Masters Degrees (Civil Engineering)

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Browsing Masters Degrees (Civil Engineering) by Author "Appelo, Sophia Aletta"

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    Structural optimisation via genetic algorithms
    (Stellenbosch : Stellenbosch University, 2012-12) Appelo, Sophia Aletta; Van der Klashorst, Etienne; Stellenbosch University. Faculty of Engineering. Dept. of Civil Engineering.
    ENGLISH ABSTRACT: The design of steel structures needs to incorporate some optimisation procedure that evolves the initial design into a more economic nal design, where this nal design must still satisfy all the initial design criteria. A candidate optimisation technique suggested by this research is the genetic algorithm. The genetic algorithm (GA) is an optimisation technique that was inspired by evolutionary principles, such as the survival of the ttest (also known as natural selection). The GA operates by generating a population of individuals which 'compete' with one another in order to survive, or di erently stated, in order to make it into the next generation. Each individual presents a solution to the problem. Surviving solutions which propagate through to the next generation are typically 'better' or ' tter' than the ones that had died o , hence suggesting a process of optimisation. This process continues until a de ned convergence criteria is met (e.g. speci ed maximum number of generations is reached), where after the best individual in the population serves as the ultimate solution to the problem. This study thoroughly investigates the inner workings that drive the algorithm, after which an algorithm is presented to face the challenges of structural optimisation. This algorithm will be concerned only with sizing optimisation; geometry, topology and shape optimisation is outside the scope of this research. The objective of this optimising problem will be to minimise the weight of the structure, it is assumed that the weight is inversely propotional to the cost of the structure. The motive behind using a genetic algorithm in this study is largely due to its ability to handle discrete search spaces; classical search methods are typically limited to some form of gradient search technique for which the search space must be continuous. The algorithm is also preferred due to its ability to e ciently search through vast search spaces, which is typically the case for a structural optimisation problem. The genetic algorithm's performance will be examined through the use of bench-marking problems. Benchmarking is done for both planar and space trusses; the 10 - and 25 bar truss problems. Such problems are typically analysed with stress and displacement constraints. After the performance of the algorithm is validated, the study commences towards solving real life practical problems. The rst step towards solving such problems would be to investigate the 160 bar truss benchmarking problem. This problem will be slightly adapted by applying South African design standards to the design, SANS (2005). This approach is more realistic, when compared to simply specifying stress and displacement constraints due to the fact that an element cannot simply be assigned the same stress constraint for tension and compression; slenderness and buckling e ects need to be taken into account. For this case, the search space will no longer simply be some sample search space, but will consist of real sections taken from the Southern African Steel Construction Handbook, SAISC (2008). Finally, the research will investigate what is needed to optimise a proper real life structure, the Eskom Self-Supporting Suspension 518H Tower. It will address a wide variety of topics, such as modelling the structure as realistically as possible, to investigating key aspects that might make the problem di erent from standard benchmarking problems and what kind of steps can be taken to over-come possible issues and errors. The algorithm runs in parallel with a nite element method program, provided by Dr G.C. van Rooyen, which analyses the solutions obtained from the algorithm and ensures structural feasibility.