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Description
Trenchless technology is a group of techniques whose utilization allows for the installation, rehabilitation, and repair of underground infrastructure with minimal excavation from the ground surface. As the built environment becomes more congested, projects are trending towards using trenchless technologies for their ability to quickly produce a quality product with minimal environmental and social costs. Pilot tube microtunneling (PTMT) is a trenchless technology where new pipelines may be installed at accurate and precise line and grade over manhole to manhole distances. The PTMT process can vary to a certain degree, but typically involves the following three phases: jacking of the pilot tube string to achieve line and grade, jacking of casing along the pilot bore and rotation of augers to excavate the borehole to a diameter slightly larger than the product pipe, and jacking of product pipe directly behind the last casing. Knowledge of the expected productivity rates and jacking forces during a PTMT installation are valuable tools that can be used for properly weighing its usefulness versus competing technologies and minimizing risks associated with PTMT. This thesis outlines the instrumentation and monitoring process used to record jacking frame hydraulic pressures from seven PTMT installations. Cyclic patterns in the data can be detected, indicating the installation of a single pipe segment, and enabling productivity rates for each PTMT phase to be determined. Furthermore, specific operations within a cycle, such as pushing a pipe or retracting the machine, can be observed, allowing for identification of the critical tasks associated with each phase. By identifying the critical tasks and developing more efficient means for their completion, PTMT productivity can be increased and costs can be reduced. Additionally, variations in depth of cover, drive length, pipe diameter, and localized ground conditions allowed for trends in jacking forces to be identified. To date, jacking force predictive models for PTMT are non-existent. Thus, jacking force data was compared to existing predictive models developed for the closely related pipe jacking and microtunneling methodologies, and the applicability of their adoption for PTMT jacking force prediction was explored.
ContributorsOlson, Matthew P (Author) / Ariaratnam, Samuel T (Thesis advisor) / Lueke, Jason S (Committee member) / Zapata, Claudia E (Committee member) / Tang, Pingbo (Committee member) / Arizona State University (Publisher)
Created2013
Description
Structural/system health monitoring (SHM) and prognostic health management (PHM) are vital techniques to ensure engineering system reliability and safety during the service. As multi-functionality and enhanced performance are in demand, modern engineering systems including aerospace, mechanical, and civil applications have become more complex. The constituent and architectural complexity, and multisource sensing sources in modern engineering systems may limit the monitoring capabilities of conventional approaches and require more advanced SHM/PHM techniques. Therefore, a hybrid methodology that incorporates information fusion, nondestructive evaluation (NDE), machine learning (ML), and statistical analysis is needed for more effective damage diagnosis/prognosis and system safety management.This dissertation presents an automated aviation health management technique to enable proactive safety management for both aircraft and national airspace system (NAS). A real-time, data-driven aircraft safety monitoring technique using ML models and statistical models is developed to enable an early-stage upset detection capability, which can improve pilot’s situational awareness and provide a sufficient safety margin. The detection accuracy and computational efficiency of the developed monitoring techniques is validated using commercial unlabeled flight data recorder (FDR) and reported accident FDR dataset. A stochastic post-upset prediction framework is developed using a high-fidelity flight dynamics model to predict the post-impacts in both aircraft and air traffic system. Stall upset scenarios that are most likely occurred during loss of control in-flight (LOC-I) operation are investigated, and stochastic flight envelopes and risk region are predicted to quantify their severities.
In addition, a robust, automatic damage diagnosis technique using ultrasonic Lamb waves and ML models is developed to effectively detect and classify fatigue damage modes in composite structures. The dispersion and propagation characteristics of the Lamb waves in a composite plate are investigated. A deep autoencoder-based diagnosis technique is proposed to detect fatigue damage using anomaly detection approach and automatically extract damage sensitive features from the waves. The patterns in the features are then further analyzed using outlier detection approach to classify the fatigue damage modes. The developed diagnosis technique is validated through an in-situ fatigue tests with periodic active sensing.
The developed techniques in this research are expected to be integrated with the existing safety strategies to enhance decision making process for improving engineering system safety without affecting the system’s functions.
ContributorsLee, Hyunseong (Author) / Chattopadhyay, Aditi (Thesis advisor) / Liu, Yongming (Committee member) / Papandreou-Suppappola, Antonia (Committee member) / Fard, Masoud Yekani (Committee member) / Tang, Pingbo (Committee member) / Campbell, Angela (Committee member) / Arizona State University (Publisher)
Created2021