Low Probability Levels Research Articles

Analysis of impulse withstand voltage of ester-based nanofluids

Ester-based nanofluids constitute an effective ecological alternative to petroleum-derived dielectric fluids for electrical engineering industry. Pulse induced dielectric breakdown is a key cause of power device failure. In this paper, we deal with the lightning impulse breakdown voltage (LI BDV) in natural and synthetic ester-based (NE and SE) nanofluids with various concentrations of magnetite (Fe3O4) and fullerene (C60) nanoparticles. The nanofluids show a decrement in positive polarity LI+ BDV as compared with the base ester. Negative polarity LI- BDV in the nanofluids is similar to that of base oils. Weibull statistical distributions are applied to analyse the results at different cumulative probabilities of 1 %, 10 %, and 50 %. Natural ester with C60 nanoparticles shows higher positive LI+ BDV values at a low probability level, while the negative LI- BDV reaches higher values at higher probability levels. The decreased LI BDV in NE and SE due to the magnetite nanoparticles are attributed to the nanoparticle clustering and percolation. The tendency of C60 nanoparticles to enhance the LI BDV in NE but not in SE is interpreted in the view of the nanoparticle concentration and oil's viscosity effect on the streamer branching and propagation.

Analisis Risiko IT untuk Pemanfaatan Tools Genogram pada Health care

This research analysis the genogram tool uses to record a person's health history as a medium for supporting health. This research analyzes the risks that might occur in a polyclinic if a genogram tool is implemented. The analysis process is through a risk identification process then a risk assessment using the COBIT for Risk IT method as IT resource mapping, then qualitative analysis as a method of mapping possibilities and impacts according to FGDs with organizations which can be used as a guide in assessing risks. level. Meanwhile, bow tie analysis is a risk mapping method that allows risk recovery so that it can serve as a guide for recovery. The results of risk analysis using the Genogram tool show several conditions, namely low probability but high level of impact, low probability but medium impact, and high probability of impact.

Implementing Reliability Analysis of a Soil Slope using First-Order Second Moment Method, Monte-Carlo Simulation Method and Subset Simulation Method in a MS-Excel Spreadsheet

The spatial variation in the soil properties affects the analysis and design of geotechnical structures such as slope stability analysis. This paper presents the reliability analysis of a soil slope using three different methods namely, first-order second moment (FOSM) method, Monte-Carlo simulation (MCS) method and Subset simulation (SS) method. The analysis has been carried out by considering the spatial variation of the soil properties of the slope. The correlation function and the correlation distance (λ) for the uncertain parameters are modelled using random field theory developed by Vanmarcke. The factor of safety (FOS) of the slope is determined using Ordinary method of slices. The reliability based probabilistic slope stability analysis has been carried out in a MS-Excel spreadsheet to determine the probability of failure (P_f ) and reliability index (β) of the slope. The spreadsheet mainly consists of three parts i.e., deterministic analysis, uncertainty analysis and uncertainty propagation. The study shows that the SS method has shown better performance as compared to FOSM method and MCS method especially at low failure probability levels. Also, SS method ensures the generation of samples in the failure region which is not always possible in MCS method.

Pipelines reliability assessment considering corrosion-related failure modes and probability distributions characteristic using subset simulation

A methodology using the subset simulation and the modified Metropolis-Hastings algorithm with delayed rejection (SS-MMHDR) is developed to evaluate the time-dependent reliability of corroded pipelines under multiple failure modes. This methodology considers the competition between the small leak and burst failures and the correlated non-normal distribution of the defect variables, and characterizes the defect geometric probability distribution from extensive inspection data. Results demonstrate that SS-MMHDR has high accuracy and low variability in evaluating the pipeline failures, outperforming the SS-MMH and Monte Carlo simulation algorithms, especially under low probability levels, but with slightly increased computation time. The Generalized Extreme Value Type II (GEVII) distribution is the optimal distribution for internal corrosion defect lengths from the Kolmogorov-Smirnov test. Lognormal, Weibull and Normal distributions provide more conservative estimates on burst and composite failure probabilities and limiting maintenance times than GEVII. Effects of operating pressure and its variability, corrosion rate on failure probability, and pipeline limiting maintenance time are also revealed through numerical cases.

