Relevant bibliographies by topics / Fuzzy- stochastic structural analysis

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Fuzzy- stochastic structural analysis'

Author: Grafiati
Published: 4 June 2021
Last updated: 6 February 2022

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Journal articles on the topic "Fuzzy- stochastic structural analysis"

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Graf, Wolfgang, and Jan Uwe Sickert. "Time-Dependent Fuzzy Stochastic Reliability Analysis of Structures." Applied Mechanics and Materials 104 (September 2011): 45–54. http://dx.doi.org/10.4028/www.scientific.net/amm.104.45.

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The paper reviews the development of reliability assessment in structural analysis under consideration of the non-traditional uncertainty model fuzzy randomness. Starting froma discussion of sources of variability and imprecision, uncertainty models are introduced. On this basis, numerical approaches are displayed for uncertain structural analysis and reliability assessment. Thereby, variations in time are considered which results in a time-dependent reliability measure. Capacity and applicability of the approaches are demonstrated by means of an example.
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Vaško, Milan, and Milan Sága. "Application of Fuzzy Structural Analysis for Damage Prediction Considering Uncertain S/N Curve." Applied Mechanics and Materials 420 (September 2013): 21–29. http://dx.doi.org/10.4028/www.scientific.net/amm.420.21.

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The paper presents chosen traditional (based on probability theory) and non-traditional (based on possibility theory) computational tools for analysis of the material, geometric or loading uncertainties in mechanical structures. Uncertainties are introduced as bounded possible values intervals or as fuzzy sets, assuming possibility theory and as random parameters in the case of the probability theory. The main goal was to propose numerical algorithms for fuzzy analysis of stochastic oscillated FE model (truss structure) and to predict fuzzy fatigue damage considering fuzzy S/N curve.
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Marano, Giuseppe Carlo, Emiliano Morrone, Sara Sgobba, and Subrata Chakraborty. "A fuzzy random approach of stochastic seismic response spectrum analysis." Engineering Structures 32, no. 12 (December 2010): 3879–87. http://dx.doi.org/10.1016/j.engstruct.2010.09.001.

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Fang, Yongfeng, Jianbin Xiong, and Kong Fah Tee. "Time-variant structural fuzzy reliability analysis under stochastic loads applied several times." Structural Engineering and Mechanics 55, no. 3 (August 10, 2015): 525–34. http://dx.doi.org/10.12989/sem.2015.55.3.525.

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Ji, Yun. "Fuzzy Reliability Analysis of Nonlinear Structural System Based on Stochastic Response Surface Method." Advanced Materials Research 912-914 (April 2014): 1268–71. http://dx.doi.org/10.4028/www.scientific.net/amr.912-914.1268.

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Response surface method (RSM) is widely accepted as an efficient method for reliability analysis, especially when the limit state function (LSF) is supposed to be highly nonlinear or closed-form mechanical models to describe the complex structural systems are not available. However, the selection of different response surface functions may result in different computational accuracy and computing time. In this paper, stochastic response surface method (SRSM), in which Hermite polynomials are employed to approximate the real LSF, is adopted in this paper to analyze the fuzzy reliability of structural systems. With a beam presented as an example, traditional methods, such as FORM, JC method and sequence response surface method, and the method raised in the context are compared in case of the study on solving the reliability. The results show that fuzzy reliability analysis with SRSM is relatively much more efficient and less time-consuming, thus the method raised is more suitable for the analysis of this kind of problems.
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Ma, Juan, Jian-jun Chen, Wei Gao, and Tian-song Zhai. "Non-stationary stochastic vibration analysis of fuzzy truss system." Mechanical Systems and Signal Processing 20, no. 8 (November 2006): 1853–66. http://dx.doi.org/10.1016/j.ymssp.2006.04.003.

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Marano, Giuseppe Carlo, Emiliano Morrone, Giuseppe Quaranta, and Francesco Trentadue. "Fuzzy Structural Analysis of a Tuned Mass Damper Subject to Random Vibration." Advances in Acoustics and Vibration 2008 (April 6, 2008): 1–9. http://dx.doi.org/10.1155/2008/207254.

