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- All Subjects: engineering
- Creators: Liu, Yongming
Description
Public Private Partnerships (PPP) have been in use for years in the United Kingdom, Europe, Australia and for a shorter time here in the United States. Typical PPP infrastructure projects include a multi-year term of operation in addition to constructing the structural features to be used. Early studies are proving PPP delivery methods to be effective at construction cost containment. An examination of the key elements that constitute the early stage negotiation reveal that there is room for negotiation created by the governing documentation while maintaining a competitive environment that brings the best value available to the Public entity. This paper will examine why PPP's are effective during this critical construction period of the facilities life cycle. It is the intent of this study to examine why the features and outcomes of more or less negotiation and the degree of rigor associated with it.
ContributorsMaddex, William E (Author) / Chasey, Allan (Thesis advisor) / El Asmar, Mounir (Committee member) / Pendyala, Ram (Committee member) / Arizona State University (Publisher)
Created2012
Description
Sustainable Materials Management and Circular Economy are both frameworks for considering the way we interact with the world's resources. Different organizations and institutions across the world have adopted one philosophy or the other. To some, there seems to be little overlap of the two, and to others, they are perceived as being interchangeable. This paper evaluates Sustainable Materials Management (SMM) and Circular Economy (CE) individually and in comparison to see how truly different these frameworks are from one another. This comparison is then extended into a theoretical walk-through of an SMM treatment of concrete pavement in contrast with a CE treatment. With concrete being a ubiquitous in the world's buildings and roads, as well as being a major constituent of Construction & Demolition waste generated, its analysis is applicable to a significant portion of the world's material flow. The ultimate test of differentiation between SMM and CE would ask: 1) If SMM principles guided action, would the outcomes be aligned with or at odds with CE principles? and conversely 2) If CE principles guided action, would the outcomes be aligned with or at odds with SMM principles? Using concrete pavement as an example, this paper seeks to determine whether or not Sustainable Materials Management and Circular Economy are simply different roads leading to the same destination.
ContributorsAbdul-Quadir, Anisa (Author) / Kelman, Candice (Thesis director) / Buch, Rajesh (Committee member) / Barrett, The Honors College (Contributor)
Created2017-05
Description
This research examines the current challenges of using Lamb wave interrogation methods to localize fatigue crack damage in a complex metallic structural component subjected to unknown temperatures. The goal of this work is to improve damage localization results for a structural component interrogated at an unknown temperature, by developing a probabilistic and reference-free framework for estimating Lamb wave velocities and the damage location. The methodology for damage localization at unknown temperatures includes the following key elements: i) a model that can describe the change in Lamb wave velocities with temperature; ii) the extension of an advanced time-frequency based signal processing technique for enhanced time-of-flight feature extraction from a dispersive signal; iii) the development of a Bayesian damage localization framework incorporating data association and sensor fusion. The technique requires no additional transducers to be installed on a structure, and allows for the estimation of both the temperature and the wave velocity in the component. Additionally, the framework of the algorithm allows it to function completely in an unsupervised manner by probabilistically accounting for all measurement origin uncertainty. The novel algorithm was experimentally validated using an aluminum lug joint with a growing fatigue crack. The lug joint was interrogated using piezoelectric transducers at multiple fatigue crack lengths, and at temperatures between 20°C and 80°C. The results showed that the algorithm could accurately predict the temperature and wave speed of the lug joint. The localization results for the fatigue damage were found to correlate well with the true locations at long crack lengths, but loss of accuracy was observed in localizing small cracks due to time-of-flight measurement errors. To validate the algorithm across a wider range of temperatures the electromechanically coupled LISA/SIM model was used to simulate the effects of temperatures. The numerical results showed that this approach would be capable of experimentally estimating the temperature and velocity in the lug joint for temperatures from -60°C to 150°C. The velocity estimation algorithm was found to significantly increase the accuracy of localization at temperatures above 120°C when error due to incorrect velocity selection begins to outweigh the error due to time-of-flight measurements.
ContributorsHensberry, Kevin (Author) / Chattopadhyay, Aditi (Thesis advisor) / Liu, Yongming (Committee member) / Papandreou-Suppappola, Antonia (Committee member) / Arizona State University (Publisher)
Created2013
Description
An Earned Value Management System (EVMS) is an organization’s system for project/program management that integrates a defined set of associated work scopes, schedules and budgets, allowing for effective planning, performance, and management control. A mature EVMS that is compliant with standards and guidelines, and that is applied in a positive social environment is critical to the overall success of large and complex projects and programs. However, a comprehensive and up-to-date literature review revealed a lack of a data-driven and consistent rating system that can gauge the maturity and the environment surrounding EVMS implementation. Therefore, the primary objective of this dissertation focuses on the EVMS maturity and environment, and investigates their impact on project performance. The author was one of the 41 research team members whose goal was to develop the novel rating system called Integrated Project/Program Management (IP2M) Maturity and Environment Total Risk Rating (METRR). Using a multi-method research approach, the rating system was developed based on a literature review of more than 600 references, a survey with 294 responses, focus group meetings, and research charrettes with more than 100 subject matter experts from the industry. Performance data from 35 completed projects and programs representing over $21.8 billion in total cost was collected and analyzed. The data analysis showed that the projects with high EVMS maturity and good EVMS environment outperformed those with low maturity and poor environment in key project performance measures. The contributions of this work includes: (1) developing definitions for EVM, EVMS and other research related terms, (2) determining the gaps in the EVMS literature, (3) determining the EVMS state of the practice in the industry, (4) developing a scalable rating system to measure the EVMS maturity and environment, (5) providing quantified evidence on the impact of EVMS maturity and environment on project performance, and (6) providing guidance to practitioners to gauge their EVMS maturity and environment for an enhanced project and program management integration and performance.
