Matching Items (3)
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- All Subjects: Micromechanical
- Creators: Ford, Emily Lucile
- Creators: Civil, Environmental and Sustainable Engineering Programs
- Creators: Mayer, Carl Randolph
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
Nanolaminate composite materials consist of alternating layers of materials at the nanoscale (≤100 nm). Due to the nanometer scale thickness of their layers, these materials display unique and tailorable properties. This enables us to alter both mechanical attributes such as strength and wear properties, as well as functional characteristics such as biocompatibility, optical, and electronic properties. This dissertation focuses on understanding the mechanical behavior of the Al-SiC system. From a practical perspective, these materials exhibit a combination of high toughness and strength which is attractive for many applications. Scientifically, these materials are interesting due to the large elastic modulus mismatch between the layers. This, paired with the small layer thickness, allows a unique opportunity for scientists to study the plastic deformation of metals under extreme amounts of constraint.
Previous studies are limited in scope and a more diverse range of mechanical characterization is required to understand both the advantages and limitations of these materials. One of the major challenges with testing these materials is that they are only able to be made in thicknesses on the order of micrometers so the testing methods are limited to small volume techniques. This work makes use of both microscale testing techniques from the literature as well as novel methodologies. Using these techniques we are able to gain insight into aspects of the material’s mechanical behavior such as the effects of layer orientation, flaw dependent fracture, tension-compression asymmetry, fracture toughness as a function of layer thickness, and shear behavior as a function of layer thickness.
Previous studies are limited in scope and a more diverse range of mechanical characterization is required to understand both the advantages and limitations of these materials. One of the major challenges with testing these materials is that they are only able to be made in thicknesses on the order of micrometers so the testing methods are limited to small volume techniques. This work makes use of both microscale testing techniques from the literature as well as novel methodologies. Using these techniques we are able to gain insight into aspects of the material’s mechanical behavior such as the effects of layer orientation, flaw dependent fracture, tension-compression asymmetry, fracture toughness as a function of layer thickness, and shear behavior as a function of layer thickness.
ContributorsMayer, Carl Randolph (Author) / Chawla, Nikhilesh (Thesis advisor) / Jiang, Hanqing (Committee member) / Molina-Aldareguia, Jon (Committee member) / Rajagopalan, Jagannathan (Committee member) / Arizona State University (Publisher)
Created2016
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