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The uncertainty is a typical feature of each human activity since the greatest part of the information is always affected by a sure level of scattering. Different methodologies which deal with the uncertainty of the real problems exist. The principal aim of this paper is to present an innovative hybrid approach which combines fuzzy and stochastic theories in facing the structural analysis of a tuned mass damper subject to a dynamic random load, modelled by a modulated filtered white noise. In this work the parameters involved in the structural analysis will be considered uncertain and supposed fuzzy sets to take into account the effects of lexical and informal uncertainties which cannot be studied in a probabilistic way. The system analysis is conducted by means of -level optimization technique. Successively, a numerical example is presented to show the effectiveness of the proposed procedure. Moreover, a sensitivity analysis is performed to expose the variation of the structural response membership function considering different input values. Finally, a comparison between the response nominal value and the fuzzificated one is proposed to obtain a structural amplification factor.
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Yang, Xingfa, Jie Liu, Xiaoyue Chen, Qixiang Qing, and Guilin Wen. "Hybrid Structural Reliability Analysis under Multisource Uncertainties Based on Universal Grey Numbers." Shock and Vibration 2018 (2018): 1–7. http://dx.doi.org/10.1155/2018/3529479.

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Nondeterministic parameters of certain distribution are employed to model structural uncertainties, which are usually assumed as stochastic factors. However, model parameters may not be precisely represented due to some factors in engineering practices, such as lack of sufficient data, data with fuzziness, and unknown-but-bounded conditions. To this end, interval and fuzzy parameters are implemented and an efficient approach to structural reliability analysis with random-interval-fuzzy hybrid parameters is proposed in this study. Fuzzy parameters are first converted to equivalent random ones based on the equal entropy principle. 3σ criterion is then employed to transform the equivalent random and the original random parameters to interval variables. In doing this, the hybrid reliability problem is transformed into the one only with interval variables, in other words, nonprobabilistic reliability analysis problem. Nevertheless, the problem of interval extension existed in interval arithmetic, especially for the nonlinear systems. Therefore, universal grey mathematics, which can tackle the issue of interval extension, is employed to solve the nonprobabilistic reliability analysis problem. The results show that the proposed method can obtain more conservative results of the hybrid structural reliability.
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Lu, Cheng, Yun-Wen Feng, and Cheng-Wei Fei. "Weighted Regression-Based Extremum Response Surface Method for Structural Dynamic Fuzzy Reliability Analysis." Energies 12, no. 9 (April 26, 2019): 1588. http://dx.doi.org/10.3390/en12091588.

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The parameters considered in structural dynamic reliability analysis have strong uncertainties during machinery operation, and affect analytical precision and efficiency. To improve structural dynamic fuzzy reliability analysis, we propose the weighted regression-based extremum response surface method (WR-ERSM) based on extremum response surface method (ERSM) and weighted regression (WR), by considering the randomness of design parameters and the fuzziness of the safety criterion. Therein, we utilize the ERSM to process the transient to improve computational efficiency, by transforming the random process of structural output response into a random variable. We employ the WR to find the efficient samples with larger weights to improve the calculative accuracy. The fuzziness of the safety criterion is regarded to improve computational precision in the WR-ERSM. The WR-ERSM is applied to perform the dynamic fuzzy reliability analysis of an aeroengine turbine blisk with the fluid-structure coupling technique, and is verified by the comparison of the Monte Carlo (MC) method, equivalent stochastic transformation method (ESTM) and ERSM, with the emphasis on model-fitting property and simulation performance. As revealed from this investigation, (1) the ERSM has the capacity of processing the transient of the structural dynamic reliability evaluation, and (2) the WR approach is able to improve modeling accuracy, and (3) regarding the fuzzy safety criterion is promising to improve the precision of structural dynamic fuzzy reliability evaluation, and (4) the change rule of turbine blisk structural stress from start to cruise for the aircraft is acquired with the maximum value of structural stress at t = 165 s and the reliability degree (Pr = 0.997) of turbine blisk. The proposed WR-ERSM can improve the efficiency and precision of structural dynamic reliability analysis. Therefore, the efforts of this study provide a promising method for structural dynamic reliability evaluation with respect to working processes.
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Tombari, Alessandro, and Luciano Stefanini. "Hybrid fuzzy – stochastic 1D site response analysis accounting for soil uncertainties." Mechanical Systems and Signal Processing 132 (October 2019): 102–21. http://dx.doi.org/10.1016/j.ymssp.2019.06.005.

