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- All Subjects: Legos
- All Subjects: Micromechanical
- Creators: Civil, Environmental and Sustainable Engineering Programs
- Creators: Ford, Emily Lucile
Description
Engineers spend several years studying intense technical details of the processes that shape our world, yet few are exposed to classes addressing social behaviors or issues. Engineering culture creates specific barriers to addressing social science issues, such as unconscious bias, within engineering classrooms. I developed a curriculum that uses optical illusions, Legos, and the instructor's vulnerability to tackle unconscious bias in a way that addresses the barriers in engineering culture that prevent engineers from learning social science issues. Unconscious bias has documented long-term negative impacts on success and personal development, even in engineering environments. Creating a module in engineering education that addresses unconscious bias with the aim of reducing the negative effects of bias would benefit developing engineers by improving product development and team diversity. Engineering culture fosters disengagement with social issues through three pillars: depoliticization, technical/social dualism, and meritocracy. The developed curriculum uses optical illusions and Legos as proxies to start discussions about unconscious bias. The proxies allow engineers to explore their own biases without running into one of the pillars of disengagement that limits the engineer's willingness to discuss social issues. The curriculum was implemented in the Fall of 2017 in an upper-division engineering classroom as a professional communication module. The module received qualitatively positive feedback from fellow instructors and students. The curriculum was only implemented once by the author, but future implementations should be done with a different instructor and using quantitative data to measure if the learning objectives were achieved. Appendix A of the paper contains a lesson plan of the module that could be implemented by other instructors.
ContributorsLauber, Emily Anne (Author) / Wernimont, Jacqueline (Thesis director) / Mertz, Benjamin (Committee member) / Civil, Environmental and Sustainable Engineering Programs (Contributor) / College of Integrative Sciences and Arts (Contributor) / Barrett, The Honors College (Contributor)
Created2017-05
Description
Productivity in the construction industry is an essential measure of production efficiency and economic progress, quantified by craft laborers' time spent directly adding value to a project. In order to better understand craft labor productivity as an aspect of lean construction, an activity analysis was conducted at the Arizona State University Palo Verde Main engineering dormitory construction site in December of 2016. The objective of this analysis on craft labor productivity in construction projects was to gather data regarding the efficiency of craft labor workers, make conclusions about the effects of time of day and other site-specific factors on labor productivity, as well as suggest improvements to implement in the construction process. Analysis suggests that supporting tasks, such as traveling or materials handling, constitute the majority of craft labors' efforts on the job site with the highest percentages occurring at the beginning and end of the work day. Direct work and delays were approximately equal at about 20% each hour with the highest peak occurring at lunchtime between 10:00 am and 11:00 am. The top suggestion to improve construction productivity would be to perform an extensive site utilization analysis due to the confined nature of this job site. Despite the limitations of an activity analysis to provide a complete prospective of all the factors that can affect craft labor productivity as well as the small number of days of data acquisition, this analysis provides a basic overview of the productivity at the Palo Verde Main construction site. Through this research, construction managers can more effectively generate site plans and schedules to increase labor productivity.
ContributorsFord, Emily Lucile (Author) / Grau, David (Thesis director) / Chong, Oswald (Committee member) / Civil, Environmental and Sustainable Engineering Programs (Contributor) / School of International Letters and Cultures (Contributor) / Barrett, The Honors College (Contributor)
Created2016-12
Description
This paper features analysis of interdisciplinary collaboration, based on the results from the Kolbe A™ Index of students in the Nano Ethics at Play (NEAP) class, a four week course in Spring 2015. The Kolbe A™ is a system which describes the Conative Strengths of each student, or their natural drive and instinct. NEAP utilized the LEGO® SERIOUS PLAY® (LSP) method, which uses abstract LEGO models to describe answers to a proposed question in school or work environments. The models could be described piece by piece to provide clear explanations without allowing disciplinary jargon, which is why the class contained students from eleven different majors (Engineering (Civil, Biomedical, & Electrical), Business (Marketing & Supply Chain Management), Architectural Studies, Sustainability, Anthropology, Communications, Philosophy, & Psychology).
The proposed hypotheses was based on the four different Kolbe A™ strengths, or Action Modes: Fact Finder, Follow Through, Quick Start, and Implementor. Hypotheses were made about class participation and official class twitter use, using #ASUsp, for each Kolbe type. The results proved these hypotheses incorrect, indicating a lack of correlation between Kolbe A™ types and playing. The report also includes qualitative results such as Twitter Keywords and a Sentiment calculation for each week of the course. The class had many positive outcomes, including growth in the ability to collaborate by students, further understanding of how to integrate Twitter use into the classroom, and more knowledge about the effectiveness of LSP.
