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

Author: Grafiati

Published: 4 June 2021

Last updated: 6 February 2022

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1

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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Penner, Eduard, Ismail Caylak, Alex Dridger, and Rolf Mahnken. "A polynomial chaos expanded hybrid fuzzy-stochastic model for transversely fiber reinforced plastics." Mathematics and Mechanics of Complex Systems 7, no. 2 (May 27, 2019): 99–129. http://dx.doi.org/10.2140/memocs.2019.7.99.

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YUE, ZHANG, and LIU XILA. "THEORY OF RESPONSE ANALYSIS FOR CONTINUOUS FUZZY STOCHASTIC DYNAMICAL SYSTEMS I. NORMAL MODE METHOD∗." Civil Engineering and Environmental Systems 15, no. 1 (January 1998): 23–44. http://dx.doi.org/10.1080/02630259808970228.

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YUE, ZHANG, and LIU XILA. "THEORY OF RESPONSE ANALYSIS FOR CONTINUOUS FUZZY STOCHASTIC DYNAMICAL SYSTEMS II. INFLUENCE FUNCTION METHOD∗." Civil Engineering and Environmental Systems 15, no. 1 (January 1998): 45–66. http://dx.doi.org/10.1080/02630259808970229.

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14

Zoghlami, Mohamed Ali, Minyar Sassi Hidri, and Rahma Ben Ayed. "Consensus-Driven Cluster Analysis: Top-Down and Bottom-Up Based Split-and-Merge Classifiers." International Journal on Artificial Intelligence Tools 26, no. 04 (August 2017): 1750018. http://dx.doi.org/10.1142/s021821301750018x.

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Consensus clustering is used in data analysis to generate stable results out of a set of partitions delivered by stochastic methods. Typically, the goal is searching for the socalled median (or consensus) partition, i.e. the partition that is most similar, on average, to all the input partitions. In this paper we address the problem of combining multiple fuzzy clusterings without access to the underlying features of the data while basing on inter-clusters similarity. We are concerned of top-down and bottom-up based consensus-driven fuzzy clustering while splitting and merging worst clusters. The objective is to reconcile a structure, developed for patterns in some dataset with the structural findings already available for other related ones. The proposed classifiers consider dispersion and dissimilarity between the partitions as well as the corresponding fuzzy proximity matrices. Several illustrative numerical examples, using both synthetic data and those coming from available machine learning repositories, are also included. The experimental component of the study shows the efficiency of the proposed classifiers in terms of quality and runtime.

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15

Subagadis, Yohannes Hagos, Niels Schütze, and Jens Grundmann. "A Fuzzy-Stochastic Modeling Approach for Multiple Criteria Decision Analysis of Coupled Groundwater-Agricultural Systems." Water Resources Management 30, no. 6 (March 1, 2016): 2075–95. http://dx.doi.org/10.1007/s11269-016-1270-5.

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16

Maravas, Alexander, and John-Paris Pantouvakis. "A New Approach to Studying Net Present Value and the Internal Rate of Return of Engineering Projects under Uncertainty with Three-Dimensional Graphs." Advances in Civil Engineering 2018 (September 12, 2018): 1–9. http://dx.doi.org/10.1155/2018/6108680.

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Cost-benefit analysis (CBA) is very useful when appraising engineering projects and examining their long-term financial and social sustainability. However, the inherent uncertainty in the estimation of completion time, final costs, and the realization of benefits often act as an impediment to its application. Since the emergence of fuzzy set theory, there have been significant developments in uncertainty modelling in project evaluation and investment analysis, primarily in the area of formulating a fuzzy version of CBA. In this context, in studying the key indicators of CBA, whereas fuzzy net present value (fNPV) has been investigated quite extensively, there are significant issues in the calculation of fuzzy internal rate of return (fIRR) that have not been addressed. Hence, this paper presents a new conceptual model for studying and calculating fNPV and fIRR. Three-dimensional fNPV and fIRR graphs are introduced as a means of visualizing uncertainty. A new approach is presented for the precise calculation of fIRR. To facilitate practical application, a computerization process is also presented. Additionally, the proposed methodology is exemplified in a sample motorway project whereby its advantages over traditional stochastic uncertainty modelling techniques such as Monte Carlo analysis are discussed. Overall, it is concluded that the new approach is very promising for modelling uncertainty during project evaluation for both project managers and project stakeholders.

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17

Montazerolghaem, Mahdi, and Wolfram Jäger. "Fuzzy Numbers Applied in Reliability Assessment of Unreinforced Masonry Shear Wall." Modern Applied Science 10, no. 6 (April 10, 2016): 147. http://dx.doi.org/10.5539/mas.v10n6p147.

