With the rapid development of reliability analysis theory, the safety assessment for engineering systems with hybrid epistemic uncertainties has received increasing attentions. To overcome the ...imperfection of traditional single-uncertainty modeling methods, this paper introduces evidence variables and fuzzy variables simultaneously to describe the epistemic uncertain parameters. A novel dual-stage reliability analysis framework is presented, where the first stage incorporates the evidence information by the belief and plausibility measures; the second stage incorporates the fuzzy information by a membership function-like formula. To improve the computational efficiency of response bound prediction associated with various joint focal elements and λk-cut interval variables, a universal metamodel is constructed by radial basis functions in the global design space. The Latin Hypercube design method without overlapped data is adopted as the sampling strategy. Finally, two numerical examples verify the effectiveness of the proposed method in both mathematical theory and engineering application.
•Hybrid epistemic uncertainties are quantified by evidence variables and fuzzy variables simultaneously.•A dual-stage reliability analysis framework is constructed to evaluate the system safety.•Metamodel technique is employed with the aid of radial basis function and Latin Hypercube design.
With the development of reliability technique, the safety assessment for the problem with epistemic uncertainty has attracted widespread attention. Evidence theory is a useful tool to deal with such ...uncertainty, and this paper aims to develop an efficient approach for the evidence theory-based reliability analysis and optimization design. By using mutually exclusive intervals to quantify the focal elements of evidence variable, a confidence range bounded by belief measure and plausibility measure is derived for system reliability assessment, by which the relatively conservative and radical optimization models can be respectively established. To decrease the huge computational burden in repetitive limit state function evaluations under the time-consuming implicit computational model, an explicit surrogate model is constructed by the Legendre polynomial chaos expansion in the support box. A Clenshaw–Curtis point-based collocation method with Smolyak algorithm is then developed to predict the unknown coefficients in surrogate model, where the collocation level can be flexibly selected according to the accuracy requirement. Compared with the traditional deterministic optimization model under nominal value assumption, the results in two numerical examples verify the effectiveness of proposed method in mathematical theory and engineering application.
As a typical convex model, the parallelepiped plays an important role in the non-probabilistic uncertainty quantification with simultaneous dependent and independent variables. To overcome the ...complexity of the conventional geometric design-based method, this paper proposes a more efficient sample-driven procedure to construct the explicit mathematical expression of parallelepiped model. Instead of the geometric characteristics of minimum-volume parallelepiped, the statistical characteristics of available samples are employed to directly evaluate the marginal intervals and correlation coefficients of uncertain variables. Especially for the inconstant uncertainty problem with dispersed samples, a sub-parallelepiped modeling method is further presented by means of the sample clustering analysis, which can effectively decrease the invalid domains in uncertainty quantification. Besides, in order to improve the computing efficiency of uncertainty propagation analysis under the parallelepiped model, the radial basis function-based surrogate model is introduced as an approximation of the original time-consuming computational model. Finally, two numerical examples verify the effectiveness of the proposed model and method.
•Parallel-type and embedded-type interval-random models are presented to quantify hybrid uncertainties.•A universal numerical approach is proposed for hybrid uncertainty propagation analysis.•The ...output response in hybrid circumstance is interpreted as an interval number with random bounds.•A cross validation-based adaptive surrogate model is constructed to improve the computational cost.
A wide variety of uncertainty propagation methods have been developed to deal with the single uncertainty; however, different kinds of uncertainties may exist simultaneously in many engineering practices. By using random variables and interval variables to quantify the probabilistic and non-probabilistic uncertainties respectively, this paper proposes two different interval-random models and a universal numerical approach for hybrid uncertainty propagation analysis. In the first model, uncertain parameters are treated as either random variables or interval variables with deterministic distributions, which exist independently. In the second model, uncertain parameters are quantified as random interval variables, where the bounds of interval variables are expressed as random variables instead of deterministic values. In both models, the effect of input hybrid uncertainties on output response is interpreted by an interval number with random bounds. To predict the moments of random bounds of interval response, a double-loop numerical analysis framework is constructed, where the outer loop is executed to traverse the discrete points for random variables and the inner loop is implemented to capture the response extreme values for interval variables. To further solve the computationally expensive issue caused by the full-scale finite element simulations, a cross validation-based adaptive surrogate model is introduced as an approximation, which can achieve an acceptable accuracy through a small number of sample points. Finally, a transient heat conduction example demonstrates the feasibility of the proposed models and method.
The field of spondyloarthritis (SpA) has experienced major progress in the last decade, especially with regard to new treatments, earlier diagnosis, imaging technology and a better definition of ...outcome parameters for clinical trials. In the present work, the Assessment in SpondyloArthritis international Society (ASAS) provides a comprehensive handbook on the most relevant aspects for the assessments of spondyloarthritis, covering classification criteria, MRI and x rays for sacroiliac joints and the spine, a complete set of all measurements relevant for clinical trials and international recommendations for the management of SpA. The handbook focuses at this time on axial SpA, with ankylosing spondylitis (AS) being the prototype disease, for which recent progress has been faster than in peripheral SpA. The target audience includes rheumatologists, trial methodologists and any doctor and/or medical student interested in SpA. The focus of this handbook is on practicality, with many examples of MRI and x ray images, which will help to standardise not only patient care but also the design of clinical studies.