Data augmentation for CNN-based probabilistic slope stability analysis in spatially variable soils

A novel methodology that involves the coupling of Convolutional Neural Networks (CNNs) and a data augmentation technique is proposed for slope reliability calculations. The methodology starts from generating a small set of random field samples, which are then calculated using the shear strength reduction method in the finite-difference scheme to obtain the associated factors of safety. Based on the theoretical relationship between the factor of safety and soil property values derived from the underlying mechanism of the shear strength reduction method, an innovative data augmentation technique is developed to enhance both the quantity and comprehensiveness of the dataset. A CNN model is then trained using the augmented dataset to learn the relationship between the factors of safety and random fields and predict the probability of slope failure. The effectiveness of the methodology is illustrated and validated using a c-φ soil slope and a multi-layered Su slope. The results show that CNNs are effective in interpreting high-dimensional random fields. In addition, through using the data augmentation technique, not only has the predictive capability of the CNN model been effectively boosted, particularly for cases involving low probability levels of failure, but the computational efficiency of the slope reliability analysis has also been improved.

Extreme springing response statistics of a tethered platform by deconvolution

The research examines the motion response and hydrodynamic wave loads of a deep-water Tension Leg Platform (TLP), emphasising the impacts of the wave sum frequency on the restrained modes of heave, roll, and pitch. The stochastic TLP structural reaction in a random sea state was precisely computed using a Volterra series representation of the TLP corner vertical displacement, which was selected as a response process. The wave loading was evaluated using the second-order diffraction code WAMIT and applied to a linear damped mass-spring model representing the dynamic system. Then, platform displacement response at the design low probability level has been determined using a novel deconvolution approach. Since the Volterra series represented the analytical solution, the exact Volterra and the approximated predictions have been compared in this study. The latter provided an accurate way to validate the effectiveness and precision of the proposed novel deconvolution method. Compared to existing engineering techniques, the most attractive advantage of the proposed deconvolution method is that it does not rely on any pre-assumed asymptotic probability distribution class. The latter may be an attractive point for practical engineering design. Thus the primary objective of this work was to validate a novel deconvolution approach using exact quasi-analytical solutions. This work also highlights the limitations of mean up-crossing rate-based extrapolation methodologies for the situation of narrowband effects, including clustering, which are often included in the springing type of response.

Offshore tethered platform springing response statistics

This paper demonstrates the validity of the Naess–Gadai method for extrapolating extreme value statistics of second-order Volterra series processes through application on a representative model of a deep water small size tension leg platform (TLP), with specific focus on wave sum frequency effects affecting restrained modes: heave, roll and pitch. The wave loading was estimated from a second order diffraction code WAMIT, and the stochastic TLP structural response in a random sea state was calculated exactly using Volterra series representation of the TLP corner vertical displacement, chosen as a response process. Although the wave loading was assumed to be a second order (non-linear) process, the dynamic system was modelled as a linear damped mass-spring system. Next, the mean up-crossing rate based extrapolation method (Naess–Gaidai method) was applied to calculate response levels at low probability levels. Since exact solution was available via Volterra series representation, both predictions were compared in this study, namely the exact Volterra and the approximate one. The latter gave a consistent way to estimate efficiency and accuracy of Naess–Gaidai extrapolation method. Therefore the main goal of this study was to validate Naess–Gaidai extrapolation method by available analytical-based exact solution. Moreover, this paper highlights limitations of mean up-crossing rate based extrapolation methods for the case of narrow band effects, such as clustering, typically included in the springing type of response.

