Ph.D. Tezi Görüntüleme

Student: Erdem TÜRKELİ
Supervisor: Yrd. Doç. Dr. Hasan Tahsin ÖZTÜRK
Department: İnşaat Mühendisliği
Institution: Graduate School of Natural and Applied Sciences
University: Karadeniz Technical University Turkey
Title of the Thesis: OPTIMUM DESIGN OF PARTIALLY PRESTRESSED CONCRETE BEAMS USING ARTIFICIAL BEE COLONY ALGORITHM AND GENETIC ALGORITHMS
Level: Ph.D.
Acceptance Date: 15/4/2016
Number of Pages: 159
Registration Number: Di1135
Summary:

      Generally, its known that partially prestressed concrete technique is more economical than fully prestressed concrete. Nevertheless there are almost no studies and applications on this subject in Turkey. However no optimum design studies have been found.

The main purpose of this study is to perform optimum design of partially prestressed concrete beams from cost point of view by using Artificial Bee Colony and Genetic Algorithms. This study consists of four parts. The first part is the general information section where bridges, technical literature about partially prestressed concrete and structural optimization techniques are focussed on. In the second part of the study; the structural optimization techniques, Artificial Bee Colony and Genetic Algorithms are summarized, with these algorithms, the optimum design of three selected example problems are performed by determining their objective function, design variables and constraints from cost point of view, and the results obtained are compared with the results obtained from traditional design. Third chapter is reserved to the results and recommendations deducted from the whole study. This last chapter is followed by the list of references and the authors biography.

      The interpretation of the results show that the design of partially prestressed concrete I and T crossectioned beams from cost point of view by using Artificial Bee Colony or Genetic Algorithms is much more economical than the traditional ones without conceding safety.

      Key Words: Bridge, Partially Prestressed Concrete, Optimization, Artificial bee colony algorithm, Genetic algorithm