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Statistical models for prediction of mechanical property and manufacturing process parameters for gas pipeline steels


Abstract Pipeline infrastructure forms a vital aspect of the United States economy and standard of living. A majority of the current pipeline systems were installed in the early 1900’s and often lack a reliable database reporting the mechanical properties, and information about manufacturing and installation, thereby raising a concern for their safety and integrity. Testing for the aging pipe strength and toughness estimation without interrupting the transmission and operations thus becomes important. The state-of-the-art techniques tend to focus on the single modality deterministic estimation of pipe strength and do not account for inhomogeneity and uncertainties, many others appear to rely on destructive means. These gaps provide an impetus for no... (more)
Created Date 2018
Contributor Dahire, Sonam (Author) / Liu, Yongming (Advisor) / Jiao, Yang (Committee member) / Ren, Yi (Committee member) / Arizona State University (Publisher)
Subject Materials Science / Bayesian Network / fatigue crack growth / multimodal diagnosis / probabilistic
Type Doctoral Dissertation
Extent 116 pages
Language English
Copyright
Note Doctoral Dissertation Materials Science and Engineering 2018
Collaborating Institutions Graduate College / ASU Library
Additional Formats MODS / OAI Dublin Core / RIS


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Description Dissertation/Thesis