Prospective Fault Displacement Hazard Assessment for Leech River Valley Fault Using Stochastic Source Modeling and Okada Fault Displacement Equations

In this study, an alternative method for conducting probabilistic fault displacement hazard analysis is developed based on stochastic source modeling and analytical formulae for evaluating the elastic dislocation due to an earthquake rupture. It characterizes the uncertainty of fault-rupture occurrence in terms of its position, geometry, and slip distribution and adopts so-called Okada equations for the calculation of fault displacement on the ground surface. The method is compatible with fault-source-based probabilistic seismic hazard analysis and can be implemented via Monte Carlo simulations. The new method is useful for evaluating the differential displacements caused by the fault rupture at multiple locations simultaneously. The proposed method is applied to the Leech River Valley Fault located in the vicinity of Victoria, British Columbia, Canada. Site-specific fault displacement and differential fault displacement hazard curves are assessed for multiple sites within the fault-rupture zone. The hazard results indicate that relatively large displacements (∼0.5 m vertical uplift) can be expected at low probability levels of 10−4. For critical infrastructures, such as bridges and pipelines, quantifying the uncertainty of fault displacement hazard is essential to manage potential damage and loss effectively.

Seismic reliability evaluation of bridges under spatially varying ground motions using a four-parameter distribution

Large-span bridges are often subjected to spatially varying ground motion (SVGM) excitations. Meanwhile, bridge structures and ground motion excitations generally present a large degree of inherent uncertainties. Carrying out the seismic reliability analysis of bridges under SVGMs can provide a direct assessment for the safety probability of bridges during an earthquake event and is of paramount importance for the probabilistic performance analysis of bridges. To this end, this study proposes a new method for the seismic reliability evaluation of random nonlinear bridges subjected to SVGMs, which tackles the drawback of the current seismic reliability analysis method that can only be applied for uniform seismic excitations. To implement, the space correlated ground motions are first generated via a random function-based spectral representation method, based on which the uncertainty of ground motions is characterized by three elementary random variables. The first-passage probability of bridges under SVSGMs is then derived by the equivalent extreme value distribution (EVD), which is approximated by a flexible four-parameter distribution, i.e., shifted generalized lognormal distribution (SGLD). To estimate the parameters in the SGLD model with high efficiency and accuracy, a novel fractional moment-based method is proposed. A numerical example of a two-span continuous box-girder bridge subjected to space varying site conditions is investigated to verify and exemplify the proposed method. The results demonstrate that: 1) The proposed method is efficient and accurate for the seismic reliability of bridge under SVGM, even at low failure probability levels, and the computational burden of the proposed method is less than 2% of that of MCS; 2) The spatial variability is significantly influential on the seismic reliability of the bridge, ignoring the spatial variability of seismic excitation will significantly underestimate the failure probability of the bridge.

Blind Recognition of Forward Error Correction Codes Based on a Depth Distribution Algorithm

Forward error correction codes (FEC) are one of the vital sections of modern communication systems; therefore, recognition of the coding type is an important issue in non-cooperative communication. At present, the recognition of FEC codes is mainly concentrated in the field of semi-blind identification with known types of codes. However, based on information asymmetry, the receiver cannot know the types of channel coding previously used in non-cooperative systems such as cognitive radio and remote sensing of communication. Therefore, it is important to recognize the error-correcting encoding type with no prior information. Although the traditional algorithm can also recognize the type of codes, it is only applicable to the case without errors, and its practicability is poor. In the paper, we propose a new method to identify the types of FEC codes based on depth distribution in non-cooperative communication. The proposed algorithm can effectively recognize linear block codes, convolutional codes, and Turbo codes under a low error probability level, and has a higher robustness to noise transmission environment. In addition, an improved matrix estimation algorithm based on Gaussian elimination was adopted in this paper, which effectively improves the parameter identification in a noisy environment. Finally, we used a general framework to unify all the reconstruction algorithms to simplify the complexity of the algorithm. The simulation results show that, compared with the traditional algorithm based on matrix rank, the proposed algorithm has a better anti-interference performance. The method proposed is simple and convenient for engineering and practical applications.

