Global Reliability Research Articles

Seismic reliability analysis of steel moment-resisting frames retrofitted by vertical link elements using combined series\u2013parallel system approach

The eccentric bracing system equipped with vertical links is capable of providing high levels of stiffness, strength and ductility, and therefore, can be efficiently used for seismic retrofit of existing structures. This study aims to investigate the seismic reliability of steel moment-resisting frames retrofitted by this system using a novel combined series–parallel system approach. The seismic response of 4, 8 and 12-storey steel moment-resisting frames (MRFs) are evaluated under a set of design basis earthquakes (DBE) before and after retrofitting intervention. Adopting an engineering demand parameter approach (EDP-Based) for reliability assessment and development of analytical models for the frames using systems consisting of the series–parallel elements are the major distinctions between the present study and the other similar works. To estimate the global reliability of the frames, first, the reliability of each storey is individually derived based on various probable damage levels for the lateral-load resisting members. Then, the seismic reliability of the frame is globally obtained by combining the reliability of each storey for different damage levels in the lateral load-resisting subsystems. The results indicate significant impact of this type of bracing system on improvement of the performance level and load-carrying capacity of the frames along with reduction of the lateral displacements. It is shown that application of the vertical links can reduce the maximum inter-storey drifts by at least 60%, while it leads to at most 17% increase in the base shear. All retrofitted frames exhibited a performance level higher than the Life Safety (LS) when subjected to the DBE hazard level records (earthquakes with return period of 475 years). At the same level of earthquake intensity, in the cases when the drift corresponding to the LS performance level is used as the target, the reliability of the retrofitted frames was improved by more than 90% compared to the original frames for all damage states developed in the vertical links.

Time-dependent mechanism reliability analysis with imprecise probability distributions based on envelope function

The time-dependent mechanism reliability and sensitivity analysis with imprecise probability distributions is investigated in this article. First, the interval model is employed to describe the uncertainty of the parameters of input variables due to the lack of data and knowledge. Next, the form of time-dependent failure probability and global reliability sensitivity (GRS) main effect index in the case of interval distribution parameters is analyzed. Then, the calculation methods of failure probability and GRS main effect index under the above circumstance are discussed. Finally, combined with the optimization algorithm, the Monte Carlo simulation (MCS) method and the method based on envelope function (MEVF) are proposed. By comparing with the MCS results, several numerical and engineering examples are analyzed to demonstrate the significance as well as the effectiveness of MEVF.

Development and Psychometric Testing of the Self-Care in COVID-19 (SCOVID) Scale, an Instrument for Measuring Self-Care in the COVID-19 Pandemic.

Aim: To develop the Self-Care in COVID-19 (SCOVID) scale and to test its psychometric characteristics in the general population. Methods: We tested SCOVID scale content validity with 19 experts. For factorial and construct validity, reliability, and measurement error, we administered the 20-item SCOVID scale to a sample of 461 Italians in May/June 2020 (mean age: 48.8, SD ± 15.8). Results: SCOVID scale item content validity ranged between 0.85–1.00, and the total scale content validity was 0.94. Confirmatory factor analysis supported SCOVID scale factorial validity (comparative fit index = 0.91; root mean square error of approximation = 0.05). Construct validity was supported by significant correlations with other instrument scores measuring self-efficacy, positivity, quality of life, anxiety, and depression. Reliability estimates were good with factor score determinacy, composite reliability, global reliability index, Cronbach’s alpha, and test-retest reliability ranging between 0.71–0.91. The standard error of measurement was adequate. Conclusions: The SCOVID scale is a new instrument measuring self-care in the COVID-19 pandemic with adequate validity and reliability. The SCOVID scale can be used in practice and research for assessing self-care in the COVID-19 pandemic to preventing COVID-19 infection and maintaining wellbeing in the general population.

