ETDs

This collection includes most of the ASU Theses and Dissertations from 2011 to present. ASU Theses and Dissertations are available in downloadable PDF format; however, a small percentage of items are under embargo. Information about the dissertations/theses includes degree information, committee members, an abstract, supporting data or media.

In addition to the electronic theses found in the ASU Digital Repository, ASU Theses and Dissertations can be found in the ASU Library Catalog.

Dissertations and Theses granted by Arizona State University are archived and made available through a joint effort of the ASU Graduate College and the ASU Libraries. For more information or questions about this collection contact or visit the Digital Repository ETD Library Guide or contact the ASU Graduate College at gradformat@asu.edu.

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Preliminary structural design optimization of tall buildings using GS-USA Frame3D

Description
Tall buildings are spreading across the globe at an ever-increasing rate (www.ctbuh.org). The global number of buildings 200m or more in height has risen from 286 to 602 in the last decade alone. The increasing complexity of building architecture poses unique challenges in the structural design of modern tall buildings.…

Tall buildings are spreading across the globe at an ever-increasing rate (www.ctbuh.org). The global number of buildings 200m or more in height has risen from 286 to 602 in the last decade alone. The increasing complexity of building architecture poses unique challenges in the structural design of modern tall buildings. Hence, innovative structural systems need to be evaluated to create an economical design that satisfies multiple design criteria. Design using traditional trial-and-error approach can be extremely time-consuming and the resultant design uneconomical. Thus, there is a need for an efficient numerical optimization tool that can explore and generate several design alternatives in the preliminary design phase which can lead to a more desirable final design. In this study, we present the details of a tool that can be very useful in preliminary design optimization - finite element modeling, design optimization, translating design code requirements into components of the FE and design optimization models, and pre-and post-processing to verify the veracity of the model. Emphasis is placed on development and deployment of various FE models (static, modal and dynamic analyses; linear, beam and plate/shell finite elements), design optimization problem formulation (sizing, shape, topology and material selection optimization) and numerical optimization tools (gradient-based and evolutionary optimization methods) [Rajan, 2001]. The design optimization results of full scale three dimensional buildings subject to multiple design criteria including stress, serviceability and dynamic response are discussed.
ContributorsSirigiri, Mamatha (Author) / Rajan, Subramaniam D. (Thesis advisor) / Neithalath, Narayanan (Committee member) / Mobasher, Barzin (Committee member) / Arizona State University (Publisher)
Created2014
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A new genetic algorithm for continuous structural optimization

Description
In this thesis, the author described a new genetic algorithm based on the idea: the better design could be found at the neighbor of the current best design. The details of the new genetic algorithm are described, including the rebuilding process from Micro-genetic algorithm and the different crossover and mutation…

In this thesis, the author described a new genetic algorithm based on the idea: the better design could be found at the neighbor of the current best design. The details of the new genetic algorithm are described, including the rebuilding process from Micro-genetic algorithm and the different crossover and mutation formation.

Some popular examples, including two variable function optimization and simple truss models are used to test this algorithm. In these study, the new genetic algorithm is proved able to find the optimized results like other algorithms.

Besides, the author also tried to build one more complex truss model. After tests, the new genetic algorithm can produce a good and reasonable optimized result. Form the results, the rebuilding, crossover and mutation can the jobs as designed.

At last, the author also discussed two possible points to improve this new genetic algorithm: the population size and the algorithm flexibility. The simple result of 2D finite element optimization showed that the effectiveness could be better, with the improvement of these two points.
ContributorsDing, Xiaosu (Author) / Hjelmstad, Keith (Thesis advisor) / Neithalath, Narayanan (Committee member) / Rajan, Subramaniam D. (Committee member) / Arizona State University (Publisher)
Created2015