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In order to have safe and economy construction, different sources of uncertainty should be properly characterized and considered in structural design and verification. A reliability analysis is run to assess the consistency of design process, including the uncertainty. A full probabilistic approach is an appropriate means in considering the aleatory portion of uncertainty. In dealing with epistemic uncertainty in reliability analysis, modern mathematical tools like fuzzy logic is required. The non-deterministic design in a case study on Unreinforced Masonry shear Wall (URMW) by applying fuzzy numbers has performed. Instead of uncertain deterministic data of material strength, a range of possible numbers in the form of fuzzy numbers introduced to the model, considering the experiences and the expert knowledge. The predicted capacity which is fuzzy number provide more insight into behavior of URMW. Moreover, the study on significant influence of each variable on the ultimate capacity of URMW is easier. Several reliability analysis are run using only stochastic method with using fuzzy numbers. The effect of model uncertainty on assessed reliability is highlighted. The distinction between linear and non-linear application of partial safety factors is assessed. The result illustrate the fluctuation of reliability level of URMW for a wide range of applied normal force and different materials.

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18

Ganji, A., D. Khalili, M. Karamouz, K. Ponnambalam, and M. Javan. "A Fuzzy Stochastic Dynamic Nash Game Analysis of Policies for Managing Water Allocation in a Reservoir System." Water Resources Management 22, no. 1 (January 18, 2007): 51–66. http://dx.doi.org/10.1007/s11269-006-9143-y.

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19

Pełczyński, J. "On Mathematical Descriptions of Uncertain Parameters in Engineering Structures." Archives of Civil Engineering 64, no. 4 (December 1, 2018): 2–19. http://dx.doi.org/10.2478/ace-2018-0059.

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AbstractCivil engineering is one of the many fields of occurrences of uncertain parameters. The present paper in an attempt to present and describe the most common methods used for inclusions of uncertain parameters. These methods can be applied in the area of civil engineering as well as for a larger domain. Definitions and short explanations of methods based on probability, interval analysis, fuzzy sets, and convex sets are presented. Selected advantages, disadvantages, and the most common fields of implementation are indicated.An example of a cantilever beam presented in this paper shows the main differences between the methods. Results of the performed analysis indicate that the use of convex sets allows us to obtain an accuracy of results similar to stochastic models. At the same time, the computational speed characteristic for interval methods is maintained.

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20

Kala, Zdeněk. "FUZZY PROBABILITY ANALYSIS OF THE FATIGUE RESISTANCE OF STEEL STRUCTURAL MEMBERS UNDER BENDING/FUZI TIKIMYBINĖ ANALIZĖ VERTINANT LENKIAMŲ PLIENINIŲ ELEMENTŲ ATSPARĮ NUOVARGIUI." JOURNAL OF CIVIL ENGINEERING AND MANAGEMENT 14, no. 1 (March 31, 2008): 67–72. http://dx.doi.org/10.3846/1392-3730.2008.14.67-72.

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The paper is aimed at the fuzzy probabilistic analysis of fatigue resistance due to uncertainty of input parameters. The fatigue resistance of the steel member is evaluated by linear fracture mechanics as the number of cycles leading to the propagation of initial cracks into a critical crack resulting in brittle fracture. When the histogram of stress range is known, the fatigue resistance is a random variable. In the event that the histogram is unknown or was acquired from a small number of experiments, another source of uncertainty is of an epistemic origin. Two basic approaches, which make provision for uncertainty of input histograms of stress range, are illustrated in the paper. Uncertainty of histograms of stress range is taken into account by the variability of equivalent stress range in the first stochastic approach. Input histograms as considered as members of a fuzzy set in the second approach. Santrauka Straipsnyje nagrinėjamas atspario nuovargio esant neapibrėžtiems pradiniams duomenims vertinimas naudojant fuzi tikimybinę analizę. Plieninių elementų atsparis nuovargiui pagal tiesinę irimo mechaniką apibūdinamas ciklų skaičiumi, kai pradiniai plyšiai perauga į kritinį plyšį, sukeliantį trapų suirimą. Kai įtempimų kitimo histograma yra žinoma, atsparis nuovargiui yra atsitiktinis dydis. Kai histograma yra nežinoma arba ji atitinka mažą eksperimentų skaičių, atsiranda kitas episteminės kilmės neapibrėžtumas. Pateikiami du pagrindiniai būdai, rodantys histogramos neapibrėžtumą. Pirmuoju, stochastiniu būdu, įtempių kitimo diapazono histograma yra modeliuojama ekvivalentinio įtempio kitimu. Antruoju būdu pradinės histogramos nagrinėjamos kaip fuzi aibės elementai.