A PDEM-based perspective to engineering reliability: From structures to lifeline networks

Research of reliability of engineering structures has experienced a developing history for more than 90 years. However, the problem of how to resolve the global reliability of structural systems still remains open, especially the problem of the combinatorial explosion and the challenge of correlation between failure modes. Benefiting from the research of probability density evolution theory in recent years, the physics-based system reliability researches open a new way for bypassing this dilemma. The present paper introduces the theoretical foundation of probability density evolution method in view of a broad background, whereby a probability density evolution equation for probability dissipative system is deduced. In conjunction of physical equations and structural failure criteria, a general engineering reliability analysis frame is then presented. For illustrative purposes, several cases are studied which prove the value of the proposed engineering reliability analysis method.

Reliability-based design optimization of adaptive sliding base isolation system for improving seismic performance of structures

The reliability-based design optimization (RBDO) of base-isolated structures by means of the state-of-the-art methods has been confronted with the high computational cost because the base isolation system often exhibits intrinsic nonlinearities and has to be modelled using a number of parameters in practice. In this regard, the present study aims at developing an efficient scheme for the RBDO of base isolation systems by integrating the probability density evolution method (for global reliability solution) and the variance-based sensitivity analysis (for design parameter reduction).To attain an adaptive seismic mitigation, the newly developed sliding implant-magnetic bearings are employed for constituting the base isolation system. It is shown that although the deployment of sliding implant-magnetic bearings allows a safer structure, the base isolation system still suffers from an unexpected failure probability of deflection under the rarely-occurring earthquake. As a result, the design optimization for strengthening the reliability of the base isolation system needs to be carried out. For identification of the critical design parameters to be optimized, the variance-based sensitivity analysis is performed, showing that the parameters of the exponential function that represents the Coulomb sliding friction associated with the sliding implant-magnetic bearing are mostly of concern. Furthermore, the RBDO of the base isolation system is carried out by employing the genetic algorithm. It is revealed that the global reliability of the base-isolated structure after optimization gains a significant improvement compared to that before optimization.

Global reliability sensitivity analysis by Sobol-based dynamic adaptive kriging importance sampling

The stochastic uncertainties affecting the models used to describe the behavior of structural/mechanical systems may give rise to unfavorable scenarios leading to failures. In this framework, the quantification of the failure probability is a recognized fundamental task for structural safety and reliability analyses. Unfortunately, the estimation of the failure probability of structural/mechanical systems is a computationally demanding task, especially when the failure is a rare event and the computer codes used to model the system response require large computational efforts. One major issue further complicates the estimation process, i.e., the parameters of the probability distributions of the random variables used to describe the uncertainties involved can, in turn, be imprecise, since they are typically estimated by means of statistical inference based on observations and engineering judgment. In this context, reliability sensitivity analysis aims at estimating the influence of this additional source of uncertainty on the system failure probability in order to assess the robustness of the system to the modeling of uncertainties. Intuitively, reliability sensitivity analyses may easily become prohibitive by standard sampling-based methods (e.g., Monte Carlo method), since a nested, second level of uncertainties is involved. To overcome this issue, in this work we embed the efficient AK-IS algorithm for estimating small failure probabilities within an original computational framework that allows to perform a Sobol-based, global sensitivity analysis of the failure probability at an affordable number of computer model evaluations. The algorithm is demonstrated with reference to two case studies of literature of structural/mechanical reliability, often used in the literature as benchmark tests.

Psychometric characteristics of the caregiver contribution to self-care of chronic illness inventory.

