Date of Award


Document Type


Degree Name

Doctor of Philosophy (PhD)


Civil Engineering

Committee Member

Dr. Nadarajah Ravichandran, Committee Chair

Committee Member

Dr. C. Hsein Juang

Committee Member

Dr. Abdul Khan

Committee Member

Dr. Weichiang Pang


This dissertation presents a robust geotechnical design optimization framework for retaining walls with sand backfill and lightweight shredded tire backfill subjected to earthquake load, and I-wall levee systems supported by sand foundation and clay foundation subjected to flood. The responses of retaining walls and levee systems are highly uncertain especially when subjected to natural disasters such as earthquake and flooding. The variations in the response of these systems are caused by the uncertainties associated with not only the soil properties, but also the loads induced by earthquake and flood. These critical systems must show satisfactory performance under these uncertainties because their failure may result in loss of life and property as noted in the past events. Therefore, in this study, the uncertainties in engineering properties of soils (backfill in retaining walls, levee fill and foundation in I-wall levee systems) were considered systematically along with the uncertainty in the external loads (earthquake in retaining walls and flooding in I-wall levee systems). The key design variables of these two systems were determined and based on their ranges several design cases were generated. Fully coupled finite element analyses were performed for computing responses of concern accurately, and appropriate response surfaces were developed for the respective responses of concern. Using the response surface and via a genetic algorithm code, the designs of these systems were optimized to cost and robustness while satisfying the safety constraints. Sets of preferred designs, known as Pareto fronts, were captured through the bi-objective robust optimizations that can be used as a decision-making tool for selecting the suitable design in engineering practice.



To view the content in your browser, please download Adobe Reader or, alternately,
you may Download the file to your hard drive.

NOTE: The latest versions of Adobe Reader do not support viewing PDF files within Firefox on Mac OS and if you are using a modern (Intel) Mac, there is no official plugin for viewing PDF files within the browser window.