By Raphael T. Haftka, Zafer Gürdal
The sphere of structural optimization continues to be a comparatively new box present process swift adjustments in equipment and concentration. till lately there has been a serious imbalance among the large volume of literature at the topic, and the paucity of purposes to functional layout difficulties. This imbalance is being steadily redressed. there's nonetheless no scarcity of latest guides, yet there also are interesting functions of the tools of structural optimizations within the car, aerospace, civil engineering, laptop layout and different engineering fields. as a result of growing to be velocity of functions, examine into structural optimization tools is more and more pushed by means of real-life difficulties. t-.Jost engineers who layout buildings hire complicated general-purpose software program applications for structural research. frequently they don't have any entry to the resource application, or even extra usually they've got merely scant wisdom of the main points of the structural research algorithms utilized in this software program applications. hence the main problem confronted via researchers in structural optimization is to strengthen tools which are compatible to be used with such software program programs. one other significant problem is the excessive computational fee linked to the research of many advanced real-life difficulties. in lots of circumstances the engineer who has the duty of designing a constitution can't have the funds for to investigate it greater than a handful of occasions.
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Additional resources for Elements of structural optimization
The evolution strategy proposed by Franco et al. (2004) was in effect an adaptive GA, whereby the magnitude of mutations adapted as the analysis proceeded. The results presented for a 10-DOF structure were very good. 7% under 5% noise. The procedure failed, however, when only partial output of three measurements was used and the average error increased to more than 15%. The modified GA strategy presented and applied throughout this book was first proposed by the authors for structural identification problems (Perry et al, 2006).
2004) also used frequency information, utilizing the sum of diagonal terms from a residual force matrix as the objective function. Chou and Ghaboussi (2001) simply used the response of the structure to a series of static load to define their objective function. This method nevertheless has a limitation that only stiffness information can be obtained. The evolution strategy proposed by Franco et al. (2004) was in effect an adaptive GA, whereby the magnitude of mutations adapted as the analysis proceeded.
Much understanding and refinements are needed to make the GA approach work effectively. Incorporating appropriate coding, altering the architecture of the GA, and integrating problem-specific information are essential in developing strategies appropriate to real world situations. For illustration, a simple GA and its theoretical framework based on classical binary encoding and operators are presented in the following sections. 4, whereas the GA strategy developed and later applied in this book is described in chapter 3.