Teaching and Learning Based Optimum Design Algorithm for Steel Grillage Systems to LRFD-AISC


AKIN A., AYDOĞDU İ.

ACE 2014 11th INTERNATIONAL CONGRESS ON ADVANCES IN CIVIL ENGINEERING, İstanbul, Turkey, 21 - 25 October 2014, pp.1-6

  • Publication Type: Conference Paper / Full Text
  • City: İstanbul
  • Country: Turkey
  • Page Numbers: pp.1-6
  • Akdeniz University Affiliated: Yes

Abstract

In this study, Teaching and Learning Based Optimization (TLBO) algorithm, a new member of meta-heuristics optimization technique, is presented for optimum design of grillage systems which is one of the most commonly used structures to cover large areas such as bridge decks, building floors and space structures, etc. TLBO algorithm is inspired by the interaction and outcome of the teacher and learners and this method has been built on the effect of the influence and guidance of a teacher on the output of learners (students) in a class. The method has two complementary components which are the teaching and learning phases. The presented algorithm and produced optimum designs consider and satisfy the serviceability, ductility, durability, ultimate strength constraints and other constraints implemented from LRFD-AISC related to the good design and detailing practice. The cross-sectional properties of transverse and longitudinal beams of the grillage system are considered as the design variables and appropriate W-sections are selected to satisfy the design limitations described in LRFD-AISC and minimize the weight of the grillage system from 272 discrete W-section designations given in LRFD-AISC. Many design examples are illustrated to show the robustness and efficiency of the new algorithm presented and the obtained results are presented in comparison with results obtained from previous studies. It is concluded that the proposed optimum design algorithm yields rational, reliable and economical designs and these types of optimization applications can be implemented easily by design engineers in the industry.


Keywords: optimum structural design, teaching and learning based optimization, heuristic optimization algorithm, minimum weight, search technique.