ASU Electronic Theses and Dissertations
This collection includes most of the ASU Theses and Dissertations from 2011 to present. ASU Theses and Dissertations are available in downloadable PDF format; however, a small percentage of items are under embargo. Information about the dissertations/theses includes degree information, committee members, an abstract, supporting data or media.
In addition to the electronic theses found in the ASU Digital Repository, ASU Theses and Dissertations can be found in the ASU Library Catalog.
Dissertations and Theses granted by Arizona State University are archived and made available through a joint effort of the ASU Graduate College and the ASU Libraries. For more information or questions about this collection contact or visit the Digital Repository ETD Library Guide or contact the ASU Graduate College at gradformat@asu.edu.
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- All Subjects: engineering
This research, sponsored by National Academy of Construction, the Construction Industry Institute and Arizona State University, used structured interviews, a Delphi study and focus groups to explore: 1) potential benefit and industry interest in an open repository of best practices and 2) important elements of a framework/model that guides the creation, management and sustainment of an open repository of best practices.
This dissertation presents findings specifically exploring the term "Practices for Excellence", its definition, elements that hinder implementation, the potential value of an open online repository for such practices and a model to develop an open repository.
Designing out waste is the core principle of the CE. Design for disassembly or design for deconstruction (DfD) is the practice of planning the future deconstruction of a building and the reuse of its materials. Concepts like DfD, CE, and product-service systems (PSS) can work together to promote CLC in the built environment. PSS are business models based on stewardship instead of ownership. CE combines DfD, PSS, materials’ durability, and materials’ reuse in multiple life cycles to promote a low-carbon, regenerative economy. CE prioritizes reuse over recycling. Dealing with resource scarcity demands us to think beyond the incremental changes from recycling waste; it demands an urgent, systemic, and radical change in the way we design, build, and procure construction materials.
This dissertation aims to answer three research questions: 1) How can researchers estimate the environmental benefits of reusing building components, 2) What variables are susceptible to affect the environmental impact assessment of reuse, and 3) What are the barriers and opportunities for DfD and materials’ reuse in the current design practice in the United States.
The first part of this study investigated how different life cycle assessment (LCA) methods (i.e., hybrid LCA and process-based LCA), assumptions (e.g., reuse rates, transportation distances, number of reuses), and LCA timelines can affect the results of a closed-loop LCA. The second part of this study built on interviews with architects in the United States to understand why DfD is not part of the current design practice in the country.
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.