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Mishra, Sonalika, Suchismita Patel, Ramesh Chandra Prusty, and Sidhartha Panda. "MVO optimized hybrid FOFPID-LQG controller for load frequency control of an AC micro-grid system." World Journal of Engineering 17, no. 5 (July 15, 2020): 675–86. http://dx.doi.org/10.1108/wje-05-2019-0142.

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Purpose This paper aims to implement a maiden methodology for load frequency control of an AC multi micro-grid (MG) by using hybrid fractional order fuzzy PID (FOFPID) controller and linear quadratic Gaussian (LQG). Design/methodology/approach The multi MG system considered is consisting of photovoltaic, wind turbine and a synchronous generator. Different energy storage devices i.e. battery energy storage system and flywheel energy storage system are also integrated to the system. The renewable energy sources suffer from uncertainty and fluctuation from their nominal values, which results in fluctuation of system frequency. Inspired by this difficulty in MG control, this research paper proposes a hybridized FOFPID and LQG controller under random and stochastic environments. Again to confer viability of proposed controller its performances are compared with PID, fuzzy PID and fuzzy PID-LQG controllers. A comparative study among all implemented techniques i.e. proposed multi-verse optimization (MVO) algorithm, particle swarm optimization and genetic algorithm has been done to justify the supremacy of MVO algorithm. To check the robustness of the controller sensitivity analysis is done. Findings The merged concept of fractional calculus and state feedback theory is found to be efficient. The designed controller is found to be capable of rejecting the effect of disturbances present in the system. Originality/value From the study, the authors observed that the proposed hybrid FOPID and LQG controller is robust hence, there is no need to reset the controller parameters with a large change in network parameters.

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Abdul-Rahman, Hamzah, Chen Wang, and Norjuma’ah Binti Muhammad. "PROJECT PERFORMANCE MONITORING METHODS USED IN MALAYSIA AND PERSPECTIVES OF INTRODUCING EVA AS A STANDARD APPROACH / MALAIZIJOJE NAUDOJAMI PROJEKTŲ EFEKTYVUMO STEBĖJIMO METODAI IR GALIMYBĖS EVA NAUDOTI KAIP STANDARTINĘ METODIKĄ." Journal of Civil Engineering and Management 17, no. 3 (September 20, 2011): 445–55. http://dx.doi.org/10.3846/13923730.2011.598331.

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Earned value analysis (EVA) is a well-known project management tool for monitoring and forecasting the project performance such as time and cost. Despite the benefits found in the manufacturing industry, EVA has not been widely implemented in the Malaysian construction industry. This study is aimed to determine the advantages of EVA over other project control methods, to determine the suitability of implementing EVA in construction projects, and to develop a working flowchart as a guide in implementing EVA. Accordingly, qualitative approaches including the structured interview survey and the flowchart development were employed in this study. Findings reveal that comparing to stochastic methods, Fuzzy logic model, and miscellaneous methods, the EVA has remarkable advantages in accuracy, flexibility, and adaptability for complexity. Malaysian government has decided to implement EVA to enhance the level of project management for the whole country. Hence, an EVA working flowchart was developed by the authors, through which more de- tailed project status could be monitored and more accurate future performance of the project could be forecasted. Santrauka Uždirbtos vertės analizė (UVA, angl. Earned Value Analysis, EVA) – paplitęs projektų valdymo įrankis, skirtas projekto efektyvumui, pavyzdžiui, laikui ir sąnaudoms, stebėti ir prognozuoti. Nors UVA nauda gamybos sektoriuje akivaizdi, Malaizijos statybų sektoriuje ji nėra plačiai taikoma. Šiuo tyrimu siekiama nustatyti UVA pranašumus prieš kitus projektų kontrolės metodus, nustatyti UVA tinkamumą statybų projektams ir sukurti darbinę srautų diagramą, kuri padėtų diegiant UVA. Taigi tyrime taikyti kokybiniai metodai, įskaitant apklausą, naudojant nustatytos struktūros pokalbius, ir srautų diagramos kūrimą. Išvados rodo, kad tikslumu, lankstumu ir pritaikymu sudėtingiems atvejams UVA yra gerokai prana- šesnė už stochastinius metodus, neraiškiosios logikos modelį ir įvairius kitus metodus. Malaizijos Vyriausybė nutarė įdiegti UVA, taip pagerindama projektų valdymą visoje šalyje. Dėl to autoriai sukūrė darbinę UVA srautų diagramą, kuri leidžia stebėti smulkesnius projekto būsenos aspektus ir tiksliau prognozuoti projekto efektyvumą ateityje.

