Please use this identifier to cite or link to this item: http://arks.princeton.edu/ark:/88435/dsp01p5547v31c
 Title: Diagnosing masonry structures using advanced sensing techniques, physics-based modeling, and supervised learning Authors: Napolitano, Rebecac Advisors: Glisic, Branko Contributors: Civil and Environmental Engineering Department Subjects: Architectural engineeringArchaeology Issue Date: 2020 Publisher: Princeton, NJ : Princeton University Abstract: Continued advances in computational, networking, and sensor technologies have begun to bring urban-scale structural health monitoring and prognostics into the realm of possibility. We are starting to think about how events can not only impact single buildings, but also how a city can operate as a network of sensors and how we can leverage urban-scale digital twins to develop smart, sustainable cities. In that city-scale digital twin however, aging infrastructure introduces a myriad of unknowns such as material condition, load paths, etc. So what should we do with our aging infrastructure? What is the place of buildings of the past in cities of the future? To answer these questions, my research focuses on minimizing uncertainties regarding the condition of aging infrastructure through new methods and applications of data extraction, multi-modal data fusion, hybrid analytics, and information modeling. I have had success creating a novel method for integrating non-destructive testing, laser scanning, and numerical modeling for damage assessment which reduces the time required for a practitioner to assess the condition as well as decreases the likelihood of diagnostic bias affecting the resulting decisions. Additionally, I have quantified how different levels of detail for modeling and simulation can affect the results of diagnostics. I found that in some cases more detailed simulations can be 100 times slower; thus practitioners should think carefully about what they need from a model before running simulations. Additionally, it was shown that if the geometry and initial conditions are not captured accurately, these can skew the results of diagnostics. Lastly, I have developed a new technique for combining physics-based modeling and machine learning tools for diagnostics of masonry which is $10^5$ times faster than prior methods and requires less human intervention. These results illustrate that computational tools can increase the efficiency and decrease the time associated with diagnostics of existing infrastructure. Additionally, these results show how new, digital solutions for diagnostics will play a critical role in preservation and reuse efforts for developing the next generation of smart and green infrastructure. URI: http://arks.princeton.edu/ark:/88435/dsp01p5547v31c Alternate format: The Mudd Manuscript Library retains one bound copy of each dissertation. Search for these copies in the library's main catalog: catalog.princeton.edu Type of Material: Academic dissertations (Ph.D.) Language: en Appears in Collections: Civil and Environmental Engineering