates new jobs, and maintains the economy flowing in various trades. Accurate cost estimation is a critical aspect for the construction industry, directly impacting project success and profitability. This master's thesis focuses

The construction industry is the backbone of any country’s economy. It is a primary source of foreign investments, creates new jobs, and maintains the economy flowing in various trades. Accurate cost estimation is a critical aspect for the construction industry, directly impacting project success and profitability. This master's thesis focuses on comprehensively identifying the key factors that influence cost estimation and provides valuable recommendations for constructing an optimized Artificial Neural Network (ANN) model. Through an extensive research methodology encompassing literature review, surveys, and interviews with industry professionals, this study uncovers significant factors that exert a substantial impact on cost estimation practices. The findings emphasize the importance of seamlessly integrating project delivery systems, meticulously considering project duration, and incorporating diverse perspectives from global regions. By incorporating these insights, stakeholders can make informed decisions, enhance project planning, and elevate overall project performance. This study successfully bridges the gap between theory and practice, presenting invaluable insights for stakeholders within the construction industry. Keywords: cost estimation, construction industry, Artificial Neural Network, factors, project delivery systems, project duration, global perspectives, informed decision-making, project planning, project performance
ContributorsAL Saber, Salem Samer (Author) / Sullivan, Kenneth (Thesis advisor) / Hurtado, Kristian (Committee member) / Standage, Richard (Committee member) / Arizona State University (Publisher)
Created2023
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Description
This research seeks to better understand the current state of US healthcare FM industry hiring practices from colleges and universities to identify potential employment barriers into healthcare FM and interventions to help overcome them. Two national surveys were distributed to healthcare facility managers and directors to collect quantifiable information

This research seeks to better understand the current state of US healthcare FM industry hiring practices from colleges and universities to identify potential employment barriers into healthcare FM and interventions to help overcome them. Two national surveys were distributed to healthcare facility managers and directors to collect quantifiable information on healthcare organizations, hiring practices from FM academic programs, individual demographics, and opinions of FM college graduates. Designated survey respondents were also contacted for phone interviews. Additionally, a Delphi method was used for this research to draw upon the collective knowledge and experience of 13 experts over three iterative rounds of input.

Results indicate that the healthcare FM industry is hiring very few college interns and new college graduates for entry-level management jobs. Strong homogeneousness demographics, backgrounds, and paths of entry among existing healthcare FM professionals has created an industry bias against candidates attempting to enter healthcare FM from non-traditional sources. The healthcare FM industry’s principal source for new talent comes from building trade succession within healthcare organizations. However, continuing to rely on building tradespersons as the main path of entry into the healthcare FM industry may prove problematic. Most existing healthcare facility managers and directors will be retiring within 10 years, yet it is taking more than 17 years of full-time work experience to prepare building tradespersons to assume these roles.

New college graduates from FM academic programs are a viable recruitment source for new talent into healthcare FM as younger professionals are commonly entering the healthcare FM through the path of higher education. Although few new college graduates enter the healthcare FM industry, they are experiencing similar promotion timeframes compared to other candidate with many years of full-time work experience. Unfamiliarity with FM academic programs, work experience requirements, limited entry-level jobs within small organizations, low pay, and a limited exposure to healthcare industry topics present challenges for new FM college graduates attempting to enter the healthcare FM industry. This study shows that gaps indeed exist in student learning outcomes for a comprehensive healthcare FM education; key technical topics specific to the healthcare industry are not being addressed by organizations accrediting construction and facility management academic programs. A framework is proposed for a comprehensive healthcare FM education including accreditation, regulatory and code compliance, infection control, systems in healthcare facilities, healthcare construction project management and methods, and clinical operations and medical equipment. Interestingly, academics in the field of FM generally disagree with industry professionals that these technical topics are important student learning outcomes. Consequently, FM academics prefer to teach students general FM principles with the expectation that specific technical knowledge will be gained in the workplace after graduation from college. Nevertheless, candidates attempting to enter healthcare FM without industry specific knowledge are disadvantaged due to industry perceptions and expectations. University-industry linkage must be improved to successfully attract students into the field of healthcare FM and establish colleges and universities as a sustainable recruitment source in helping address FM attrition.

