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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- All Subjects: Damage Prognosis
- All Subjects: Predictive control
Description
There is a concerted effort in developing robust systems health monitoring/management (SHM) technology as a means to reduce the life cycle costs, improve availability, extend life and minimize downtime of various platforms including aerospace and civil infrastructure. The implementation of a robust SHM system requires a collaborative effort in a variety of areas such as sensor development, damage detection and localization, physics based models, and prognosis models for residual useful life (RUL) estimation. Damage localization and prediction is further complicated by geometric, material, loading, and environmental variabilities. Therefore, it is essential to develop robust SHM methodologies by taking into account such uncertainties. In this research, damage localization and RUL estimation of two different physical systems are addressed: (i) fatigue crack propagation in metallic materials under complex multiaxial loading and (ii) temporal scour prediction near bridge piers. With little modifications, the methodologies developed can be applied to other systems.
Current practice in fatigue life prediction is based on either physics based modeling or data-driven methods, and is limited to predicting RUL for simple geometries under uniaxial loading conditions. In this research, crack initiation and propagation behavior under uniaxial and complex biaxial fatigue loading is addressed. The crack propagation behavior is studied by performing extensive material characterization and fatigue testing under in-plane biaxial loading, both in-phase and out-of-phase, with different biaxiality ratios. A hybrid prognosis model, which combines machine learning with physics based modeling, is developed to account for the uncertainties in crack propagation and fatigue life prediction due to variabilities in material microstructural characteristics, crack localization information and environmental changes. The methodology iteratively combines localization information with hybrid prognosis models using sequential Bayesian techniques. The results show significant improvements in the localization and prediction accuracy under varying temperature.
For civil infrastructure, especially bridges, pier scour is a major failure mechanism. Currently available techniques are developed from a design perspective and provide highly conservative scour estimates. In this research, a fully probabilistic scour prediction methodology is developed using machine learning to accurately predict scour in real-time under varying flow conditions.
Current practice in fatigue life prediction is based on either physics based modeling or data-driven methods, and is limited to predicting RUL for simple geometries under uniaxial loading conditions. In this research, crack initiation and propagation behavior under uniaxial and complex biaxial fatigue loading is addressed. The crack propagation behavior is studied by performing extensive material characterization and fatigue testing under in-plane biaxial loading, both in-phase and out-of-phase, with different biaxiality ratios. A hybrid prognosis model, which combines machine learning with physics based modeling, is developed to account for the uncertainties in crack propagation and fatigue life prediction due to variabilities in material microstructural characteristics, crack localization information and environmental changes. The methodology iteratively combines localization information with hybrid prognosis models using sequential Bayesian techniques. The results show significant improvements in the localization and prediction accuracy under varying temperature.
For civil infrastructure, especially bridges, pier scour is a major failure mechanism. Currently available techniques are developed from a design perspective and provide highly conservative scour estimates. In this research, a fully probabilistic scour prediction methodology is developed using machine learning to accurately predict scour in real-time under varying flow conditions.
ContributorsNeerukatti, Rajesh Kumar (Author) / Chattopadhyay, Aditi (Thesis advisor) / Jiang, Hanqing (Committee member) / Liu, Yongming (Committee member) / Rajadas, John (Committee member) / Yekani Fard, Masoud (Committee member) / Arizona State University (Publisher)
Created2016
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