This work tackles the issue of identifiability of fracture and bond properties in reinforced concrete. The basis for modeling of fracture is a computational model capable of describing damage and ...failure mechanisms in concrete, as well as bond‐slip which is a result of degradation of the concrete‐steel interface. The discrete approximation combines ED‐FEM for concrete crack representation in each element and X‐FEM representation of bond‐slip along a particular reinforcement bar. The uncertain model parameters are modeled as random variables and identified via Bayesian inference with the help of observations from tensile tests on concrete tie beams with a single embedded reinforcement bar. We discuss how the choice of observation type affects the parameter identifiability and propose combinations which improve the estimation capabilities and reduce the discrepancy between the computed and observed quantities of interest.
As a typical method for hybrid uncertainty analysis, imprecise probability theory recently plays an increasing important role in many engineering systems. To extend its application, this paper ...proposes a new imprecise probability model and a more efficient numerical method for hybrid uncertainty propagation. Instead of crisp real values, fuzzy sets with membership functions are utilized to describe the epistemic uncertainty in the distribution parameters of input random variables. In the presence of fuzzy distribution parameters, the conventional random moments of output response will include certain fuzzy characteristic. Based on the cut-set operation and fuzzy decomposition theorem, the calculation of fuzzy random moment can be transformed into a series of λ-cut interval random moments. To further decrease the huge computational cost caused by the original complex computational model, a relatively simple metamodel with explicit expression is constructed by the radial basis functions. Meanwhile, the bisection points with nestedness property are adopted as the experimental design strategy. Finally, two examples are implemented to verify the effectiveness of the proposed model and method in practical application.
•A new random model is presented considering the fuzzy distribution parameters.•A dual-stage framework is constructed for hybrid uncertainty propagation.•Radial basis function-based metamodel is used to improve computational cost.•Bisection points with nestedness property are adopted in the experimental design.
Due to the aggressive and changing environmental conditions, various time-varying uncertainties widely exist in many engineering heat transfer problems. This paper introduces a non-probabilistic ...interval process model to characterize the time-varying uncertainty with limited information, whose lower and upper bounds are quantified as time-dependent functions instead of constant values. Meanwhile, two numerical methods, named as Monte Carlo method under interval process model (MCM-IP) and sensitivity analysis method under interval process model (SAM-IP), are presented for uncertain temperature response prediction. In MCM-IP, the temperature response bounds are directly simulated via substantial sample processes, which are constructed by the interpolation methods from the discrete interval samples. To avoid the huge computational cost of MCM-IP caused by a large number of sample processes, SAM-IP carries out the monotonicity prejudgment via sensitivity analysis, by which only two sample processes are constructed for response bounds prediction. Finally, MCM-IP and SAM-IP are comparatively investigated in a mathematical example and an engineering example, by which the effectiveness of the proposed model and methods are verified.
•Transient heat transfer problem with non-probabilistic uncertainty is studied.•Time-varying uncertainty is quantified by interval process instead of interval variable.•Monte Carlo method is extended to interval process by assigning sample serial numbers.•Sensitivity analysis and monotonicity prejudgment are carried out to improve the computational efficiency.
Magnetic resonance imaging (MRI) of sacroiliac joints has evolved as the most relevant imaging modality for diagnosis and classification of early axial spondyloarthritis (SpA) including early ...ankylosing spondylitis.
To identify and describe MRI findings in sacroiliitis and to reach consensus on which MRI findings are essential for the definition of sacroiliitis.
Ten doctors (two radiologists and eight rheumatologists) from the ASAS/OMERACT MRI working group reviewed and discussed in three workshops MR images depicting sacroiliitis associated with SpA and other conditions which may mimic SpA. Descriptions of the pathological findings and technical requirements for the appropriate acquisition were formulated. In a consensual approach MRI findings considered to be essential for sacroiliitis were defined.
Active inflammatory lesions such as bone marrow oedema (BMO)/osteitis, synovitis, enthesitis and capsulitis associated with SpA can be detected by MRI. Among these, the clear presence of BMO/osteitis was considered essential for defining active sacroiliitis. Structural damage lesions such as sclerosis, erosions, fat deposition and ankylosis can also be detected by MRI. At present, however, the exact place of structural damage lesions for diagnosis and classification is less clear, particularly if these findings are minor. The ASAS group formally approved these proposals by voting at the annual assembly.
For the first time, MRI findings relevant for sacroiliitis have been defined by consensus by a group of rheumatologists and radiologists. These definitions should help in applying correctly the imaging feature "active sacroiliitis by MRI" in the new ASAS classification criteria for axial SpA.
The structural control of concrete gravity dams is of primary importance. In this context, numerical models play a fundamental role both to assess the vulnerability of gravity dams and to control ...their behaviour during normal operativity and after extreme events. In this regard, data monitoring represents an important source of information for numerical model calibrations.
This study proposes a novel probabilistic procedure, defined in the Bayesian framework, to calibrate the parameters of finite elements models of dams. To this aim, monitoring data and the results of material tests are used as reference information. The computational burden is reduced by using a new hybrid-predictive model of the dam displacements. An application on an Italian dam shows the feasibility of the proposed procedure.
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