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Dissertations / Theses on the topic "Fuzzy- stochastic structural analysis"

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Sládková, Ivana. "Modelování projektů se stochastickou cyklickou strukturou." Master's thesis, Vysoká škola ekonomická v Praze, 2010. http://www.nusl.cz/ntk/nusl-17283.

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The Presented Thesis is focused on exploitation of stochastic cyclical networks in project management during project planning. Particularly, it is focused on the GERT method, which enables to carry out both the probability analysis and the time analysis of projects with stochastic structure. We deal primarily with analysis of such networks where cyclical activities occur. As an integral part of the Thesis, derivation of simplified computing procedures for cyclical activities is included. We extend the possibilities of the GERT method with stochastic evaluation of time duration of activities using the fuzzy GERT method. This fuzzy GERT method is applied on the real project and its results are compared to results of Monte Carlo simulation.
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Steinigen, Frank, Wolfgang Graf, Andreas Hoffmann, and Michael Kaliske. "Nachträglich textilverstärkte Stahlbetontragwerke — Strukturanalyse mit unscharfen Daten." Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2009. http://nbn-resolving.de/urn:nbn:de:bsz:14-ds-1244047124333-78222.

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Mit der Fuzzy-Stochastischen Finite-Elemente-Methode (FSFEM) kann die nachgewiesene stochastische und nichtstochastische Datenunschärfe des stahlbewehrten Altbetons und des Textilbeton bei der Strukturanalyse berücksichtigt werden. Die für die deterministische Analyse textilverstärkter Tragwerke auf der Basis des Multi-Referenzebenen-Modells (MRM) entwickelten finiten MRM-Elemente wurden zu FSMRM-Elementen weiterentwickelt. Das Stoffmodell des mit AR-Glas bewehrten Feinbetons wurde für textile Gelege aus Carbon erweitert. Die entwickelten Modelle und Algorithmen werden zur fuzzystochastischen Tragwerksanalyse textilverstärkter Tragwerke eingesetzt.
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Mahadevan, Sankaran. "Stochastic finite element-based structural reliability analysis and optimization." Diss., Georgia Institute of Technology, 1988. http://hdl.handle.net/1853/19517.

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Wei, Xiaofan. "Stochastic Analysis and Optimization of Structures." University of Akron / OhioLINK, 2006. http://rave.ohiolink.edu/etdc/view?acc_num=akron1163789451.

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Weber, Marc Anton. "Stochastic structural analysis of engineering components using the finite element method." Doctoral thesis, University of Cape Town, 1993. http://hdl.handle.net/11427/8476.

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Bibliography: p. 113-123.
This thesis investigates probabilistic and stochastic methods for structural analysis which can be integrated into existing, commercially available finite element programs. It develops general probabilistic finite element routines which can be implemented within deterministic finite element programs without requiring major code development. These routines are implemented in the general purpose finite element program ABAQUS through its user element subroutine facility and two probabilistic finite elements are developed: a three-dimensional beam element limited to linear material behaviour and a two-dimensional plane element involving elastic-plastic material behaviour. The plane element incorporates plane strain, plane stress and axisymmetric formulations. The numerical accuracy and robustness of the routines are verified and application of the probabilistic finite element method is illustrated in two case studies, one involving a four-story, two-bay frame structure, the other a reactor pressure vessel nozzle. The probabilistic finite element routines developed in this thesis integrate point estimate methods and mean value first order methods within the same program. Both methods require a systematic sequence involving the perturbation of the random parameters to be evaluated, although the perturbation sequence of the methods differ. It is shown that computer-time saving techniques such as Taylor series and iterative perturbation schemes, developed for mean value based methods, can also be used to solve point estimate method problems. These efficient techniques are limited to linear problems; nonlinear problems must use full perturbation schemes. Finally, it is shown that all these probabilistic methods and perturbation schemes can be integrated within one program and can follow many of the existing deterministic program structures and subroutines. An overall strategy for converting deterministic finite element programs to probabilistic finite element programs is outlined.
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Cheung, Sai Hung. "Novel simulation methods for calculating the reliability of structural dynamical systems subjected to stochastic loads /." View Abstract or Full-Text, 2003. http://library.ust.hk/cgi/db/thesis.pl?CIVL%202003%20CHEUNGS.