Evaluation of damage probability of railway embankments in permafrost regions in Qinghai–Tibet Plateau

The Qinghai–Tibet Railway (QTR) has been operating since 2006. It crosses continuous permafrost regions in the Qinghai–Tibet Plateau. Half of these regions are characterized by high ground temperature and ice content, and slight changes in the permafrost may cause embankment damage. In this study, the deformed embankment data of the QTR collected from 2010 to 2018 were analyzed. The analysis results showed that the total length of deformed embankments of the QTR in the permafrost regions was 22.97 km, and the damage rate since its completion in 2006 was approximately 5.4%. The results also indicated that the embankments stabilized over time. In 2010, the deformed embankments that were reinforced consisted of 26 sites and were 7.64-km long, whereas in 2018, only a 0.48-km long site was reinforced. In this study, a damage probability model of the embankments is developed. The damage probability of the embankments along the QTR in permafrost regions was classified into five grades: slight, low, moderate, high, and extremely-high damage probability levels. The total lengths of embankment sections with slight, low, moderate, high, and extremely-high levels of damage probability are 190.38, 161.42, 61.72, 13.51, and 2.99 km, respectively. It is suggested that damage-free embankment sections with high and extremely-high levels of damage probability should be monitored more closely during railway embankment maintenance. This study provides a reference for the application of the logistic regression model to establish the damage probability model of railway embankments in permafrost regions. • Statistically analysis of ten years’ worth of monitoring and reinforcement data of QTR embankments. • Distribution of deformed QTR embankments and embankment damage rate in permafrost regions. • Causes of embankment deformation, case records of reinforcement and maintenance measures. • Model to evaluate the damage probability of QTR embankments in permafrost regions.

Changes in the Occurrence of Late Spring Frost in Poland

Trends in the appearance of the last spring frost for three thresholds of minimum daily air temperature at the height of 2 m and near the ground were examined for six meteorological stations located in two agricultural regions in Poland. For most time series, the last spring frost, calculated as a consecutive day of the year, showed a statistically significant trend indicating its earlier appearance from 1.6 to about 3.5 days per decade. The date of the last spring frost was also calculated in relation to the ongoing growing season. In this case, few statistically significant changes in the dates of the last frosts were found. The probability of the last spring frost on a specific day of the calendar year and the day of the growing season was also examined for two periods: 1961–1990 and 1991–2020. For low probability levels corresponding to the early dates of the last spring frost, the last frost usually occurred much earlier (6–14 days) in 1991–2020. With the probability levels of 80–90% describing the late occurrence of the last frost with a frequency of once every 5–10 years, at some stations, the last spring frosts occurred at a similar time for both periods.

An advanced residual error model for tropospheric delay estimation

Global navigation satellite systems (GNSS) are widely used for safety-of-life positioning applications. Such applications require high integrity, availability, and continuity of the positioning service. Integrity is assessed by the definition of a protection level, which is an estimation of the maximum positioning error at extremely low probability levels. The emergence of multi-frequency civilian signals and the availability of satellite-based augmentation systems improve the modeling of ionospheric disturbances considerably. As a result, in many applications the tropospheric delay tends to become one of the limiting factors of positioning—especially at low elevation angles. The currently adopted integrity concepts employ a global constant to model the variance of the residual tropospheric delay error. We introduce a new approach to derive residual tropospheric delay error models using the extreme value analysis technique. Seventeen years of global numerical weather model fields are analyzed, and new residual error models are derived for some recently developed tropospheric delay models. Our approach provides models that consider both the geographical location and the seasonal variation of meteorological parameters. Our models are validated with a 17-year-long time series of zenith tropospheric delay estimates as provided by the International GNSS Service. The results show that the developed models are still conservative, while the maximal residual error of the tropospheric delay is still improved by 39–55%. This improvement yields higher service availability and continuity in safety-of-life applications of GNSS.

General Fishnet Statistics of Strength: Nacreous, Biomimetic, Concrete, Octet-Truss, and Other Architected or Quasibrittle Materials

Abstract The fishnet probabilistic model was recently developed to characterize the strength distribution of nacre-like biomimetic materials. It reveals that the unique fishnet-like connectivity of the material microstructure brings about enormous safety gain at the extremely low failure probability level of one out of a million, desired for engineering structures. The gist of the theory is that the material microstructure plays a determining role in its failure probability tail. Therefore, a carefully designed connectivity for a material microstructure not only enhances its mean strength but also significantly reduces its marginal failure risk. Here, we first show that the initially introduced series expansion and the newer formulation based on order statistics are, in the fishnet model, essentially equivalent. From that we develop a neat general form of the fishnet statistics. Then, we extend our theoretical approach to the strength distributions of architected nanomaterials such as the printed octet-truss carbon nanolattices, as well as to quasibrittle particulate composites such as concrete, and formulate a unified general fishnet statistics. We demonstrate that the octet-truss system can be physically seen and statistically treated as a union of three fishnets with three mutually orthogonal orientations. We show that the three-dimensional assembly of fishnets further enhances the tail strength at the 10−6 probability quantile, compared to two-dimensional (2D) fishnet statistics. We compare the performance of different statistical strength models by fitting of the simulated and experimental histograms data for the octet-truss nanolattice. Finally, we argue that, at the extreme lower tail of failure probability, quasibrittle materials such as concrete or fiber composites should partially exhibit the fishnet-type statistical behavior.