ContributorsAramali, Vartenie Mardiros (Author) / Gibson Jr., George Edward (Thesis advisor) / El Asmar, Mounir (Committee member) / Parrish, Kristen (Committee member) / Arizona State University (Publisher)
Created2022
Description
Dubai has emerged as an important center for international business attracting significant inflows of the foreign workforce. Dubai’s population is unique as nationals represent only 15% of the total population, with 200 other nationalities comprising the other 85%. Thus, Cultural Diversity is unavoidable. Cultural Diversity refers to cultural heterogeneity such as differences in race, ethnicity, language, nationality, and religion. As it is a characteristic of Culturally Heterogeneous Workgroups (CHWs), cultural diversity affects how they interact with each other. Since the core concepts of leadership are dealing, inspiring, and motivating teams, the team member’s diversity directly connects with the leadership concept.While many researchers argue whether (CHWs) suffer or benefit from cultural diversity, it is agreed that such diversity has its challenges. Diverse workgroups have been shown to suffer from poor cohesion and social integration. People who are different from their co-worker’s report feeling uneasy and having less organizational commitment. Miscommunication, the development of obstacles, and improper adaptation behaviors are all possible negative impacts.
In the absence of local studies on how cultural diversity is related to leadership, this thesis questioned the connection between cultural diversity and leadership level through a quantitative research approach. This would help understand how different leaders at different levels perceive cultural diversity challenges, which would help focus on specific level(s) in future and research practical ways to address cultural diversity issues of cultural diversity. Measurement scales for leadership levels and cultural diversity challenges were developed. A survey was used to collect data from skilled workers in the construction industry in Dubai, and non-parametric statistical methods were used to analyze the collected data and answer the research question. Whereas a strong correlation was initially expected between work experience, whether in total or within UAE, and leadership level, this was not the case. Most importantly, no significant evidence was found to support a relationship between cultural diversity challenges and both participants’ leadership level and their UAE work experience.
ContributorsSalama, Anas (Author) / Ariaratnam, Samuel (Thesis advisor) / El Asmar, Mounir (Committee member) / Czerniawski, Thomas (Committee member) / Arizona State University (Publisher)
Created2022
Description
As the construction industry in Saudi Arabia was on its way to thriving again. Their growth was due to the unprecedented volume of planned projects such as large-scale and unique projects. Suddenly, the world was faced with one of the most disrupting events in the last century which had a devastating impact on the construction industry specifically. This paper explores mainly the impact of the COVID-19 pandemic on construction projects in Saudi Arabia. Particularly, this paper explores how the pandemic and its related events contributed to the projects' schedule disturbances. This is because most of the projects rely on manpower and supply chains which were heavily disrupted due to the protective measures. For that, a study was conducted to evaluate the impact on the construction projects in Saudi Arabia, to what extent the schedule projects were affected, and what were the main reasons for the schedule delays. The research relied on a field survey and schedule analysis for 12 projects which resulted in identifying several causes of delays and the delayed durations that the projects in Saudi Arabia were facing. This research allows those in construction fields to identify the main causes of delays in order to avoid or minimize the impact of these issues on future projects.
ContributorsObeid, Muhammad Hasan Hani (Author) / Ariaratnam, Samuel (Thesis advisor) / El Asmar, Mounir (Committee member) / Chong, Oswald (Committee member) / Arizona State University (Publisher)
Created2021
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
Description
National Airspace Systems (NAS) are complex cyber-physical systems that require swift air traffic management (ATM) to ensure flight safety and efficiency. With the surging demand for air travel and the increasing intricacy of aviation systems, the need for advanced technologies to support air traffic management and air traffic control (ATC) service has become more crucial than ever. Data-driven models or artificial intelligence (AI) have been conceptually investigated by various parties and shown immense potential, especially when provided with a vast volume of real-world data. These data include traffic information, weather contours, operational reports, terrain information, flight procedures, and aviation regulations. Data-driven models learn from historical experiences and observations and provide expeditious recommendations and decision support for various operation tasks, directly contributing to the digital transformation in aviation. This dissertation reports several research studies covering different aspects of air traffic management and ATC service utilizing data-driven modeling, which are validated using real-world big data (flight tracks, flight events, convective weather, workload probes). These studies encompass a range of topics, including trajectory recommendations, weather studies, landing operations, and aviation human factors. Specifically, the topics explored are (i) trajectory recommendations under weather conditions, which examine the impact of convective weather on last on-file flight plans and provide calibrated trajectories based on convective weather; (ii) multi-aircraft trajectory predictions, which study the intention of multiple mid-air aircraft in the near-terminal airspace and provide trajectory predictions; (iii) flight scheduling operations, which involve probabilistic machine learning-enhanced optimization algorithms for robust and efficient aircraft landing sequencing; (iv) aviation human factors, which predict air traffic controller workload level from flight traffic data with conformalized graph neural network. The uncertainties associated with these studies are given special attention and addressed through Bayesian/probabilistic machine learning. Finally, discussions on high-level AI-enabled ATM research directions are provided, hoping to extend the proposed studies in the future. This dissertation demonstrates that data-driven modeling has great potential for aviation digital twins, revolutionizing the aviation decision-making process and enhancing the safety and efficiency of ATM. Moreover, these research directions are not merely add-ons to existing aviation practices but also contribute to the future of transportation, particularly in the development of autonomous systems.