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Thesis (M. Phil.)--Hong Kong University of Science and Technology, 2003.
Includes bibliographical references (leaves 113-116). Also available in electronic version. Access restricted to campus users.
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Chan, Wing-chi. "Modelling of nonlinear stochastic systems using neural and neurofuzzy networks /." Hong Kong : University of Hong Kong, 2001. http://sunzi.lib.hku.hk/hkuto/record.jsp?B22925843.

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Wang, Jia. "Reliability analysis and reliability-based optimal design of linear structures subjected to stochastic excitations /." View abstract or full-text, 2010. http://library.ust.hk/cgi/db/thesis.pl?CIVL%202010%20WANG.

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陳穎志 and Wing-chi Chan. "Modelling of nonlinear stochastic systems using neural and neurofuzzy networks." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2001. http://hub.hku.hk/bib/B31241475.

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Ding, Jie. "Structural and fluid analysis for large scale PEPA models, with applications to content adaptation systems." Thesis, University of Edinburgh, 2010. http://hdl.handle.net/1842/7975.

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The stochastic process algebra PEPA is a powerful modelling formalism for concurrent systems, which has enjoyed considerable success over the last decade. Such modelling can help designers by allowing aspects of a system which are not readily tested, such as protocol validity and performance, to be analysed before a system is deployed. However, model construction and analysis can be challenged by the size and complexity of large scale systems, which consist of large numbers of components and thus result in state-space explosion problems. Both structural and quantitative analysis of large scale PEPA models suffers from this problem, which has limited wider applications of the PEPA language. This thesis focuses on developing PEPA, to overcome the state-space explosion problem, and make it suitable to validate and evaluate large scale computer and communications systems, in particular a content adaption framework proposed by the Mobile VCE. In this thesis, a new representation scheme for PEPA is proposed to numerically capture the structural and timing information in a model. Through this numerical representation, we have found that there is a Place/Transition structure underlying each PEPA model. Based on this structure and the theories developed for Petri nets, some important techniques for the structural analysis of PEPA have been given. These techniques do not suffer from the state-space explosion problem. They include a new method for deriving and storing the state space and an approach to finding invariants which can be used to reason qualitatively about systems. In particular, a novel deadlock-checking algorithm has been proposed to avoid the state-space explosion problem, which can not only efficiently carry out deadlock-checking for a particular system but can tell when and how a system structure lead to deadlocks. In order to avoid the state-space explosion problem encountered in the quantitative analysis of a large scale PEPA model, a fluid approximation approach has recently been proposed, which results in a set of ordinary differential equations (ODEs) to approximate the underlying CTMC. This thesis presents an improved mapping from PEPA to ODEs based on the numerical representation scheme, which extends the class of PEPA models that can be subjected to fluid approximation. Furthermore, we have established the fundamental characteristics of the derived ODEs, such as the existence, uniqueness, boundedness and nonnegativeness of the solution. The convergence of the solution as time tends to infinity for several classes of PEPA models, has been proved under some mild conditions. For general PEPA models, the convergence is proved under a particular condition, which has been revealed to relate to some famous constants of Markov chains such as the spectral gap and the Log-Sobolev constant. This thesis has established the consistency between the fluid approximation and the underlying CTMCs for PEPA, i.e. the limit of the solution is consistent with the equilibrium probability distribution corresponding to a family of underlying density dependent CTMCs. These developments and investigations for PEPA have been applied to both qualitatively and quantitatively evaluate the large scale content adaptation system proposed by the Mobile VCE. These analyses provide an assessment of the current design and should guide the development of the system and contribute towards efficient working patterns and system optimisation.
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Books on the topic "Fuzzy- stochastic structural analysis"

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Shahram, Sarkani, ed. Stochastic analysis of structural and mechanical vibrations. Upper Saddle River, N.J: Prentice Hall, 1997.