The purpose of the this study was to test the factorial structure, internal consistency reliability and concurrent validity of the Caregiver Contribution to Self-Care Chronic Illness Inventory. Existing measures of caregiver contribution to self-care are disease-specific or behaviour-specific; no generic measures exist. A cross-sectional study. Between April 2017 - December 2018, we enrolled a convenience sample of 358 patients with chronic illnesses and their caregivers. Patients completed the Self-Care of Chronic Illness Inventory while caregivers completed the Caregiver Contribution to Self-Care of Chronic Illness Inventory, a modification of the Self-Care of Chronic Illness Inventory, which includes three scales as follows: the Caregiver Contribution to Self-Care Maintenance, the Caregiver Contribution to Self-Care Monitoring and the Caregiver Contribution to Self-Care Management. Of each scale, we tested the factorial structure with confirmatory factor analysis and reliability with the factor score determinacy coefficient, the global reliability index for multidimensional scale and Cronbach's alpha. Also, we used Pearson's correlations for concurrent validity purposes. Confirmatory factor analysis supported the two-factor structure of the Caregiver Contribution to Self-Care Maintenance and Management scales and the one-factor structure of the Caregiver Contribution to Self-Care Monitoring scale. A simultaneous confirmatory factor analysis on the combined set of items supported the more general model (Comparative Fit Index=0.933). Reliability estimates ranged between 0.701 and 0.961 across the three scales. Concurrent validity of Caregiver Contribution to Self-Care of Chronic Illness Inventory with the Self-Care of Chronic Illness Inventory was not sufficiently supported since weak correlations were found. The Caregiver Contribution to Self-Care of Chronic Illness Inventory is valid and reliable and can be used in clinical practice and research. The Caregiver Contribution to Self-Care of Chronic Illness Inventory is an useful instrument to evaluate the extent to which caregivers contribute to patient self-care in chronic illnesses.

An Improved Method of Global Reliability Analysis

An Improved Method of Global Reliability Analysis

Parameter global reliability sensitivity analysis with meta-models: A probability estimation-driven approach

Parameter global reliability sensitivity (PGRS) reflects the influence of imprecise distribution parameters of basic model input variables on the variation of failure probability. It can be measured by the variance decomposition item of the failure probability function (FPF). The variance decomposition item of FPF can be expanded to the mean square of the difference between the unconditional expectation of the FPF and the conditional expectation of the FPF. To simplify the calculation, this paper uses the absolute value item of the difference to replace the square item of the difference. They both can reflect accumulation of the differences. To transform the direct triple-loop estimation of PGRS into a single-loop calculation, this paper firstly uses Bayes formula to transform the calculation of PGRS into a single-loop sample classification process and a failure-conditional probability density function (PDF) estimation. Secondly, by using the interval-conditional feature to approximate the point-conditional feature, the estimation of failure-conditional PDF for the conditional imprecise distribution parameter is substituted by estimating the interval conditional probability. Finally, the adaptive Kriging model widely used in reliability analysis is introduced in the proposed algorithm to classify samples efficiently. The proposed method only requires one matrix of samples to analyze the PGRS of all imprecise distribution parameters. The effectiveness of the proposed method is verified by three examples.

Evaluation of Precipitable Water Vapor from Five Reanalysis Products with Ground-Based GNSS Observations

At present, the global reliability and accuracy of Precipitable Water Vapor (PWV) from different reanalysis products have not been comprehensively evaluated. In this study, PWV values derived by 268 Global Navigation Satellite Systems (GNSS) stations around the world covering the period from 2016 to 2018 are used to evaluate the accuracies of PWV values from five reanalysis products. The temporal and spatial evolution is not taken into account in this analysis, although the temporal and spatial evolution of atmospheric flows is one of the most important information elements available in numerical weather prediction products. The evaluation results present that five reanalysis products with PWV accuracy from high to low are in the order of the fifth generation of European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis (ERA5), ERA-Interim, Japanese 55-year Reanalysis (JRA-55), National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR), and NCEP/DOE (Department of Energy) according to root mean square error (RMSE), bias and correlation coefficient. The ERA5 has the smallest RMSE value of 1.84 mm, while NCEP/NCAR and NCEP/DOE have bigger RMSE values of 3.34 mm and 3.51 mm, respectively. The findings demonstrate that ERA5 and two NCEP reanalysis products have the best and worst performance, respectively, among five reanalysis products. The differences in the accuracy of the five reanalysis products are mainly attributed to the differences in the spatial resolution of reanalysis products. There are some large absolute biases greater than 4 mm between GNSS PWV values and the PWV values of five reanalysis products in the southwest of South America and western China due to the limit of terrains and fewer observations. The accuracies of five reanalysis products are compared in different climatic zones. The results indicate that the absolute accuracies of five reanalysis products are highest in the polar regions and lowest in the tropics. Furthermore, the effects of different seasons on the accuracies of five reanalysis products are also analyzed, which indicates that RMSE values of five reanalysis products in summer and in winter are the largest and the smallest in the temperate regions. Evaluation results from five reanalysis products can help us to learn more about the advantages and disadvantages of the five released water vapor products and promote their applications.

