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Physical Sciences and Mathematics Commons

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Full-Text Articles in Physical Sciences and Mathematics

Improving Data-Driven Infrastructure Degradation Forecast Skill With Stepwise Asset Condition Prediction Models, Kurt R. Lamm, Justin D. Delorit, Michael N. Grussing, Steven J. Schuldt Aug 2022

Improving Data-Driven Infrastructure Degradation Forecast Skill With Stepwise Asset Condition Prediction Models, Kurt R. Lamm, Justin D. Delorit, Michael N. Grussing, Steven J. Schuldt

Faculty Publications

Organizations with large facility and infrastructure portfolios have used asset management databases for over ten years to collect and standardize asset condition data. Decision makers use these data to predict asset degradation and expected service life, enabling prioritized maintenance, repair, and renovation actions that reduce asset life-cycle costs and achieve organizational objectives. However, these asset condition forecasts are calculated using standardized, self-correcting distribution models that rely on poorly-fit, continuous functions. This research presents four stepwise asset condition forecast models that utilize historical asset inspection data to improve prediction accuracy: (1) Slope, (2) Weighted Slope, (3) Condition-Intelligent Weighted Slope, and (4) …


A Post-Disaster Construction Portfolio Optimization Framework For Tyndall Afb Rebuild Post Hurricane Michael, Andre J. May Mar 2022

A Post-Disaster Construction Portfolio Optimization Framework For Tyndall Afb Rebuild Post Hurricane Michael, Andre J. May

Theses and Dissertations

Natural disasters such as hurricanes, earthquakes, tsunamis, and extreme flooding cause severe social and economic disruptions. Restoration of social and revenue-generating services often requires extensive reconstruction, from the facility to the campus scale. For multi-facility portfolios, decision-makers must prioritize post-disaster reconstruction activities appropriately to ensure facilities and infrastructure are restored. In addition, any expansion or new construction initiatives are ideally completed in order of decision-maker and community preference. Most post-disaster optimization and decision framework research consider a single stakeholder as guiding decisions related to a project portfolio. However, these portfolio prioritization frameworks ignore the effect of multiple stakeholders and competing …


A Critical Review Of Climate Change On Coastal Infrastructure Systems, Gregory J. Howland Jr. Mar 2022

A Critical Review Of Climate Change On Coastal Infrastructure Systems, Gregory J. Howland Jr.

Theses and Dissertations

This thesis is a response to climate threats identified by DoD report on Climate Change in 2019. A critical review of climate change literature related to coastal infrastructure was conducted to synthesize past research and to inform future research. This review intends to inform how climate change may impact infrastructure systems, how those impacts are evaluated, can the investigation be improved, and what can stakeholders learn from the outcomes. The end goal is to find climate change mitigation strategies and adaptation measures, or identify the easiest path to get to that end. The compiled information will inform civilian and military …


Application Of Machine Learning Models With Numerical Simulations Of An Experimental Microwave Induced Plasma Gasification Reactor, Owen D. Sedej Mar 2022

Application Of Machine Learning Models With Numerical Simulations Of An Experimental Microwave Induced Plasma Gasification Reactor, Owen D. Sedej

Theses and Dissertations

This thesis aims to contribute to the future development of this technology by providing an in-depth literature review of how this technology physically operates and can be numerically modeled. Additionally, this thesis reviews literature of machine learning models that have been applied to gasification to make accurate predictions regarding the system. Finally, this thesis provides a framework of how to numerically model an experimental plasma gasification reactor in order to inform a variety of machine learning models.