The proposed hypotheses was based on the four different Kolbe A™ strengths, or Action Modes: Fact Finder, Follow Through, Quick Start, and Implementor. Hypotheses were made about class participation and official class twitter use, using #ASUsp, for each Kolbe type. The results proved these hypotheses incorrect, indicating a lack of correlation between Kolbe A™ types and playing. The report also includes qualitative results such as Twitter Keywords and a Sentiment calculation for each week of the course. The class had many positive outcomes, including growth in the ability to collaborate by students, further understanding of how to integrate Twitter use into the classroom, and more knowledge about the effectiveness of LSP.
ContributorsStevens, Jenna Marie (Author) / Seager, Thomas (Thesis director) / Jenses, Camilla (Committee member) / Civil, Environmental and Sustainable Engineering Programs (Contributor) / Herberger Institute for Design and the Arts (Contributor) / Barrett, The Honors College (Contributor)
Created2015-12
Description
Ultra High Performance (UHP) cementitious binders are a class of cement-based materials with high strength and ductility, designed for use in precast bridge connections, bridge superstructures, high load-bearing structural members like columns, and in structural repair and strengthening. This dissertation aims to elucidate the chemo-mechanical relationships in complex UHP binders to facilitate better microstructure-based design of these materials and develop machine learning (ML) models to predict their scale-relevant properties from microstructural information.To establish the connection between micromechanical properties and constitutive materials, nanoindentation and scanning electron microscopy experiments are performed on several cementitious pastes. Following Bayesian statistical clustering, mixed reaction products with scattered nanomechanical properties are observed, attributable to the low degree of reaction of the constituent particles, enhanced particle packing, and very low water-to-binder ratio of UHP binders. Relating the phase chemistry to the micromechanical properties, the chemical intensity ratios of Ca/Si and Al/Si are found to be important parameters influencing the incorporation of Al into the C-S-H gel.
ML algorithms for classification of cementitious phases are found to require only the intensities of Ca, Si, and Al as inputs to generate accurate predictions for more homogeneous cement pastes. When applied to more complex UHP systems, the overlapping chemical intensities in the three dominant phases – Ultra High Stiffness (UHS), unreacted cementitious replacements, and clinker – led to ML models misidentifying these three phases. Similarly, a reduced amount of data available on the hard and stiff UHS phases prevents accurate ML regression predictions of the microstructural phase stiffness using only chemical information. The use of generic virtual two-phase microstructures coupled with finite element analysis is also adopted to train MLs to predict composite mechanical properties. This approach applied to three different representations of composite materials produces accurate predictions, thus providing an avenue for image-based microstructural characterization of multi-phase composites such UHP binders. This thesis provides insights into the microstructure of the complex, heterogeneous UHP binders and the utilization of big-data methods such as ML to predict their properties. These results are expected to provide means for rational, first-principles design of UHP mixtures.
ML algorithms for classification of cementitious phases are found to require only the intensities of Ca, Si, and Al as inputs to generate accurate predictions for more homogeneous cement pastes. When applied to more complex UHP systems, the overlapping chemical intensities in the three dominant phases – Ultra High Stiffness (UHS), unreacted cementitious replacements, and clinker – led to ML models misidentifying these three phases. Similarly, a reduced amount of data available on the hard and stiff UHS phases prevents accurate ML regression predictions of the microstructural phase stiffness using only chemical information. The use of generic virtual two-phase microstructures coupled with finite element analysis is also adopted to train MLs to predict composite mechanical properties. This approach applied to three different representations of composite materials produces accurate predictions, thus providing an avenue for image-based microstructural characterization of multi-phase composites such UHP binders. This thesis provides insights into the microstructure of the complex, heterogeneous UHP binders and the utilization of big-data methods such as ML to predict their properties. These results are expected to provide means for rational, first-principles design of UHP mixtures.
ContributorsFord, Emily Lucile (Author) / Neithalath, Narayanan (Thesis advisor) / Rajan, Subramaniam D. (Committee member) / Mobasher, Barzin (Committee member) / Chawla, Nikhilesh (Committee member) / Hoover, Christian G. (Committee member) / Maneparambil, Kailas (Committee member) / Arizona State University (Publisher)
Created2020