Subset simulation for efficient slope reliability analysis involving copula-based cross-correlated random fields

This study proposes a subset simulation (SS)-based approach for efficient slope reliability analysis involving copula-based cross-correlated random fields of cohesion (c) and friction angle (ϕ) of soils. First, the copula theory for modeling the cross-correlation between c and ϕ is briefly introduced. The algorithms for generating the copula-based cross-correlated random fields of c and ϕ are detailed. Then, the SS for efficient slope reliability analysis involving copula-based cross-correlated random fields of c and ϕ is explained. Finally, two slope examples with the same geometry but different sources of probability information are presented to illustrate and demonstrate the proposed approach. The results indicate that the proposed approach has both good accuracy and efficiency in slope reliability analysis involving the copula-based cross-correlated random fields of c and ϕ at low failure probability levels. The copula theory for characterizing the cross-correlated random fields can consider both the Gaussian and non-Gaussian dependence structures between c and ϕ. The copula selection has a significant impact on slope reliability with spatially variable c and ϕ. The probabilities of slope failure produced by different copulas differ considerably. This difference increases with decreasing probability of slope failure. The commonly-used Gaussian copula may lead to a significant underestimate of the probability of slope failure. The reasonable identification of the best-fit copula for characterizing the cross-correlated random fields of c and ϕ based on the test data is highlighted in practical slope reliability analysis.

Added prognostic value of left ventricular shape by gated SPECT imaging in patients with suspected coronary artery disease and normal myocardial perfusion

BackgroundLeft ventricular (LV) remodeling is associated with adverse cardiovascular events. We evaluated the added prognostic value of LV shape index (SI) assessed by gated single-photon emission tomography (SPECT) in patients without known coronary artery disease (CAD). Methods and resultsWe studied 674 patients with normal myocardial perfusion and normal LV ejection fraction (EF) on stress gated SPECT imaging. An automated software program was used to calculate end-diastolic and end-systolic LVSI. An LVSI ≤ 0.54 at end-systole was considered normal. Follow-up was 96% complete with a median follow-up of 37 months. During follow-up, 25 events occurred (3.8% cumulative event rate). Event-free survival was lower in patients with abnormal end-systolic LVSI (P < .001). Age (P = .021), diabetes (P = .048), and end-systolic LVSI (P < .001) were independent predictors of events. LVSI added prognostic information increasing the global chi-square of the model including age and diabetes from 15.15 to 25.97 (P < .001). The effect of diabetes on hazard ratio increased with increasing values of end-systolic LVSI. The probability of events at 48 months predicted by Weibull analysis progressively increased with increasing values of end-systolic LVSI and was higher in patients with diabetes as compared to those without. Decision curve analyses indicate that the model including end-systolic LVSI resulted in an increased net benefit between 5% and 30% threshold probability, indicating superior estimation of outcomes at low threshold probability levels. ConclusionsThe evaluation of LVSI may identify patients with early-stage LV remodeling and at higher risk of adverse cardiac events, even in the presence of normal myocardial perfusion.