This paper is valuable in establishing the current state of the US healthcare industry’s hiring practices from FM academic programs and identifying major barriers of entering the healthcare FM industry for new FM college graduates. Findings facilitate development of interventions by healthcare organizations and universities to further open FM academic programs as a sustainable source of new talent to help address healthcare FM attrition, including a healthcare FM education framework to elucidate college student learning outcomes for successful employment in healthcare FM. These student learning outcomes provide a framework for both the healthcare industry and academia in preparing future facility managers.
ContributorsCall, Steven Alan (Author) / Sullivan, Kenneth (Thesis advisor) / Hurtado, Kristen (Committee member) / Standage, Richard (Committee member) / Arizona State University (Publisher)
Created2019
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Description
Standardized processes for training and accountability, for an Environmental Services department within a healthcare system, were implemented to see the impact they would have on key performance indicators (KPIs). The KPIs involved infection rate for hospital acquired Clostridium Difficile (CDI), cleaning verification compliance, patient satisfaction, concerning the cleaning of their

Standardized processes for training and accountability, for an Environmental Services department within a healthcare system, were implemented to see the impact they would have on key performance indicators (KPIs). The KPIs involved infection rate for hospital acquired Clostridium Difficile (CDI), cleaning verification compliance, patient satisfaction, concerning the cleaning of their environment, and employee turnover. The results show that standardizing training and an accountability measure can have a significant impact on turnover, contribute to the reduction in CDI cases, ensure cleaning is performed at a high level and that the patient perception requires additional tools to meet their expectations on a consistent basis.
ContributorsZiffer, Steven (Author) / Sullivan, Kenneth (Thesis advisor) / Smithwick, Jake (Committee member) / Lines, Brian (Committee member) / Arizona State University (Publisher)
Created2017
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Description
The alternative project delivery methods (APDMs) today are being increasingly used by owner organizations in the architecture, engineering, and construction (AEC) industry. Yet the adoption of these methods can be extremely difficult to accomplish and requires significant change management efforts. To facilitate the APDM adoption, this research aimed to better

The alternative project delivery methods (APDMs) today are being increasingly used by owner organizations in the architecture, engineering, and construction (AEC) industry. Yet the adoption of these methods can be extremely difficult to accomplish and requires significant change management efforts. To facilitate the APDM adoption, this research aimed to better understand how AEC owner organizations have changed from only using the design-bid-build method to also successfully implementing APDMs from an organizational change perspective. This research utilized a literature review, survey and interviews to fulfill the research objectives. The dissertation follows a three paper format. The first paper focuses on identifying organizational change management (OCM) practices that, when effectively executed, lead to increased success rates of adopting APDMs in owner AEC organizations. The results of the first paper indicated that the five OCM practices with the strongest correlations to successful APDM adoption were realistic timeframe, effective change agents, workload adjustments, senior-leadership commitment, and sufficient change-related training. The second paper focuses on investigating AEC employees’ reactions to the adoption of APDMs. The findings of the second paper revealed that employees in AEC organizations react favorably to adopting a change in their project delivery systems. The findings further revealed that increasing the use of OCM practices is related to decreased employee resistance to change. The third paper aimed to provide guidelines detailing on how to lead APDM adoption. The findings of the third paper indicated that there was a general sequence of four implementation phases, which were preparing and planning, pilot project testing, expanding to the intended scale, and sustaining and evaluating. The phases include specific OCM practices that increase the probability of successful APDM adoption. The dissertation results can help in guiding the senior managers of construction organizations and OCM consultants to effectively implement APDMs for the first time in the construction sector.
ContributorsAldossari, Khaled Medath (Author) / Sullivan, Kenneth T. (Thesis advisor) / Hurtado, Kristen C (Committee member) / Standage, Richard (Committee member) / Arizona State University (Publisher)
Created2020