Optical flow refinement using iterative propagation under colour, proximity and flow reliability constraints

This study proposes a strategy to refine optical flow based on the estimated reliability maps. These maps are firstly estimated a posteriori after the motion estimation by the well-known Kanade–Lucas–Tomasi (KLT). With two new defined criteria based, respectively, on the optical flow local variance and the temporal evolution of the KLT residuals, a global refinement of the motion map is then carried out through two stages under the control of the reliability measures and the colour local homogeneousness. According to the experiments performed on the Middlebury dataset, the authors' reliability measures prove to be a good indicator for the quality of the estimation. Indeed, the correction process increases the global reliability measures and reduces the global errors in a significant way. The experiments show that the quality is higher than classical estimation methods and ranked at 88/168 on Middlebury website

An efficient method for estimating fuzzy failure probability-based global fuzzy reliability sensitivity

The global fuzzy reliability sensitivity can measure the effect of input on fuzzy failure probability (FFP) through the expected difference between the unconditional FFP and the conditional one. Thus, an efficient method is proposed to estimate this sensitivity index. The proposed method includes two steps. First, the unconditional–conditional FFPs are equivalently transformed into the integrals of the unconditional–conditional failure probabilities (FPs) by an introduced variable related to the fuzzy state assumption. The unconditional–conditional FPs can be further expressed as the integrals of the unconditional–conditional probability density functions (PDFs) of the output. Secondly, the unconditional–conditional PDFs are solved using the maximum entropy theory constrained by the unconditional–conditional fractional moments. These fractional moments can be solved simultaneously through the multiplicative dimensional reduction using the same group of Gaussian quadrature points. The computational cost of the proposed method grows linearly with the dimensionality, and the computational efficiency is greatly improved.

"U-Sniff" - the international odor identification test for children: an extension of its normative database and study of global reliability.

To extend the previous study by Schriever and colleagues from 2018 providing normative data and re-investigating the reliability for U-Sniff test in children in additional countries. A total of 388 children (196 boys, 192 girls) from eight countries (China, Germany, Iran, Netherlands, Norway, Oman, Paraguay, and Russia) participated in this study. The children were recruited from public local schools in those particular countries. The odor identification ability was evaluated using the U-Sniff test, a 12-item odor identification test developed for children. In addition, reliability was examined using test-retest design in the children for each country. The mean U-Sniff test score across all children was 10.3 +- 1.7 points. Normative data were established. A high test-retest reliability of the U-Sniff test was demonstrated across the eight participating countries. The U-Sniff test for children exhibits a high test-retest reliability on a global scale.

An efficient method for estimating time-dependent global reliability sensitivity

Time-dependent global reliability sensitivity can quantify the effect of input variables in their whole distribution ranges on the time-dependent failure probability. To efficiently estimate it to help researchers control the time-dependent failure probability, a novel method is proposed. The proposed method transforms the estimation of unconditional-conditional time-dependent failure probabilities into that of the unconditional-conditional probability density functions (PDFs) of the minimum of time-dependent performance function. Firstly, the minimum of time-dependent performance function is evaluated by adaptive Kriging, and its unconditional-conditional fractional moments are estimated by multiplicative dimensional reduction method (M-DRM). Then, the maximum entropy (MaxEnt) constrained by these fractional moments is used to estimate the unconditional-conditional PDFs, on which the unconditional-conditional time-dependent failure probabilities can be obtained. Finally, the one-dimensional Gaussian quadrature is applied to estimate the time-dependent global reliability sensitivity indices. Due to the high efficiency of adaptive Kriging for estimating the minimum of time-dependent performance function, the avoidance of dimensional curse by M-DRM, and the high efficiency of MaxEnt constrained by fractional moments for estimating PDF, the proposed method can reduce the computational cost dramatically.

