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- Genre: Doctoral Dissertation
Description
Deformation during hydration of concrete includes curling at joints and terminations. Previous research has explored mix designs, chemical additives, and other material factors to minimize slab distortion due to curling. This research study explores the development and use of externally applied silicone-based compounds after both the placing and cutting of joints. This exploratory study presents the results of controlled testing and a field study results that include distortion of contraction joints as measured with a Spectra LL300N under existing environmental conditions. Specifically, the study presents the results of a side-by-side test of two slabs, a base case, and a silicone-altered case, as well as field measures of two large commercial buildings using the developed methods. The results of the study show reduced distortion due to curling as compared to standard comparative slabs and warrant the continued exploration and testing of the concept.
ContributorsStandage, Richard Mc Rae (Author) / Ernzen, James (Thesis advisor) / Sullivan, Kenneth (Committee member) / Knutson, Kraig (Committee member) / Arizona State University (Publisher)
Created2019
Description
The solar energy sector has been growing rapidly over the past decade. Growth in renewable electricity generation using photovoltaic (PV) systems is accompanied by an increased awareness of the fault conditions developing during the operational lifetime of these systems. While the annual energy losses caused by faults in PV systems could reach up to 18.9% of their total capacity, emerging technologies and models are driving for greater efficiency to assure the reliability of a product under its actual application. The objectives of this dissertation consist of (1) reviewing the state of the art and practice of prognostics and health management for the Direct Current (DC) side of photovoltaic systems; (2) assessing the corrosion of the driven posts supporting PV structures in utility scale plants; and (3) assessing the probabilistic risk associated with the failure of polymeric materials that are used in tracker and fixed tilt systems.
As photovoltaic systems age under relatively harsh and changing environmental conditions, several potential fault conditions can develop during the operational lifetime including corrosion of supporting structures and failures of polymeric materials. The ability to accurately predict the remaining useful life of photovoltaic systems is critical for plants ‘continuous operation. This research contributes to the body of knowledge of PV systems reliability by: (1) developing a meta-model of the expected service life of mounting structures; (2) creating decision frameworks and tools to support practitioners in mitigating risks; (3) and supporting material selection for fielded and future photovoltaic systems. The newly developed frameworks were validated by a global solar company.
As photovoltaic systems age under relatively harsh and changing environmental conditions, several potential fault conditions can develop during the operational lifetime including corrosion of supporting structures and failures of polymeric materials. The ability to accurately predict the remaining useful life of photovoltaic systems is critical for plants ‘continuous operation. This research contributes to the body of knowledge of PV systems reliability by: (1) developing a meta-model of the expected service life of mounting structures; (2) creating decision frameworks and tools to support practitioners in mitigating risks; (3) and supporting material selection for fielded and future photovoltaic systems. The newly developed frameworks were validated by a global solar company.
ContributorsChokor, Abbas (Author) / El Asmar, Mounir (Thesis advisor) / Chong, Oswald (Committee member) / Ernzen, James (Committee member) / Arizona State University (Publisher)
Created2017
Description
The construction industry is becoming more aware of its impact on the environment. It has become more sensitive to how it operates and how it can reduce the carbon footprint of the construction process. This research identifies the source of and quantities of the carbon emissions created by an operating modular home fabrication plant in producing, transporting and installing modular structures. This study demonstrates how to measure the carbon footprint created in the production of a modular home. It quantifies and reports the results on a home, on a single module and on a per square foot basis. The primary conclusions of this study are: a) electricity was found to be the largest energy source used in this fabrication process; b) the modular fabrication process consumes a significant amount of electrical energy per month; c) production volume has a bearing on the carbon footprint of each home since the carbon footprint for each period is allocated to every home produced in that period; and d) transportation of fabricated modules and set-up add to the carbon footprint. Further, a carbon calculator was produced and is included with the study. The tool calculates the impact of energy consumption on the carbon footprint of a modular factory or a modular home. It may be expanded to other process driven fabrication entities. This research is valuable to developers and builders who wish to measure the carbon impact of a modular new home delivery system. The study also provides a methodology for modular home fabricators to measure the carbon footprint of their factories and factory production.
ContributorsKawecki, Leonard Robert (Author) / Bashford, Howard H (Thesis advisor) / Davis, Joseph (Committee member) / Ernzen, James (Committee member) / Arizona State University (Publisher)
Created2010
Description
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