Theses and Dissertations
This collection includes both ASU Theses and Dissertations, submitted by graduate students, and the Barrett, Honors College theses submitted by undergraduate students.
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- Creators: Ankit, Kumar
- Creators: Ayer, Steven K
Description
Interpersonal communications during civil infrastructure systems operation and maintenance (CIS O&M) are processes for CIS O&M participants to exchange critical information. Poor communications that provide misleading information can jeopardize CIS O&M safety and efficiency. Previous studies suggest that communication contexts and features could be indicators of communication errors and relevant CIS O&M risks. However, challenges remain for reliable prediction of communication errors to ensure CIS O&M safety and efficiency. For example, existing studies lack a systematic summarization of risky contexts and features of communication processes for predicting communication errors. Limited studies examined quantitative methods for incorporating expert opinions as constraints for reliable communication error prediction. How to examine mitigation strategies (e.g., adjustments of communication protocols) for reducing communication-related CIS O&M risks is also challenging. The main reason is the lack of causal analysis about how various factors influence the occurrences and impacts of communication errors so that engineers lack the basis for intervention.
This dissertation presents a method that integrates Bayesian Network (BN) modeling and simulation for communication-related risk prediction and mitigation. The proposed method aims at tackling the three challenges mentioned above for ensuring CIS O&M safety and efficiency. The proposed method contains three parts: 1) Communication Data Collection and Error Detection – designing lab experiments for collecting communication data in CIS O&M workflows and using the collected data for identifying risky communication contexts and features; 2) Communication Error Classification and Prediction – encoding expert knowledge as constraints through BN model updating to improve the accuracy of communication error prediction based on given communication contexts and features, and 3) Communication Risk Mitigation – carrying out simulations to adjust communication protocols for reducing communication-related CIS O&M risks.
This dissertation uses two CIS O&M case studies (air traffic control and NPP outages) to validate the proposed method. The results indicate that the proposed method can 1) identify risky communication contexts and features, 2) predict communication errors and CIS O&M risks, and 3) reduce CIS O&M risks triggered by communication errors. The author envisions that the proposed method will shed light on achieving predictive control of interpersonal communications in dynamic and complex CIS O&M.
This dissertation presents a method that integrates Bayesian Network (BN) modeling and simulation for communication-related risk prediction and mitigation. The proposed method aims at tackling the three challenges mentioned above for ensuring CIS O&M safety and efficiency. The proposed method contains three parts: 1) Communication Data Collection and Error Detection – designing lab experiments for collecting communication data in CIS O&M workflows and using the collected data for identifying risky communication contexts and features; 2) Communication Error Classification and Prediction – encoding expert knowledge as constraints through BN model updating to improve the accuracy of communication error prediction based on given communication contexts and features, and 3) Communication Risk Mitigation – carrying out simulations to adjust communication protocols for reducing communication-related CIS O&M risks.
This dissertation uses two CIS O&M case studies (air traffic control and NPP outages) to validate the proposed method. The results indicate that the proposed method can 1) identify risky communication contexts and features, 2) predict communication errors and CIS O&M risks, and 3) reduce CIS O&M risks triggered by communication errors. The author envisions that the proposed method will shed light on achieving predictive control of interpersonal communications in dynamic and complex CIS O&M.
ContributorsSun, Zhe (Author) / Tang, Pingbo (Thesis advisor) / Ayer, Steven K (Committee member) / Cooke, Nancy J. (Committee member) / Liu, Yongming (Committee member) / Arizona State University (Publisher)
Created2020
Description
AA 7XXX alloys are used extensively in aircraft and naval structures due to their excellent strength to weight ratio. These alloys are often exposed to harsh corrosive environments and mechanical stresses that can compromise their reliability in service. They are also coupled with fasteners that are composed of different materials such as Titanium alloys. Such dissimilar metal contact facilitates galvanic and crevice corrosion, which can further reduce their lifetimes. Despite decades of research in the area, the confluence of mechanical, microstructural, and electrochemical aspects of damage is still unclear. Traditionally, 2D and destructive methods have often been employed to study the corrosion and cracking behavior in these systems which can be severely limiting and lead to inaccurate conclusions. This dissertation is aimed at comprehensively studying the corrosion and cracking behavior of these systems using time-dependent 3D microstructural characterization, as well as correlative microscopy. The microstructural evolution of corrosion in AA 7075 was studied using a combination of potentiodynamic polarization, X-ray Computed Tomography (XCT) and Transmission X-ray Microscopy (TXM). In both experiments, a strong emphasis was placed on studying localized corrosion attack at constituent particles and intergranular corrosion. With an understanding of the alloy’s corrosion behavior, a dissimilar alloy couple comprising AA 7075 / Ti-6Al-4V was then investigated. Ex situ and in situ x-ray microtomography was used extensively to investigate the evolution of pitting corrosion and corrosion fatigue in AA 7075 plates fastened separately with Ti-6Al-4V screws and rivets. The 4D tomography combined with the extensive fractography yielded valuable information pertaining the preferred sites of pit initiation, crack initiation and growth in these complex geometries. The use of correlative microscopy-based methodologies yielded multimodal characterization results that provided a unique and seminal insight on corrosion mechanisms in these materials.
ContributorsNiverty, Sridhar (Author) / Chawla, Nikhilesh (Thesis advisor) / Liu, Yongming (Committee member) / Ankit, Kumar (Committee member) / Xiao, Xianghui (Committee member) / Arizona State University (Publisher)
Created2020