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Computational analysis of randomness in structural mechanics. Boca Raton, Fla: CRC Press, 2009.

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Stochastic structural dynamics: Application of finite element methods. Chichester, West Sussex: Wiley, 2014.

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Shahram, Sarkani, ed. Random vibrations: Analysis of structural and mechanical systems. Amsterdam: Elsevier, 2004.

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Pawar, Prashant M. Structural health monitoring using genetic fuzzy systems. London: Springer, 2011.

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Matěj, Bílý, and Bukoveczky Juraj, eds. Random processes: Measurement, analysis, and simulation. Amsterdam: Elsevier, 1988.

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Institute for Social and Economic Change, ed. Liberalisation and efficiency of Indian commercial banks: A stochastic frontier analysis. Bangalore: Institute for Social and Economic Change, 2006.

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Brock, William A. A dynamic structural model for stock return volatility and trading volume. Cambridge, MA: National Bureau of Economic Research, 1995.

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A, Voronov A., ed. Parametricheskiĭ sintez stokhasticheskikh sistem s uchetom trebovaniĭ nadezhnosti. Moskva: "Nauka", 1992.

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International Conference on Structural Safety and Reliability (8th 2001 Newport Beach, Calif.). Structural safety and reliability: Proceedings of the 8th International Conference on Structural Safety and Reliability, ICOSSAR '01, Newport Beach, California, USA, 17-22 June 2001 / edited by R.B. Corotis, G.I. Schuëller, M. Shinozuka. Lisse, The Netherlands: A.A. Balkema, 2001.

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Book chapters on the topic "Fuzzy- stochastic structural analysis"

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Möller, Bernd, and Michael Beer. "Fuzzy and Fuzzy Stochastic Structural Analysis." In Fuzzy Randomness, 135–227. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-662-07358-2_5.

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Steinigen, Frank, Jan-Uwe Sickert, Wolfgang Graf, and Michael Kaliske. "Fuzzy and Fuzzy Stochastic Methods for the Numerical Analysis of Reinforced Concrete Structures Under Dynamical Loading." In Computational Methods in Stochastic Dynamics, 113–30. Dordrecht: Springer Netherlands, 2013. http://dx.doi.org/10.1007/978-94-007-5134-7_7.

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Au, Siu-Kui. "Stochastic Structural Dynamics." In Operational Modal Analysis, 179–204. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-4118-1_5.

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Bucher, C. G., H. J. Pradlwarter, and G. I. Schuëller. "Computational Stochastic Structural Analysis (COSSAN)." In Structural Dynamics, 301–15. Berlin, Heidelberg: Springer Berlin Heidelberg, 1991. http://dx.doi.org/10.1007/978-3-642-88298-2_13.

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Hilton, Harry H., John Hsu, and John S. Kirby. "Linear Viscoelastic Analysis with Random Material Properties." In Stochastic Structural Dynamics 1, 83–109. Berlin, Heidelberg: Springer Berlin Heidelberg, 1991. http://dx.doi.org/10.1007/978-3-642-84531-4_5.

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Inuiguchi, Masahiro, Kosuke Kato, and Hideki Katagiri. "Fuzzy Multi-Criteria Optimization: Possibilistic and Fuzzy/Stochastic Approaches." In Multiple Criteria Decision Analysis, 851–902. New York, NY: Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4939-3094-4_20.

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Chamis, C. C. "Engine Probabilistic Structural Analysis Methods Reliability/Certification." In Nonlinear Stochastic Mechanics, 115–29. Berlin, Heidelberg: Springer Berlin Heidelberg, 1992. http://dx.doi.org/10.1007/978-3-642-84789-9_10.

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Di Paola, M., G. Falsone, G. Muscolino, and G. Ricciardi. "Modal Analysis for Random Response of MDOF Systems." In Stochastic Structural Dynamics 1, 63–81. Berlin, Heidelberg: Springer Berlin Heidelberg, 1991. http://dx.doi.org/10.1007/978-3-642-84531-4_4.

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Roy, R. Valéry, and Pol D. Spanos. "Padé-Type Approach to Nonlinear Random Vibration Analysis." In Stochastic Structural Dynamics 1, 155–72. Berlin, Heidelberg: Springer Berlin Heidelberg, 1991. http://dx.doi.org/10.1007/978-3-642-84531-4_9.

