ASU Electronic Theses and Dissertations
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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- All Subjects: Civil Engineering
- Creators: Hjelmstad, Keith
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.
A considerable amount of studies have been conducted for the past four decades. Most researchers have used constraints and tried to minimize the cost of the structure by reducing the weight of it [8]. Although this approach may be true for steel structures, it is not accurate for composite structures such as reinforced and prestressed concrete. Maximizing the amount of reinforcing steel to minimize the weight of the overall structure can produce an increase of the cost if the price of steel is too high compared to concrete [8]. A better approach is to reduce the total cost of the structure instead of weight. However, some structures such as Prestressed Concrete AASHTO Girders have been standardized with the purpose of simplifying production, design and construction. Optimizing a bridge girder requires good judgment at an early stage of the design and some studies have provided guides for preliminary design that will generate a final economical solution [17] [18]. Therefore, no calculations or optimization procedure is required to select the appropriate Standard AASHTO Girder. This simplifies the optimization problem of a bridge girder to reducing the amount of prestressing and mild steel only. This study will address the problem of optimizing the prestressing force of a PC AASHTO girder by using linear programming and feasibility domain of working stresses. A computer program will be presented to apply the optimization technique effectively.
Previous studies started collecting detailed geometric data generated by 3D laser scanners for defect detection and geometric change analysis of structures. However, previous studies have not yet systematically examined methods for exploring the correlation between the detected geometric changes and their relation to the behaviors of the structural system. Manually checking every possible loading combination leading to the observed geometric change is tedious and sometimes error-prone. The work presented in this dissertation develops a spatial change analysis framework that utilizes spatiotemporal data collected using 3D laser scanning technology and the as-designed models of the structures to automatically detect, classify, and correlate the spatial changes of a structure. The change detection part of the developed framework is computationally efficient and can automatically detect spatial changes between as-designed model and as-built data or between two sets of as-built data collected using 3D laser scanning technology. Then a spatial change classification algorithm automatically classifies the detected spatial changes as global (rigid body motion) and local deformations (tension, compression). Finally, a change correlation technique utilizes a qualitative shape-based reasoning approach for identifying correlated deformations of structure elements connected at joints that contradicts the joint equilibrium. Those contradicting deformations can help to eliminate improbable loading combinations therefore guiding the loading path analysis of the structure.