ContributorsPang, Yutian (Author) / Liu, Yongming (Thesis advisor) / Yan, Hao (Committee member) / Zhuang, Houlong (Committee member) / Marvi, Hamid (Committee member) / Ren, Yi (Committee member) / Arizona State University (Publisher)
Created2023
Description
The design of energy absorbing structures is driven by application specific requirements like the amount of energy to be absorbed, maximum transmitted stress that is permissible, stroke length, and available enclosing space. Cellular structures like foams are commonly leveraged in nature for energy absorption and have also found use in engineering applications. With the possibility of manufacturing complex cellular shapes using additive manufacturing technologies, there is an opportunity to explore new topologies that improve energy absorption performance. This thesis aims to systematically understand the relationships between four key elements: (i) unit cell topology, (ii) material composition, (iii) relative density, and (iv) fields; and energy absorption behavior, and then leverage this understanding to develop, implement and validate a methodology to design the ideal cellular structure energy absorber. After a review of the literature in the domain of additively manufactured cellular materials for energy absorption, results from quasi-static compression of six cellular structures (hexagonal honeycomb, auxetic and Voronoi lattice, and diamond, Gyroid, and Schwarz-P) manufactured out of AlSi10Mg and Nylon-12. These cellular structures were compared to each other in the context of four design-relevant metrics to understand the influence of cell design on the deformation and failure behavior. Three new and revised metrics for energy absorption were proposed to enable more meaningful comparisons and subsequent design selection. Triply Periodic Minimal Surface (TPMS) structures were found to have the most promising overall performance and formed the basis for the numerical investigation of the effect of fields on the energy absorption performance of TPMS structures. A continuum shell-based methodology was developed to analyze the large deformation behavior of field-driven variable thickness TPMS structures and validated against experimental data. A range of analytical and stochastic fields were then evaluated that modified the TPMS structure, some of which were found to be effective in enhancing energy absorption behavior in the structures while retaining the same relative density. Combining findings from studies on the role of cell geometry, composition, relative density, and fields, this thesis concludes with the development of a design framework that can enable the formulation of cellular material energy absorbers with idealized behavior.
ContributorsShinde, Mandar (Author) / Bhate, Dhruv (Thesis advisor) / Peralta, Pedro (Committee member) / Liu, Yongming (Committee member) / Jiao, Yang (Committee member) / Kwon, Beomjin (Committee member) / Arizona State University (Publisher)
Created2023
Description
Buildings continue to take up a significant portion of the global energy consumption, meaning there are significant research opportunities in reducing the energy consumption of the building sector. One widely studied area is waste heat recovery. The purpose of this research is to test a prototype thermogalvanic cell in the form factor of a UK metric brick sized at 215 mm × 102.5 mm × 65 mm for the experimental power output using a copper/copper(II) (Cu/Cu2+) based aqueous electrode. In this study the thermogalvanic brick uses a 0.7 M CuSO4 + 0.1 M H2SO4 aqueous electrolyte with copper electrodes as two of the walls. The other walls of the thermogalvanic brick are made of 5.588 mm (0.22 in) thick acrylic sheet. Internal to the brick, a 0.2 volume fraction minimal surface Schwartz diamond (Schwartz D) structure made of ABS, Polycarbonate-ABS (PCABS), and Polycarbonate-Carbon Fiber (PCCF) was tested to see the effects on the power output of the thermogalvanic brick. By changing the size of the thermogalvanic cell into that of a brick will allow this thermogalvanic cell to become the literal building blocks of green buildings. The thermogalvanic brick was tested by applying a constant power to the strip heater attached to the hot side of the brick, resulting in various ∆T values between 8◦C and 15◦C depending on the material of Schwartz D inside. From this, it was found that a single Cu/Cu2+ thermogalvanic brick containing the PCCF or PCABS Schwartz D performed equivalently well at a 163.8% or 164.9%, respectively, higher normalized power density output than the control brick containing only electrolyte solution.
ContributorsLee, William J. (Author) / Phelan, Patrick (Thesis advisor) / El Asmar, Mounir (Committee member) / Milcarek, Ryan (Committee member) / Arizona State University (Publisher)
Created2018