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Shiao, Michael C. "First Passage Problem: A Probabilistic Dynamic Analysis for Hot Aerospace Components." In Stochastic Structural Dynamics 2, 211–32. Berlin, Heidelberg: Springer Berlin Heidelberg, 1991. http://dx.doi.org/10.1007/978-3-642-84534-5_13.

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Conference papers on the topic "Fuzzy- stochastic structural analysis"

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Ruckman, Christopher E., and David Feit. "A Tutorial on Soize’s Method for Stochastic Modeling in Structural Acoustics (Fuzzy Structures Analysis)." In ASME 1995 Design Engineering Technical Conferences collocated with the ASME 1995 15th International Computers in Engineering Conference and the ASME 1995 9th Annual Engineering Database Symposium. American Society of Mechanical Engineers, 1995. http://dx.doi.org/10.1115/detc1995-0412.

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Abstract Fuzzy structures analysis (FSA) is a stochastic approach for vibro-acoustic modeling of structures whose details cannot be modeled precisely. Pioneered by C. Soize, FSA provides a new tool that may lead to more realistic models of damping mechanisms in complex structures. This paper summarizes Soize’s method in simplified form, emphasizing the solution and post-processing procedures needed to implement FSA in computer programs. Input data needed for implementing FSA with a numerical method such as finite-element analysis are summarized. Stochastic modeling of fuzzy structures is outlined, with Newmann series expressions truncated explicitly after the second-order term. Selected post-processing formulas are given for first-order and second-order solutions.
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Feng, Jinwen, Di Wu, Wei Gao, and Guoyin Li. "Non-Deterministic Free Vibration Analysis of Structures With Random and Fuzzy Parameters." In ASME 2017 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/imece2017-71059.

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In this study, the uncertain free vibration of structures with hybrid (random and fuzzy) parameters is investigated. The non-deterministic Young’s modulus of structural elements is modeled as random variables while the uncertain mass density is represented by fuzzy parameters with associated membership functions. By implementing the α -sublevel strategy, the stochastic fuzzy eigenvalue problem is rigorously transformed into a series of stochastic interval analysis of natural frequencies. In this context, the Unified Interval Stochastic Sampling (UISS) is adopted such that the statistical characteristics of the upper and lower bounds of natural frequencies at each α -sublevel can be calculated. Subsequently, the membership functions of mean and standard deviations are respectively established for the extreme bounds of eigenvalues. Benefiting from the stochastic samples offered by the UISS method, the probability profiles of the uncertain natural frequencies at each α -sublevel can be identified by utilizing either parametric or nonparametric statistical inference methods. The accuracy and effectiveness of the proposed approach is demonstrated by analyzing a practically motivated 3D structure with comparison to the results obtained by the conventional Monte Carlo simulation method.
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Hamza, Karim, and Kazuhiro Saitou. "Automated Vehicle Structural Crashworthiness Design via a Crash Mode Matching Algorithm." In ASME 2009 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2009. http://dx.doi.org/10.1115/detc2009-87072.

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This paper presents an automated algorithm for design of vehicle structures for crashworthiness, based on the analyses of the structural crash mode. The crash mode (CM) is the history of the deformation of the different zones of the vehicle structure during a crash event. The algorithm emulates a process called crash mode matching where structural crashworthiness is improved by manually modifying the design until its crash mode matches the one the designers deem as optimal. Given an initial design and a desired crash mode, the algorithm iteratively finds new designs that have better crashworthiness performance via stochastic sampling of the design space. A new design is chosen per iteration as the best among the normally distributed samples near the current design. The mean and standard deviation of the normal distributions are adjusted in each iteration by examining the crash mode of the current design and applying a set of fuzzy logic rules that encapsulate elementary knowledge of the crash mode matching practice. Two case studies are presented to demonstrate the effectiveness of the proposed algorithm. The case studies examine a front half vehicle model, as well as a fully detailed vehicle model.
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Zvyagin, Petr, and Kirill Sazonov. "Analysis and Probabilistic Modeling of the Unstationary Ice Loads Stochastic Process, Based on Experiments With Models of Offshore Structures." In ASME 2015 34th International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/omae2015-41619.

