Aminbakhsh, Saman

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Profile URL
https://hdl.handle.net/20.500.14411/6691
Name Variants
Saman, Aminbakhsh
S., Aminbakhsh
A., Saman
Saman Aminbakhsh
S.,Aminbakhsh
Aminbakhsh,Saman
A.,Saman
Aminbakhsh, Saman
Aminbakhsh,S.
Job Title
Doktor Öğretim Üyesi
Email Address
saman.aminbakhsh@atilim.edu.tr
Main Affiliation
Civil Engineering
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Website
ORCID ID
0000-0002-4389-1910
Scopus Author ID
55768552300
Turkish CoHE Profile ID
8D50169F8600DF9B
Google Scholar ID
JRu69LMAAAAJ
WoS Researcher ID
LIF-9792-2024

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14

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17

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5

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4

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6

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7

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10

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11

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15

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632

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Scholarly Output

15

Articles

11

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2

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WoS Citation Count

115

Scopus Citation Count

102

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5

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4

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WoS Citations per Publication

7.67

Scopus Citations per Publication

6.80

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4

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Structures2
Journal of Construction Engineering, Management & Innovation2
European Journal of Operational Research1
Journal of Building Engineering1
Journal of Construction Engineering and Management1
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Author: Azad, Saeid KazemzadehHas files: false

Scholarly Output Search Results

Now showing 1 - 3 of 3
  • Thumbnail Image
    Article
    Citation - WoS: 12
    Citation - Scopus: 13
    E-Constraint Guided Stochastic Search With Successive Seeding for Multi-Objective Optimization of Large-Scale Steel Double-Layer Grids
    (Elsevier, 2022) Azad, Saeid Kazemzadeh; Aminbakhsh, Saman
    This paper proposes a design-driven structural optimization algorithm named e-constraint guided stochastic search (e-GSS) for multi-objective design optimization of large-scale steel double-layer grids having numerous discrete design variables. Based on the well-known e-constraint method, first, the multi-objective optimization problem is transformed into a set of single-objective optimization problems. Next, each single-objective optimization problem is tackled using an enhanced reformulation of the standard guided stochastic search algorithm proposed based on a stochastic maximum incremental/decremental step size approach. Moreover, a successive seeding strategy is employed in conjunction with the proposed e-GSS algorithm to improve its performance in multi-objective optimization of large-scale steel double-layer grids. The numerical results obtained through multi-objective optimization of three challenging test examples, namely a 1728-member double-layer compound barrel vault, a 2304-member double-layer scallop dome, and a 2400-member double-layer multi-radial dome, demonstrate the usefulness of the proposed e-GSS algorithm in generating Pareto fronts of the foregoing multi-objective structural optimization problems with up to 2400 distinct sizing variables.
  • Thumbnail Image
    Article
    Citation - WoS: 25
    Citation - Scopus: 32
    High-Dimensional Optimization of Large-Scale Steel Truss Structures Using Guided Stochastic Search
    (Elsevier Science inc, 2021) Azad, Saeid Kazemzadeh; Aminbakhsh, Saman
    Despite a plethora of truss optimization algorithms devised in the recent literature of structural optimization, still high-dimensional large-scale truss optimization problems have not been properly tackled basically due to the excessive computational effort required to handle the foregoing instances. In this study, application of a recently developed design-driven heuristic, namely guided stochastic search (GSS), is extended to a more challenging class of truss optimization problems having thousands of design variables. Two variants of the algorithm, namely GSSA and GSSB, have been employed for sizing optimization of four high-dimensional examples of steel trusses, i.e., a 2075-member single-layer onion dome, a 2688-member double-layer open dome, a 6000-member doublelayer scallop dome, and a 15048-member double-layer grid as per AISC-LRFD specification. The numerical results obtained indicate the efficiency of GSSA and GSSB in handling high-dimensional instances of large-scale steel trusses with up to 15048 discrete design variables.
  • Thumbnail Image
    Article
    Citation - WoS: 11
    Citation - Scopus: 10
    Multi-Stage Guided Stochastic Search for Optimization and Standardization of Free-Form Steel Double-Layer Grids
    (Elsevier Science inc, 2021) Azad, Saeid Kazemzadeh; Aminbakhsh, Saman; Shaban, Samer S. S.
    There has been a growing interest in the use of free-form structures with irregularly curved yet aesthetically pleasing configurations in the recent decades. Although design optimization of regular steel grids has been well addressed in the literature of structural optimization, still limited work has been devoted to optimum design of real-size free-form grid structures. On the one hand, a main obstacle when dealing with real-size free-form steel grids is the excessive computational effort associated with contemporary evolutionary optimization algorithms. On the other hand, it is generally perceived that the obtained final designs using conventional optimization algorithms may not necessarily be favored in practice if certain provisions are not stipulated by the algorithm to preclude an abundance of distinct steel section sizes in the final design. Hence, instead of offering a single optimum or near optimum design, it would be more desirable to provide the designer or decision maker with a Pareto front set of non-dominated design alternatives taking into account both the minimum weight as well as the assortment of available steel section sizes in the final design. Accordingly, in this paper, a computationally efficient multi-stage guided stochastic search algorithm is proposed for optimization and standardization of realsize free-form steel double-layer grids. A gradual design-oriented section elimination approach is followed where in the first optimization stage, a complete set of commercially available steel sections is introduced to the algorithm and in the succeeding stages, the size of section list is reduced by eliminating the redundant sizes. Two variants of the algorithm are employed to demonstrate the usefulness of the proposed technique in challenging test examples of free-form steel double-layer grids, and the obtained Pareto fronts are plotted to illustrate the trade-off between minimum weight and assortment of steel section sizes in the final design.