- author = "Michael Fenton and Ciaran McNally and Jonathan Byrne and Erik Hemberg and James McDermott and Michael O'Neill",
- title = "Automatic innovative truss design using grammatical evolution",
- journal = "Automation in Construction",
- year = "2014",
- volume = "39",
- pages = "59--69",
- note = "Bronze Humie winner",
- keywords = "genetic algorithms, genetic programming, Grammatical evolution, Structural optimisation, Evolutionary computation, Truss design, Computer aided design",
- ISSN = "0926-5805",
-
URL = "
http://www.sciencedirect.com/science/article/pii/S0926580513002124",
-
URL = "
http://www.human-competitive.org/sites/default/files/fenton-paper-from-2014-for-background.pdf",
-
URL = "http://www.human-competitive.org/sites/default/files/fenton-text.txt",
-
DOI = "
doi:10.1016/j.autcon.2013.11.009",
- size = "11 pages",
-
abstract = "Truss optimization in the field of Structural
Engineering is a growing discipline. The application of
Grammatical Evolution, a grammar-based form of Genetic
Programming (GP), has shown that it is capable of
generating innovative engineering designs. Existing
truss optimization methods in GP focus primarily on
optimizing global topology. The standard method is to
explore the search space while seeking minimum
cross-sectional areas for all elements. In doing so,
critical knowledge of section geometry and orientation
is omitted, leading to inaccurate stress calculations
and structures not meeting codes of practice. This can
be addressed by constraining the optimisation method to
only use standard construction elements.
The aim of this paper is not to find fully optimized solutions, but rather to show that solutions very close to the theoretical optimum can be achieved using real-world elements. This methodology can be applied to any structural engineering design which can be generated by a grammar.",
- notes = "Humies http://www.human-competitive.org/awards https://pbs.twimg.com/media/DFHp6iTXkAQO613.jpg",