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Experiments with models of platforms and offshore structures with vertical and inclined panels, which were conducted at Krylov Research Center (St. Petersburg), demonstrated that sometimes ice loads time series registered in these experiments cannot be considered as stationary. At the same time until nowadays methods and algorithms of probabilistic modeling were mainly based on the assumption of ice loads time series stationarity. That is because the analysis and modeling for stationary stochastic process is easier than for those unstationary. In the paper the method for determining the presence of unstationarity in ice loads time series, based on statistical analysis, is described. This method employs sample mean normality. Fuzzy C-means algorithm is used to cluster autocorrelation vectors, which are built for different fragments of time series. In the paper ice loads time series, got in experiments in ice tank with offshore structure columns and basement models, are investigated on their unstationarity. The algorithm of unstationary ice loads time series simulation is offered.
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Mochio, Takashi. "Active Control of Non-Linear System by a Fuzzy-Optimal Control Technique." In ASME 2010 Pressure Vessels and Piping Division/K-PVP Conference. ASMEDC, 2010. http://dx.doi.org/10.1115/pvp2010-25413.

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The purpose of this paper is to estimate the real time vibration control of an actively-controlled nonlinear structure due to non-stationary external loads. When the optimal control theory is adopted as a control law against the concerned task, the derivation of time dependent optimal control gains may be required because of a remarkable non-stationarity of response amplitude. In addition, since the system is nonlinear, it takes more time to calculate those time dependent gains. This means that it is difficult to strictly execute the real time active control with optimal control theory as for the non-stationary and nonlinear system. In this paper, therefore, one approximate technique, coupled fuzzy-optimal control, is proposed in order to realize the real time control of non-stationary and nonlinear system. Finally, results by deterministic analysis based on numerical simulations are compared with those by stochastic analysis using statistical equivalent linearization technique.
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Naess, Arvid, Bernt J. Leira, and Oleksandr Batsevych. "Reliability Analysis of Large Structural Systems." In 6th International Conference on Computational Stochastic Mechanics. Singapore: Research Publishing Services, 2011. http://dx.doi.org/10.3850/978-981-08-7619-7_p047.

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Valdebenito, M. A., H. A. Jensen, M. Beer, and C. A. Pérez. "Approximation Concepts for Fuzzy Structural Analysis." In Second International Conference on Vulnerability and Risk Analysis and Management (ICVRAM) and the Sixth International Symposium on Uncertainty, Modeling, and Analysis (ISUMA). Reston, VA: American Society of Civil Engineers, 2014. http://dx.doi.org/10.1061/9780784413609.014.

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Zhou, Hua-ren, Chang-yu Liu, and Shi-kai Li. "The completeness research on fuzzy stochastic analysis." In 2011 Eighth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2011). IEEE, 2011. http://dx.doi.org/10.1109/fskd.2011.6019757.

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YANG, J., and S. MANNING. "Stochastic crack growth analysis methodologies for metallic structures." In 31st Structures, Structural Dynamics and Materials Conference. Reston, Virigina: American Institute of Aeronautics and Astronautics, 1990. http://dx.doi.org/10.2514/6.1990-1015.

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Muscolino, Giuseppe, Roberta Santoro, and Alba Sofi. "Stochastic Sensitivity Analysis of Structural Systems With Interval Uncertainties." In ASME 2013 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/imece2013-63482.

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Abstract:
Interval sensitivity analysis of linear discretized structures with uncertain-but-bounded parameters subjected to stationary multi-correlated Gaussian stochastic processes is addressed. The proposed procedure relies on the use of the so-called Interval Rational Series Expansion (IRSE), recently proposed by the authors as an alternative explicit expression of the Neumann series expansion for the inverse of a matrix with a small rank-r modification and properly extended to handle also interval matrices. The IRSE allows to derive approximate explicit expressions of the interval sensitivities of the mean-value vector and Power Spectral Density (PSD) function matrix of the interval stationary stochastic response. The effectiveness of the proposed method is demonstrated through numerical results pertaining to a seismically excited three-storey frame structure with interval Young’s moduli of some columns.
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