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Research Papers: Design Automation

Ranked-Based Sensitivity Analysis for Size Optimization of Structures

[+] Author and Article Information
Babak Dizangian

Department of Civil Engineering,
University of Sistan and Baluchestan,
Zahedan 9816811775, Iran

Mohammad Reza Ghasemi

Department of Civil Engineering,
University of Sistan and Baluchestan,
Zahedan 9816745437, Iran
e-mail: mrghasemi@hamoon.usb.ac.ir

1Corresponding author.

Contributed by the Design Automation Committee of ASME for publication in the JOURNAL OF MECHANICAL DESIGN. Manuscript received January 13, 2015; final manuscript received August 6, 2015; published online October 16, 2015. Assoc. Editor: Nam H. Kim.

J. Mech. Des 137(12), 121402 (Oct 16, 2015) (10 pages) Paper No: MD-15-1022; doi: 10.1115/1.4031295 History: Received January 13, 2015; Revised August 06, 2015

This article proposes a novel ranked-based method for size optimization of structures. This method uses violation-based sensitivity analysis and borderline adaptive sliding technique (ViS-BLAST) on the margin of feasible nonfeasible (FNF) design space. ViS-BLAST maybe considered a multiphase optimization technique, where in the first phase, the first arbitrary local optimum is found by few analyses and in the second phase, a sequence of local optimum points is found through jumps and BLASTs until the global optimum is found. In fact, this technique reaches a sensitive margin zone where the global optimum is located, with a small number of analyses, utilizing a space-degradation strategy (SDS). This strategy substantially degrades the high order searching space and then proceeds with the proposed ViS-BLAST search for the optimum design. Its robustness and effectiveness are then defied by some well-known benchmark examples. The ViS-BLAST not only speeds up the optimization procedure but also it ensures nonviolated optimum designs.

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References

Figures

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Fig. 2

Flowchart of Vis-BLAST

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Fig. 1

The pseudocode of BLAST

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Fig. 3

Phase 2; progressive jumps and BLASTs to find the best solution XG

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Fig. 4

The 25-bar space truss

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Fig. 5

Selecting initial random designs by dividing the searching space into subspaces; the 25-bar truss example

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Fig. 6

First sensitivity analysis at Xdp1; the 25-bar truss example

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Fig. 13

Convergence history of optimum weight; 200-bar truss

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Fig. 7

Convergence history of optimum weight; 25-bar truss example

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Fig. 11

The 200-bar planar truss

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Fig. 12

First sensitivity analysis at Xdp1; the 200-bar truss example

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Fig. 8

The 72-bar space truss

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Fig. 9

First sensitivity analysis at Xdp1 ; the 72-bar truss example

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Fig. 10

Convergence history of best weight of 72-bar truss example

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