Long-Term Extreme Response and Reliability of a Vessel Rolling in Random Beam Seas

In this paper, the long-term extreme response of a vessel rolling in random beam seas and the associated reliability evaluation are addressed. The long-term response analysis is based on the upcrossing rates of the roll motion under different sea states. Generally, for nonlinear roll motion in random seas, the high-level roll response is sensitive and closely related to the nonlinear effects associated with the restoring and damping terms. Therefore, assessing the corresponding statistics of the random roll motion with low probability levels is difficult and time-consuming. In this work, the Markov theory is introduced in order to tackle this problem. Specifically, for the dead ship condition, the random roll excitation moment is approximated as a filtered white noise process by applying a second-order linear filter and an efficient four-dimensional (4D) path integration (PI) technique is applied in order to calculate the response statistics. Furthermore, the reliability evaluation is based on the well-known Poisson estimate as well as on the upcrossing rate calculated by the 4D PI method. The long-term analysis and reliability evaluation of the nonlinear roll motion in random seas, which consider the variation of the sea states could be a valuable reference for ship stability research.

Efficient slope reliability analysis at low-probability levels in spatially variable soils

Direct Monte Carlo simulation for the reliability analysis of slope stability with spatially variable soil properties suffers from a serious lack of efficiency when the probability of failure, pf, is low (e.g., pf<10-6). Based on the multiple response surfaces and Subset simulation, this paper proposes an efficient approach for the estimating of small probabilities of slope failure in spatially variable soils. An improved Cholesky decomposition technique is presented for the simulation of the globally non-stationary random fields of spatially variable soil properties in the multiple soil layers. Two slope examples are investigated to demonstrate the effectiveness of the proposed method. The efficiency of the proposed approach for parametric sensitivity analysis is also highlighted.

Efficient reliability updating of slope stability by reweighting failure samples generated by Monte Carlo simulation

Direct Monte Carlo simulation (MCS) could be prohibitively expensive for slope reliability updating problem with relatively low-probability level. This paper aims to propose an efficient slope reliability updating method based on the direct MCS. First, the direct MCS is briefly introduced. Thereafter, a weighted approach based on the direct MCS is proposed for slope reliability updating. Furthermore, the implementation procedure and the flow chart of the proposed method are presented. Two illustrative examples are investigated to demonstrate the capacity and validity of the proposed method. The results indicate that the proposed method can properly utilize the information contained in failure samples generated by performing direct MCS based on the baseline distribution, which is much more efficient than repeatedly performing direct MCS as the probability distributions of soil properties are updated. The proposed method can solve the slope reliability updating problems involving multiple uncertain parameters and implicit performance functions. The accuracy of the proposed method depends on the failure samples generated from the baseline distribution. When the failure samples cannot completely cover the failure region underlying the reliability updating problem, the proposed method should be used with caution. The coefficient of variation (COV) of probability of failure from the proposed approach is derived and used as a measure of the accuracy in updated probability of failure. In geotechnical engineering practices, the updated COV of an uncertain parameter is often smaller than its baseline COV. In this case, the proposed method can reasonably update the probability of failure.

Earthquake Early Warning System for Schools: A Feasibility Study in Southern Italy

Over the last few decades, metropolitan areas have experienced a dramatic increase of exposure to earthquakes. Because of the still very low probability level at which short‐term earthquake forecasting is feasible, earthquake early warning systems (EEWS) currently represent an effective, pragmatic, and viable means for the implementation of protective measures to reduce the exposure of population to seismic risk. EEWS can be simply defined as systems that integrate seismic networks and software capable of performing real‐time data telemetry and analysis to provide alert messages to users within seconds of the beginning of an earthquake and certainly before that the S waves generated by the event reach the users. Worldwide, a number of EEWS capable of rapidly performing seismological analysis of ground motion during a strong earthquake are currently operative or are under real‐time testing (e.g., in Japan, Taiwan, Mexico, Italy, Turkey, California, etc.; Allen and Kanamori, 2003; Kanamori, 2005; Allen et al. , 2009). A tangible example of EEWS benefit is given by the experience of the OKI factory in the Miyagi prefecture in Japan that, after having experienced severe damages in 2003 due to two earthquakes, installed an EEWS, and at the following earthquakes experienced a significant reduction of losses (Allen et al. , 2009). Moreover, the Japan Meteorological Agency has shown the effectiveness of a combined on‐site and network‐based approach to rapidly broadcast the rapid warning after a potential damaging earthquake (Hoshiba, 2013). The key parameter of EEWS is the lead time, which is the time available for taking protective measures at distant targets once an earthquake has been promptly detected and …