Developing an integrated approach to the assessment of student nurse competence using the Total Client Care (TCC) assessment tool

The aim of this study was to evaluate the validity and reliability of an integrated assessment of competence using the Total Client Care (TCC) assessment tool within an undergraduate Nursing course. The Total Client Care Assessment Tool aims to assess multiple competencies in an integrated way thereby mirroring the way in which registered nurses are expected to practice. TCC is a tool designed to assess the student's ability to provide holistic care to a client over a specified period of time. TCC measures the student's performance around four constructs, these are: Communication, Planning and Responding, Care Delivery and Assessing and Evaluating.G-theory analysis revealed satisfactory levels of global reliability on single use G co-efficient 0.90 although this dropped to 0.76 when used on eight occasions to assess the same students over a two-year period. Analysis of variance revealed that students and assessment occasions accounted for most of the variance. The TCC assessment tool is useful as it provides data about the student's performance when providing actual care. When used as part of a wider system of assessment involving triangulation of evidence from a number of sources the tool can support mentor judgements about the achievement of competence.

Validity and Reliability of the Caregiver Contribution to Self-Care of Heart Failure Index Version 2.

Lay caregivers are important in contributing to self-care of patients with heart failure (HF). The Caregiver Contributions to Self-Care of Heart Failure Index (CC-SCHFI) measures these contributions, but after developing the Situation-Specific Theory of Caregiver Contributions to Heart Failure Self-Care, the CC-SCHFI needed updating to reflect the theory. The aim of this study was to test the psychometric characteristics of the CC-SCHFI 2 that measures caregiver contributions (CC) to HF self-care with 3 scales: CC to self-care maintenance, CC to symptom perception, and CC to self-care management. This is a cross-sectional study. We tested the CC-SCHFI 2 with confirmatory factor analysis, internal consistency, item-total correlations, and test-retest reliability. With the CC-SCHFI 2, we also administered the Self-Care of Heart Failure Index v.7.2 to patients and the Caregiver Contribution to Heart Failure Self-Care Scale to caregivers to assess concurrent validity. A sample of 277 caregivers was enrolled (mean [SD] age, 52.7 [14.9] years; 70.4% female). In confirmatory factor analysis, each CC-SCHFI 2 scale had supportive fit indices: comparative fit index ranged between 0.94 and 0.95, and root mean square error of approximation ranged between 0.05 and 0.07. Internal consistency of the 3 scales was evident with a Cronbach α between .81 and .83 and a global reliability index between 0.79 and 0.86. Item-total correlations were all greater than 0.30. In concurrent validity testing, there were significant correlations between the CC-SCHFI 2 and the Self-Care of Heart Failure Index v.7.2 and the Caregiver Contribution to Heart Failure Self-Care Scale. Test-retest reliability showed intraclass correlation coefficients between 0.72 and 0.91. Testing of the CC-SCHFI 2 supported validity and reliability, indicating that the instrument can be used in clinical practice and research to evaluate CC to the self-care of patients with HF.

