Section 8 7: Scale Reliability Statistics for Research Students

The management of infrastructure involves accounting for factors which vary in space over the system domain and in time as the system changes. Effective system management should be guided by models which account for uncertainty in these influencing factors as well as for information gathered to reduce this uncertainty. In this paper, we address the problem of optimal information collection for spatially distributed dynamic infrastructure systems. Based on prior information, a monitoring scheme can be designed, including placement and scheduling of sensors. This scheme can be adapted during the management process, as more information becomes available.

multi-scale reliability analysis

Compared to the traditional convex model, the constructed multi-CEM has a rigorous but understandable form, and is more effective for handling the uncertainty with complex distribution. Recent earthquake events evidenced that damage of structural components in a lifeline network may cause prolonged disruption of lifeline services, which eventually results in significant socio-economic losses in the affected area. Despite recent advances in network reliability analysis, the complexity of the problem and various uncertainties still make it a challenging task to evaluate the post-hazard performance and connectivity of lifeline networks efficiently and accurately. In order to overcome such challenges and take advantage of merits of multi-scale analysis, this paper develops a multi-scale system reliability analysis method by integrating a network decomposition approach with the matrix-based system reliability method. The proposed multi-scale analysis method is demonstrated by its application to a gas distribution network in Shelby County of Tennessee.

Numerical Examples and Discussion

In order to demonstrate the proposed approach, a 3-story shear-type building equipped with an optimal active control device is considered. The control performance under uncertainties is investigated through the reliability assessment by the proposed approach and the Monte Carlo Simulation approach, respectively. The numerical study also investigates the influence of the uncertainties in the system parameters and the earthquake excitations on the system failure probability. The results of the numerical examples demonstrate that the proposed approach can efficiently estimate the system reliability and the failure probability of an actively-controlled structure. An efficient FEA-based multi-scale reliability framework used in this study is extended and combined with a proposed sequential optimisation strategy to produce an efficient, flexible and accurate RBDO framework for fibre-reinforced composite laminate components.

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If dealing with multiple constructs or factors that cluster within a higher order factor, Cronbach’s alpha should be run for both the total scale, and the items in each factor. This is most often done by using a type of reliability called internal consistency reliability, which is based on formulas that give an index of how much variability is shared and accounted for by the set of items, thus reflecting their degree of interrelationship. High internal consistency reliability reflects that items are consistent with other items in the set, and that the items are measuring the same construct. The coefficient alpha, or Cronbach’s alpha, is the average of all possible split-half coefficients resulting from different ways of splitting the scale items. This coefficient varies from 0 to 1, and a value of 0.6 or less generally indicates unsatisfactory internal consistency reliability. Reliability measures are one of the key elements of the scale evaluation process.

An enhanced PDEM-based framework for reliability analysis of structures considering multiple failure modes and limit states

Through Gaussian clustering analysis and EM algorithm, the optimal Gaussian function for these additional samples is constructed, whose parameters are listed in Table 5. Via determining the critical contour ellipsoid, the AEM is then constructed. This chapter presents a state-of-the-art on fragility models for the components of Electric Power Networks available in the technical literature. First, the main characteristics of an electric power network and its taxonomy are introduced. Then, the main recent works on fragility functions of electric components are listed, and details are provided for a few selected ones.

multi-scale reliability analysis

For instance, do students’ scores in a calculus class correlate well with their scores in a linear algebra class? These scores should be related concurrently because they are both tests of mathematics. Unlike convergent and discriminant validity, concurrent and predictive validity is frequently ignored in empirical social science research.

Reliability Engineering & System Safety

And then the high precision direct integration method is also employed to reduce the large number of transient analysis into two times for overcoming the challenges of strenuous computing effort. The time-dependent pounding system probability of bridges is derived by the combination of extreme value distribution theory and matrix-based system probability theory. Finally based on the proposed time-dependent probability method the numerical analysis of a real two span simply-supported beam bridge subjected to spatial and non-stationary ground motions is conducted herein for the pounding risk assessment. The results of this study provides in-depth insight into the time-dependent pounding probability of bridge systems subjected to spatially varying and non-stationary ground motions.

multi-scale reliability analysis

Liu et al. suggested a pseudo-probabilistic measure method which combined the multidimensional volume ratio with the first-order approximation of the system-state function. Furthermore, by formulating the constraints in terms of NPR index, a series of reliability-based design and optimization methods have also been proposed . Yet, although structural reliability analysis methods using the ellipsoidal model have been developed and enriched by many researchers, the distribution property or clustering status of uncertainty samples was rarely considered. This may lead to failure of the non-probabilistic convex models to describe the uncertain variables with multi-cluster property effectively. Meanwhile, the calculated results of reliability analysis may also be misleading, and then a risky or over conservative design would be obtained.

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Ben-Haim Y. A non-probabilistic measure of reliability of linear systems based on expansion of convex models. Luo Y., Kang Z., Luo Z., Li A. Continuum topology optimization with non-probabilistic reliability constraints based on multi-ellipsoid convex model. Liu J., Liu H., Jiang C., Han X., Zhang D.Q., Hu Y.F. A new measurement for structural uncertainty propagation based on pseudo-probability distribution.

  • One of the primary sources is the observer’s (or researcher’s) subjectivity.
  • To estimate the Cronbach’s alpha of the BSS, go to the Analyze menu and select Scale → Reliability Analysis….
  • Each factor contains 4 questions , totalling 20 questions (i.e. 5 factors x 4 questions for each factor).
  • The purpose of this study is to enable performing reliability-based design optimisation for a composite component while accounting for several multi-scale uncertainties using a large representative volume element .
  • It is worth noting here that when there are multiple uncertain variables, the intersection of the multi-CEM components indeed can be complex, which, unavoidably, cannot be observed intuitively and accurately.

One is that there is no intersection between the components of the multi-CEM, as shown in Figure 1a, and the other is that there is an intersection between the components, as shown in Figure 1b. In this section, the two situations will be investigated for structural reliability analysis, respectively. With the rapid development of science and technology, the designed structures are not only required to meet the functional requirements, but also expected to have high reliability. Yet, in engineering practice, due to the complexity of actual structures, the discreteness of used materials, and the multi-scale analysis manufacturing and installation errors, the physical, geometric, and boundary characteristics of the structure inevitably suffer a certain level of uncertainty. If the relevant variables of the structure are still regarded as unique and deterministic, the designed structure may have a significant deviation from the expected one, resulting in weakening of its service effect, shortening of service life and even failure of basic functions. Therefore, to perform reliability analysis, with taking into account the uncertain variables, before structural design turns to be quite necessary and valuable.

Multi-scale reliability analysis and updating of complex systems by use of linear programming

Generally, the longer is the time gap, the greater is the chance that the two observations may change during this time , and the lower will be the test-retest reliability. A reliability analysis assumes that there is only one factor and that all variables you use are weighted the same. Liu J., Meng X.H., Xu C., Zhang D.Q., Jiang C. Forward and inverse structural uncertainty propagations under stochastic variables with arbitrary probability distributions. Where x1 and x2 are the two uncertain-but-bounded variables, and a means the threshold value.

multi-scale reliability analysis

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