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Supplemental Lecture Videos to Aide in the Transition from a Traditional Learning Environment to an Engaged Learning Environment

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Teaching methods in the present day are beginning to transition from the traditional lecture style to the flipped learning style. The flipped classroom, also known as an engaged learning classroom, follows the model where students are presented with lecture material

Teaching methods in the present day are beginning to transition from the traditional lecture style to the flipped learning style. The flipped classroom, also known as an engaged learning classroom, follows the model where students are presented with lecture material prior to attending class. Instead of being lectured in class, they work on applications of the material with the help of their peers and the instructional staff. One component that many engaged learning environments have in common is lecture videos for the students to view prior to attending class. An undergraduate civil engineering course at Arizona State University is modeled using an engaged learning environment; however, it does not provide lecture videos for the students. Many students in this course are seeing an engaged learning environment for the first time and need guidance on how to prepare for the course, how to approach course material, and how to interpret feedback, in addition to getting help in the technical concepts. This project aims to create supplemental lecture videos based on the concepts that students in the class identified as needing more information, as well as topics that will help students make this transition to an engaged learning environment. A series of sixteen videos were created and posted for the students to view prior to attending recitation periods. The feedback from the students regarding the videos was studied and implementation techniques for future semesters were tested.

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Date Created
2015-12

Development and Use of Instructional Multimedia to Enhance Student Comprehension of Fundamental Structural Analysis and Design Techniques

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In the Spring 2013 and Fall 2013 semesters, a survey was taken of students enrolled in the principal undergraduate civil engineering structures course, CEE 321: Structural Analysis and Design, to assess both the prevalence of technology in the lives of

In the Spring 2013 and Fall 2013 semesters, a survey was taken of students enrolled in the principal undergraduate civil engineering structures course, CEE 321: Structural Analysis and Design, to assess both the prevalence of technology in the lives of the students and the potential ways this information could be use to improve the educational experience. The results of this survey indicated that there was a considerable demand for additional online resources outside of the formal classroom. The students of CEE 321 requested online lecture videos in particular, and so a project was launched at the start of the Spring 2014 semester to deliver a large body of academic instructional videos. In total, a collection of 30 instructional videos which covered all key topics covered over a semester of CEE 321 was published. The driving interest behind this creative project is to increase the level of understanding, comfort, and performance in students enrolled in the class. Although the quantity of initial student feedback is relatively small, the reactions are distinctly positive and reflect an improvement in understanding amongst the responding students. Over the course of upcoming semesters, qualitative and quantitative assessments of the impact of the videos are expected to provide a better indication of their quality and effectiveness in supporting student comprehension and performance in CEE 321. Above all, the success of these videos is directly tied to their ability to function as living, adaptable resources which are continuously molded and improved by student feedback.

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Date Created
2014-05

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An Analysis of Craft Labor Productivity

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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

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.

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Date Created
2016-12

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Experimental Investigations and Machine Learning-Based Predictive Modeling of the Chemo-mechanical Characteristics of Ultra-High Performance Binders

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

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.

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Date Created
2020