A novel reliability sensitivity analysis method based on directional sampling and Monte Carlo simulation

Local reliability sensitivity and global reliability sensitivity are required in reliability-based design optimization, since they can provide rich information including variable importance ranking and gradient information. However, traditional Monte Carlo simulation is inefficient for engineering application. A novel numerical simulation method based on Monte Carlo simulation and directional sampling is proposed to simultaneously estimate local reliability sensitivity and global reliability sensitivity. By suitable transformation, local reliability sensitivity and global reliability sensitivity can be estimated simultaneously as by-products of reliability analysis for Monte Carlo simulation method. The key is how to efficiently classify Monte Carlo simulation samples into two categories: failure samples and safety samples. Directional sampling method, a classical reliability analysis method, is more efficient than Monte Carlo simulation for reliability analysis. A novel strategy based on nearest Euclidean distance is proposed to approximately screen out failure samples from Monte Carlo simulation samples using directional sampling samples. In the proposed method, local reliability sensitivity and global reliability sensitivity are by-products of reliability analysis using the directional sampling method. Different from existing methods, the proposed method does not introduce hypotheses and does not require additional gradient information. The advantages of the Monte Carlo simulation and directional sampling are well integrated in the proposed method. The accuracy and the efficiency of the proposed method for local reliability sensitivity and global reliability sensitivity are demonstrated by four numerical examples and two engineering examples including the headless rivet and the wing box structure.

An Improved Structural Reliability Analysis Method Based on Local Approximation and Parallelization

The Kriging-based reliability method with a sequential design of experiments (DoE) has been developed in recent years for implicit limit state functions. Such methods include the efficient global reliability analysis, the active learning reliability method combining Kriging and MCS Simulations. In this research, a novel local approximation method based on the most probable failure point (MPFP) is proposed to improve such methods. In this method, the MPFP calculated in the last iteration is the center of the next sampling region. The size of the local region depends on the reliability index obtained by the First Order Reliability Method (FORM) and the deviation distance of the standard deviation. The proposed algorithm, which approximates the limit state function accurately near MPFP rather than in the whole design space, can avoid selecting samples in regions that have negligible effects on the reliability analysis results. In addition, a multi-point enrichment technique is also introduced to select multiple sample points in each iteration. After the high-quality approximation of limit state function is obtained, the failure probability is calculated by the Monte Carlo method. Four numerical examples are used to validate the accuracy and efficiency of the proposed method. Results show that the proposed method is very effective for an accurate evaluation of the failure probability.

Physical synthesis method for global reliability analysis of engineering structures

Global reliability analysis is quite essential for safety design and serviceability maintenance of modern engineering structures. Various reliability analysis methods have been proposed though, the issues of combination explosion as well as probabilistic correlation of system failure paths remain challenging. Practical applications of these methods are thus hindered, especially when complex, implicit, and nonlinear performance functions are involved. There isn’t an efficient way out of this dilemma until the emergence of the probability density evolution method (PDEM) combined with the equivalent extreme value event principle in recent years. The governing partial differential equations regarding the probability density evolution of a stochastic dynamical system are decoupled to a one-dimensional generalized probability density evolution equation (GPDEE) by employing the random event description. In conjunction with the PDEM, this paper explores a physical synthesis approach towards the global reliability analysis of actual engineering structures. Evaluation of multiple failure potential is incorporated into the mathematical resolving of the GPDEE for structural response at a representative point. An accurate and efficient solving procedure for the global reliability of probabilistic dissipative systems is presented where no need of seeking the failure paths and their associated probabilities. Numerical applications are examined to illustrate the use of the physical synthesis method as a practical analysis tool.

Global reliability sensitivity analysis based on state dependent parameter method and efficient sampling techniques

In order to efficiently assess the influence of input variables on the failure of the structural systems, an improved global reliability sensitivity analysis (SA) method is proposed in this paper. The new method is based on the state dependent parameter (SDP) method and the efficient sampling techniques. In the new method, the efficient sampling techniques are first used to generate samples that are more efficient for reliability analysis, and then the SDP method is further employed to estimate the global reliability sensitivity index by using the same set of samples as the reliability analysis. Two efficient sampling methods, e.g., importance sampling (IS) and truncated importance sampling (TIS), are employed in this paper, and the strategies of combining these methods with the SDP method for global reliability SA are discussed. Compared with the existing SDP method, the new method is more efficient for global reliability SA of structural systems. Three examples are used in the paper to demonstrate the efficiency and precision of the new methods.