Design optimization of steel structures considering uncertainties

Papadrakakis, Manolis; Lagaros, Nikos D.; Plevris, Vagelis

doi:10.1016/j.engstruct.2005.04.002

Cited by 102 publications

(47 citation statements)

References 13 publications

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“…Several authors also propose their use as assessors of innovative design. Papadrakakis et al (2005) note that an important part of designing steel structures should be to ensure reliability and robustness in a way that takes account of the fact that some of the key parameters may be uncertain. To achieve robustness, they utilise neural networks to obtain structures that are least effected by variation in the key parameters whilst at the same time attempting to minimise the weight of a structure.…”

Section: Neural Network In Designmentioning

confidence: 99%

A survey of AI in operations management from 2005 to 2009

Kobbacy

Vadera

2011

Journal of Manufacturing Technology Management

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Purpose: The use of AI for operations management, with its ability to evolve solutions, handle uncertainty and perform optimisation continues to be a major field of research. The growing body of publications over the last two decades means that it can be difficult to keep track of what has been done previously, what has worked, and what really needs to be addressed. Hence this paper presents a survey of the use of AI in operations management aimed at presenting the key research themes, trends and directions of research.Design/methodology/approach: The paper builds upon our previous survey of this field which was carried out for the 10 year period 1995 to 2004(Kobbacy et al. 2007. Like the previous survey, it uses the Elsevier's ScienceDirect database as a source. The framework and methodology adopted for the survey is kept as similar as possible to enable continuity and comparison of trends. Thus the application categories adopted are: (a) design, (b) scheduling, (c) process planning and control and (d) quality, maintenance and fault diagnosis. Research on utilising neural networks, case based reasoning, fuzzy logic, knowledge based systems, data mining, and hybrid AI in the four application areas are identified. Findings:The survey categorises over 1400 papers, identifying the uses of AI in the four categories of operations management and concludes with an analysis of the trends, gaps and directions for future research. The findings include: (a) The trends for Design and Scheduling show a dramatic increase in the use of GAs since 2003-04 that reflect recognition of their success in these areas, (b) A significant decline in research on use of KBS, reflecting their transition into practice, (c) an increasing trend in the use of fuzzy logic in Quality, Maintenance and Fault Diagnosis, (d) surprising gaps in the use of CBR and hybrid methods in operations management that offer opportunities for future research.Originality/value: This is the largest and most comprehensive study to classify research on the use of AI in operations management to date. The survey and trends identified provide a useful reference point and directions for future research.

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Section: Neural Network In Designmentioning

confidence: 99%

A survey of AI in operations management from 2005 to 2009

Kobbacy

Vadera

2011

Journal of Manufacturing Technology Management

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“…Uncertainty quantification provides metrics to study the relationship between imprecisely prescribed model inputs and the model's predictions (Papadrakakis et al, 2005). However, the explicit quantification of uncertainties is still a major challenge (Takewaki, 2015).…”

Section: Modeling Of Uncertaintiesmentioning

confidence: 99%

Computational Structural Engineering: Past Achievements and Future Challenges

Plevris

Tsiatas

2018

Front. Built Environ.

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PAST ACHIEVEMENTS AND CURRENT TRENDSComputational methods are computer-based methods used to numerically solve mathematical models that describe physical phenomena. The purpose of computational modeling is to study the behavior of complex systems by means of computer simulations and it can be used to make predictions of the system's behavior under different conditions, often for cases in which intuitive analytical solutions are not available (Nature, 2018). Computational methods have emerged in engineering during the 1960s. Since then, structural engineers have been leaders in technological solutions to engineering analysis and design problems. The evolution of electronic computers together with the tremendous increase of computational power has triggered the continuous development of computational methods. Rapid advances in computer hardware have had a profound effect on various engineering disciplines. The applications are numerous and cover a broad field of engineering branches including civil, mechanical, naval, electrical, aerospace, material, biomolecular, among others. Computational structural engineering has evolved as an insightful blend combining both structural analysis and computer science.Among all computational methods, the Finite Element Method (FEM) and the Boundary Element Method (BEM) are the most prevalent ones. Both methods exhibit unique characteristics as well as advantages and disadvantages. The FEM, as a simulation tool, enables sophisticated methods of computational mechanics, computer technology and applied mathematics. It has been broadly adopted in scientific research and engineering applications and it can be considered as the most popular method used for structural analysis, tackling linear and nonlinear problems of systems with various geometries, material properties and loads (Reddy, 2005). In FEM, the solution domain is subdivided into a finite number of elements. Each element approximately reproduces the behavior of a small region of the body it represents, but continuity between the elements is only enforced in an overall minimum energy sense. The FEM is especially suitable for problems with complex geometries, but the whole-body discretization scheme that is utilized inevitably leads to a large number of finite elements and, thus, increased computational cost. Although Professor R. Clough coined the term "Finite Element Method" in his pioneer work dated back in 1960 (Clough, 1960), the answer to the question "who invented FEM in everyday use?" is possibly M. Jonathan (Jon) Turner at Boeing who generalized and perfected the Direct Stiffness Method, and convinced his company to commit resources to it, over the period 1952 -1964(Felippa, 2017. The FEM obtained its real impetus in the 1960s and 1970s by the developments of pioneers J. H. Argyris, R. W. Clough, H. C. Martin, O. C. Zienkiewicz and their co-workers who evolved the method and applied it to a wide range of structural problems and applications.In the years since its first use, FEM has grown and developed into a standard...

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“…The applications and studies of robust optimisation can be widely found in other non software engineering research literature [39], [40] but are seldom found in the requirements engineering research. In requirements engineering, to the best of our knowledge, there are only three studies applying robust optimisation on requirements optimisation area.…”

Section: B Uncertainty Handling In Requirements Selection and Optimimentioning

confidence: 99%

Exact Analysis for Next Release Problem

Li¹

2016

2016 IEEE 24th International Requirements Engineering Conference (RE)

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Abstract-In software engineering, determining the set of requirements to implement in the next release is a critical foundation for the success of a project. Inappropriately including or excluding requirements may result in products that fail to satisfy stakeholders' needs, and might cause loss of revenue. In the meantime, uncertainty is characterised by incomplete understanding. It is inevitable in the early phase of requirements engineering, and could lead to unsound requirement decisions. To ease the impact of uncertainty in the software development process, it is important to provide techniques that explicitly manage uncertainty in requirements analysis and optimisation.This proposed research aims to provide a decision support framework for analysing uncertainty in requirements selection and optimisation. The proposed research involves three stages. Firstly, a simulation optimisation technique is introduced to model requirements uncertainty in requirements optimisation. Then, an exact technique is designed to eliminate the algorithmic uncertainty. Lastly, a probabilistic uncertainty analysis is applied to help the decision maker to understand requirement uncertainty propagation and the characteristics of requirements in requirements selection process.

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Design optimization of steel structures considering uncertainties

Cited by 102 publications

References 13 publications

A survey of AI in operations management from 2005 to 2009

A survey of AI in operations management from 2005 to 2009

Computational Structural Engineering: Past Achievements and Future Challenges

Exact Analysis for Next Release Problem

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