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OGURA, Yukio. "Stochastic Fuzzy Analysis." Journal of Japan Society for Fuzzy Theory and Systems 10, no. 6 (1998): 1012–19. http://dx.doi.org/10.3156/jfuzzy.10.6_1012.

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Ma, Juan, Jian-jun Chen, and Wei Gao. "Dynamic Response Analysis of Fuzzy Stochastic Truss Structures under Fuzzy Stochastic Excitation." Computational Mechanics 38, no. 3 (April 28, 2006): 283–92. http://dx.doi.org/10.1007/s00466-006-0052-y.

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Jiang, Xiaomo, Sankaran Mahadevan, and Yong Yuan. "Fuzzy stochastic neural network model for structural system identification." Mechanical Systems and Signal Processing 82 (January 2017): 394–411. http://dx.doi.org/10.1016/j.ymssp.2016.05.030.

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Möller, Bernd, Wolfgang Graf, Jan-Uwe Sickert, and Uwe Reuter. "Numerical simulation based on fuzzy stochastic analysis." Mathematical and Computer Modelling of Dynamical Systems 13, no. 4 (August 2007): 349–64. http://dx.doi.org/10.1080/13873950600994514.

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Chao, Ru-Jen, and Bilal M. Ayyub. "Structural Analysis with Fuzzy Variables." Computer-Aided Civil and Infrastructure Engineering 11, no. 1 (January 1996): 47–58. http://dx.doi.org/10.1111/j.1467-8667.1996.tb00308.x.

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Marti, K., G. I. Schueller, B. H. V. Topping, and C. A. Mota Soares. "Stochastic Structural Analysis, Optimization and Re-analysis." Computers & Structures 85, no. 10 (May 2007): 563–65. http://dx.doi.org/10.1016/j.compstruc.2006.10.002.

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Xu, Xiangdong, Anthony Chen, and Lin Cheng. "Stochastic Network Design Problem with Fuzzy Goals." Transportation Research Record: Journal of the Transportation Research Board 2399, no. 1 (January 2013): 23–33. http://dx.doi.org/10.3141/2399-03.

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Marti, K. "Plastic Structural Analysis Under Stochastic Uncertainty." Mathematical and Computer Modelling of Dynamical Systems 9, no. 3 (September 2003): 303–25. http://dx.doi.org/10.1076/mcmd.9.3.303.24149.

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Lyon, Richard H. "Statistical energy analysis and structural fuzzy." Journal of the Acoustical Society of America 97, no. 5 (May 1995): 2878–81. http://dx.doi.org/10.1121/1.411854.

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Zhang, Ling, Bo Zhang, and YanPing Zhang. "The structural analysis of fuzzy measures." Science China Information Sciences 54, no. 1 (January 2011): 38–50. http://dx.doi.org/10.1007/s11432-010-4155-x.

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Sniady, Pawel, Krystyna Mazur-Sniady, Roza Sieniawska, and Stanislaw Zukowski. "Fuzzy stochastic elements method. Spectral approach." Mechanical Systems and Signal Processing 37, no. 1-2 (May 2013): 152–62. http://dx.doi.org/10.1016/j.ymssp.2012.09.016.

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Zhang, Yong-guang, and Masanori Sugisaka. "FUZZY CLUSTERING ANALYSIS OF A CLASS OF STOCHASTIC SYSTEMS." Proceedings of the ISCIE International Symposium on Stochastic Systems Theory and its Applications 1995 (May 5, 1995): 143–48. http://dx.doi.org/10.5687/sss.1995.143.

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35

Yan, Xin Chi, and Yuan Hua. "Failure Analysis of the Stochastic Structural System." Key Engineering Materials 324-325 (November 2006): 223–26. http://dx.doi.org/10.4028/www.scientific.net/kem.324-325.223.

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Because there were many random factors, the failure analysis and reliability analysis of stochastic structural system was very difficult. In this paper, failure procedure and reliability analysis flow chart of stochastic structural system based on stochastic finite element were present. Establishment of the safety margin, reduced member stiffness matrix and opposite sign of the equivalent nodal force was analyzed in the failure process. Stochastic finite element method was adopt to solve the structures’ stochastic, and the reliability of structural system is evaluated by PNET method. According to probabilities of the failure paths redound to probability of failure of the structural system, the most significant failure paths was determined on the basis of the branch-and-bound method. Then, a classical 48-bar space truss problem is made as an example to illustrate the predominance of this algorithm, the calculation shows that the analysis of the failure process is justified; this methodology is efficient and useful for reliability analysis of large stochastic structural system.

