Matching Items (3)
Description
The ability to design high performance buildings has acquired great importance in recent years due to numerous federal, societal and environmental initiatives. However, this endeavor is much more demanding in terms of designer expertise and time. It requires a whole new level of synergy between automated performance prediction with the human capabilities to perceive, evaluate and ultimately select a suitable solution. While performance prediction can be highly automated through the use of computers, performance evaluation cannot, unless it is with respect to a single criterion. The need to address multi-criteria requirements makes it more valuable for a designer to know the "latitude" or "degrees of freedom" he has in changing certain design variables while achieving preset criteria such as energy performance, life cycle cost, environmental impacts etc. This requirement can be met by a decision support framework based on near-optimal "satisficing" as opposed to purely optimal decision making techniques. Currently, such a comprehensive design framework is lacking, which is the basis for undertaking this research. The primary objective of this research is to facilitate a complementary relationship between designers and computers for Multi-Criterion Decision Making (MCDM) during high performance building design. It is based on the application of Monte Carlo approaches to create a database of solutions using deterministic whole building energy simulations, along with data mining methods to rank variable importance and reduce the multi-dimensionality of the problem. A novel interactive visualization approach is then proposed which uses regression based models to create dynamic interplays of how varying these important variables affect the multiple criteria, while providing a visual range or band of variation of the different design parameters. The MCDM process has been incorporated into an alternative methodology for high performance building design referred to as Visual Analytics based Decision Support Methodology [VADSM]. VADSM is envisioned to be most useful during the conceptual and early design performance modeling stages by providing a set of potential solutions that can be analyzed further for final design selection. The proposed methodology can be used for new building design synthesis as well as evaluation of retrofits and operational deficiencies in existing buildings.
ContributorsDutta, Ranojoy (Author) / Reddy, T Agami (Thesis advisor) / Runger, George C. (Committee member) / Addison, Marlin S. (Committee member) / Arizona State University (Publisher)
Created2013
Description
Coarse Grain Reconfigurable Arrays (CGRAs) are promising accelerators capable of
achieving high performance at low power consumption. While CGRAs can efficiently
accelerate loop kernels, accelerating loops with control flow (loops with if-then-else
structures) is quite challenging. Techniques that handle control flow execution in
CGRAs generally use predication. Such techniques execute both branches of an
if-then-else structure and select outcome of either branch to commit based on the
result of the conditional. This results in poor utilization of CGRA s computational
resources. Dual-issue scheme which is the state of the art technique for control flow
fetches instructions from both paths of the branch and selects one to execute at
runtime based on the result of the conditional. This technique has an overhead in
instruction fetch bandwidth. In this thesis, to improve performance of control flow
execution in CGRAs, I propose a solution in which the result of the conditional
expression that decides the branch outcome is communicated to the instruction fetch
unit to selectively issue instructions from the path taken by the branch at run time.
Experimental results show that my solution can achieve 34.6% better performance
and 52.1% improvement in energy efficiency on an average compared to state of the
art dual issue scheme without imposing any overhead in instruction fetch bandwidth.
achieving high performance at low power consumption. While CGRAs can efficiently
accelerate loop kernels, accelerating loops with control flow (loops with if-then-else
structures) is quite challenging. Techniques that handle control flow execution in
CGRAs generally use predication. Such techniques execute both branches of an
if-then-else structure and select outcome of either branch to commit based on the
result of the conditional. This results in poor utilization of CGRA s computational
resources. Dual-issue scheme which is the state of the art technique for control flow
fetches instructions from both paths of the branch and selects one to execute at
runtime based on the result of the conditional. This technique has an overhead in
instruction fetch bandwidth. In this thesis, to improve performance of control flow
execution in CGRAs, I propose a solution in which the result of the conditional
expression that decides the branch outcome is communicated to the instruction fetch
unit to selectively issue instructions from the path taken by the branch at run time.
Experimental results show that my solution can achieve 34.6% better performance
and 52.1% improvement in energy efficiency on an average compared to state of the
art dual issue scheme without imposing any overhead in instruction fetch bandwidth.
ContributorsRajendran Radhika, Shri Hari (Author) / Shrivastava, Aviral (Thesis advisor) / Christen, Jennifer Blain (Committee member) / Cao, Yu (Committee member) / Arizona State University (Publisher)
Created2014
Description
The construction industry faces important performance problems such as low productivity, poor quality of work, and work-related accidents and injuries. Creating a high reliability work system that is simultaneously highly productive and exceptionally safe has become a challenge for construction practitioners and scholars. The main goal of this dissertation was to create an understanding of high reliability construction work systems based on lessons from the production practices of high performance work crews. High performance work crews are defined as the work crews that constantly reach and maintain a high level of productivity and exceptional safety record while delivering high quality of work. This study was conceptualized on findings from High Reliability Organizations and with a primary focus on lean construction, human factors, safety, and error management. Toward the research objective, this dissertation answered two major questions. First, it explored the task factors and project attributes that shape and increase workers' task demands and consequently affect workers' safety, production, and quality performance. Second, it explored and investigated the production practices of construction field supervisors (foremen) to understand how successful supervisors regulate task and project demands to create a highly reliable work process. Employing case study methodology, this study explored and analyzed the work practices of six work crews and crew supervisors in different trades including concrete, masonry, and hot asphalt roofing construction. The case studies included one exceptional and one average performing crew from each trade. Four major factors were considered in the selection of exceptional crew supervisors: (1) safety performance, (2) production performance, (3) quality performance, and (4) the level of project difficulty they supervised. The data collection was carried out in three phases including: (1) interview with field supervisors to understand their production practices, (2) survey and interview with workers to understand their perception and to identify the major sources of task demands, and (3) several close field observations. Each trade's specific findings including task demands, project attributes, and production practices used by crew supervisors are presented in a separate chapter. At the end the production practices that converged to create high reliability work systems are summarized and presented in nine major categories.
ContributorsMemarian, Babak (Author) / Bashford, Howard (Thesis advisor) / Boren, Rebecca (Committee member) / Wiezel, Avi (Committee member) / Arizona State University (Publisher)
Created2012