A Grasshopper Optimization Algorithm-Based Response Surface Method for Non-Probabilistic Structural Reliability Analysis with an Implicit Performance Function Article Swipe
Qi Li
,
Junmu Wang
,
Guoshao Su
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3390/buildings12071061
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3390/buildings12071061
Non-probabilistic reliability analysis has great developmental potential in the field of structural reliability analysis, as it is often difficult to obtain enough samples to construct an accurate probability distribution function of random variables based on probabilistic theory. In practical engineering cases, the performance function (PF) is commonly implicit. Monte Carlo simulation (MCS) is commonly used for structural reliability analysis with implicit PFs. However, MCS requires the calculation of thousands of PF values. Such calculation could be time-consuming when the structural systems are complicated, and numerical analysis procedures such as the finite element method have to be adopted to obtain the PF values. To address this issue, this paper presents a grasshopper optimization algorithm-based response surface method (RSM). First, the method employs a quadratic polynomial to approximate the implicit PF with a small set of the actual values of the implicit PF. Second, the grasshopper optimization algorithm (GOA) is used to search for the global optimal solution of the scaling factor of the convex set since the problem of solving the reliability index is transformed into an unconstrained optimal problem. During the search process in the GOA, a dynamic response surface updating strategy is used to improve the approximate accuracy near the current optimal point to improve the computing efficiency. Two mathematical examples and two engineering structure examples that use the proposed method are given to verify its feasibility. The results compare favorably with those of MCS. The proposed method can be non-invasively combined with finite element analysis software to solve non-probabilistic reliability analysis problems of structures with implicit PF with high efficiency and high accuracy.
Related Topics
Concepts
Probabilistic logic
Mathematical optimization
Computer science
Algorithm
Reliability (semiconductor)
Finite element method
Probabilistic analysis of algorithms
Monte Carlo method
Interval (graph theory)
Mathematics
Artificial intelligence
Engineering
Power (physics)
Structural engineering
Combinatorics
Physics
Statistics
Quantum mechanics
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/buildings12071061
- https://www.mdpi.com/2075-5309/12/7/1061/pdf?version=1658408763
- OA Status
- gold
- Cited By
- 3
- References
- 58
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4286495459
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4286495459Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/buildings12071061Digital Object Identifier
- Title
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A Grasshopper Optimization Algorithm-Based Response Surface Method for Non-Probabilistic Structural Reliability Analysis with an Implicit Performance FunctionWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-07-21Full publication date if available
- Authors
-
Qi Li, Junmu Wang, Guoshao SuList of authors in order
- Landing page
-
https://doi.org/10.3390/buildings12071061Publisher landing page
- PDF URL
-
https://www.mdpi.com/2075-5309/12/7/1061/pdf?version=1658408763Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
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https://www.mdpi.com/2075-5309/12/7/1061/pdf?version=1658408763Direct OA link when available
- Concepts
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Probabilistic logic, Mathematical optimization, Computer science, Algorithm, Reliability (semiconductor), Finite element method, Probabilistic analysis of algorithms, Monte Carlo method, Interval (graph theory), Mathematics, Artificial intelligence, Engineering, Power (physics), Structural engineering, Combinatorics, Physics, Statistics, Quantum mechanicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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3Total citation count in OpenAlex
- Citations by year (recent)
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2024: 1, 2023: 2Per-year citation counts (last 5 years)
- References (count)
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58Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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