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Sherman, Alex, Casey Smith, Andreas Geiger, Paul Stroud, Jeremy Hinds, and Garth Simpson. "Structural analysis by stochastic differential scanning calorimetry." Acta Crystallographica Section A Foundations and Advances 75, a1 (July 20, 2019): a296. http://dx.doi.org/10.1107/s0108767319097101.

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Zimmerman, James J., J. Hugh Ellis, and Ross B. Corotis. "Stochastic Optimization Models for Structural‐Reliability Analysis." Journal of Structural Engineering 119, no. 1 (January 1993): 223–39. http://dx.doi.org/10.1061/(asce)0733-9445(1993)119:1(223).

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Adamson, L. J., S. Fichera, and J. E. Mottershead. "Aeroelastic stability analysis using stochastic structural modifications." Journal of Sound and Vibration 477 (July 2020): 115333. http://dx.doi.org/10.1016/j.jsv.2020.115333.

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Lenzen, A., and D. Hartmann. "Stochastic structural analysis and lifetime oriented optimization." Optimization 47, no. 3-4 (January 2000): 369–81. http://dx.doi.org/10.1080/02331930008844487.

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Araújo, J. M., and A. M. Awruch. "On stochastic finite elements for structural analysis." Computers & Structures 52, no. 3 (August 1994): 461–69. http://dx.doi.org/10.1016/0045-7949(94)90231-3.

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Panayirci, H. M., H. J. Pradlwarter, and G. I. Schuëller. "Efficient stochastic structural analysis using Guyan reduction." Advances in Engineering Software 42, no. 4 (April 2011): 187–96. http://dx.doi.org/10.1016/j.advengsoft.2011.02.004.

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DiazDelaO, F. A., S. Adhikari, E. I. Saavedra Flores, and M. I. Friswell. "Stochastic structural dynamic analysis using Bayesian emulators." Computers & Structures 120 (April 2013): 24–32. http://dx.doi.org/10.1016/j.compstruc.2013.01.013.

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Fujino, Tomoyuki, and Fabian C. Hadipriono. "Fuzzy Fault Tree Analysis for Structural Safety." Journal of Intelligent and Fuzzy Systems 4, no. 4 (1996): 269–80. http://dx.doi.org/10.3233/ifs-1996-4403.

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Möller, B., W. Graf, and M. Beer. "Fuzzy structural analysis using α-level optimization." Computational Mechanics 26, no. 6 (December 14, 2000): 547–65. http://dx.doi.org/10.1007/s004660000204.

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Zhang, Yue, and Guang-Yuan Wang. "Analysis of structural fuzzy random seismic response." Acta Seismologica Sinica 8, no. 2 (May 1995): 271–79. http://dx.doi.org/10.1007/bf02650491.

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Grzywiński, Maksym, and Iwona Pokorska. "Stochastic Analysis of Cylindrical Shell." Transactions of the VŠB – Technical University of Ostrava, Civil Engineering Series. 14, no. 1 (June 1, 2014): 38–41. http://dx.doi.org/10.2478/tvsb-2014-0005.

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Abstract:

Abstract The paper deals with some chosen aspects of stochastic structural analysis and its application in the engineering practice. The main aim of the study is to apply the generalized stochastic perturbation techniques based on classical Taylor expansion with a single random variable for solution of stochastic problems in structural mechanics. The study is illustrated by numerical results concerning an industrial thin shell structure modeled as a 3-D structure.

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Song, Xiaogang, Zhengjun Zhai, Peican Zhu, and Jie Han. "A Stochastic Computational Approach for the Analysis of Fuzzy Systems." IEEE Access 5 (2017): 13465–77. http://dx.doi.org/10.1109/access.2017.2728123.

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Chen, Ling, and Hongyong Zhao. "Stability analysis of stochastic fuzzy cellular neural networks with delays." Neurocomputing 72, no. 1-3 (December 2008): 436–44. http://dx.doi.org/10.1016/j.neucom.2007.12.005.

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Hu, L. "Analysis of dynamical systems whose inputs are fuzzy stochastic processes." Fuzzy Sets and Systems 129, no. 1 (July 1, 2002): 111–18. http://dx.doi.org/10.1016/s0165-0114(01)00073-2.

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Kumar, Mohit, Sebastian Neubert, Sabine Behrendt, Annika Rieger, Matthias Weippert, Norbert Stoll, Kerstin Thurow, and Regina Stoll. "Stress Monitoring Based on Stochastic Fuzzy Analysis of Heartbeat Intervals." IEEE Transactions on Fuzzy Systems 20, no. 4 (August 2012): 746–59. http://dx.doi.org/10.1109/tfuzz.2012.2183602.

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