Probabilistic Assessment Method Research Articles

Assessing Voltage Stability in Distribution Networks: A Methodology Considering Correlation among Stochastic Variables

Distributed photovoltaic (PV) output exhibits strong stochasticity and weak adjustability. After being integrated with the network, its interaction with stochastic loads increases the difficulty of assessing the distribution network’s static voltage stability (SVS). In response to this issue, this article presents a probabilistic assessment method for SVS in a distribution network with distributed PV that considers the bilateral uncertainties and correlations on the source and load sides. The probabilistic models for the uncertain variables are established, with the correlation between stochastic variables described using the Copula function. The three-point estimate method (3PEM) based on the Nataf transformation is used to generate correlated samples. Continuous power flow (CPF) calculations are then performed on these samples to obtain the system’s critical voltage stability state. The distribution curves of critical voltage and load margin index (LMI) are fitted using Cornish-Fisher series. Finally, the utility function is introduced to establish the degree of risk of voltage instability under different scenarios, and the SVS assessment of the distribution network is completed. The IEEE 33-node distribution system is utilized to test the method presented, and the results across various scenarios highlight the method’s effectiveness.

Quantifying the effect of pulse-like ground motions on the seismic design of SMA restrained isolation bridges using probabilistic repair cost method

The shape memory alloy (SMA) restrainer serves as an effective but expensive bridge restraining device. However, the impact of pulse effect on SMA design approach under pulse-like ground motions (PLGM) has not been fully quantified in previous studies, which hinders its application in near-fault regions. Therefore, based on the risk probability assessment method throughout the entire life cycle, by accounting for the comprehensive repair cost of each component damage, this paper introduces a parameter design method for SMA restrainer of near-fault bridges that considers structural parameters, pulse parameters, and economic indicators. Firstly, the repair cost ratio (RCR) of bridge system, which means the expenses for repairs expressed as a proportion of bridge replacement costs, was regarded as the life-time optimization goal and overall performance indicator. Secondly, by accounting for near-fault effects, the relationship between RCR and SMA design parameters was established by convolution algorithm of probabilistic seismic hazard analysis, demand analysis and capacity analysis. A novel probabilistic seismic demand model was utilized to quickly determine the RCR of the bridge system under PLGM. Finally, the influence of pulse period on the rational design parameter of SMA restrainer was comprehensively investigated by RCR-based method. A seismic isolation arch bridge was selected as the illustrated case in this article. The results indicate that the rational design parameters of SMA exhibit a pattern of initially rising, then falling as the pulse period increases, reaching the peak value when the pulse period approaches bridge fundamental period. Moreover, the price parameter of SMA significantly affect the optimal design parameters, and the effective range is also recommended.

Comparison of probabilistic structural reliability methods for ultimate limit state assessment of wind turbines

The probabilistic design of offshore wind turbines aims to ensure structural safety in a cost-effective way. This involves conducting structural reliability assessments for different design options and considering different structural responses. There are several structural reliability methods, and this paper will apply and compare different approaches in some simplified case studies. In particular, the well known environmental contour method will be compared to a more novel approach based on sequential sampling and Gaussian processes regression for an ultimate limit state case study on the maximum flapwise blade root bending moment. For one of the case studies, results will also be compared to results from a brute force simulation approach. Interestingly, the comparison is very different from the two case studies. In one of the cases the environmental contours method agrees well with the sequential sampling method but in the other, results vary considerably. Probably, this can be explained by the violation of some of the assumptions associated with the environmental contour approach, i.e. that the short-term variability of the response is large compared to the long-term variability of the environmental conditions. Results from this simple comparison study suggests that the sequential sampling method can be a robust and computationally effective approach for structural reliability assessment.

Spatial correlation in building seismic performance for regional resilience assessment

Probabilistic seismic performance assessment method for buildings offers a valuable approach to simulate the broader regional impacts: economic losses, downtime, and casualties. A crucial aspect of this process entails accounting for the spatial correlation of building performances, aiming for an accurate estimation of the probability of extreme regional losses, such as the simultaneous collapse of buildings with similar structural characteristics. In this study, a correlation model based on a Gaussian random field is employed, and several key challenges associated with its application are addressed. In addition, efficiency of five different methods of selecting station records from the same earthquake scenario is compared. The minimum number of earthquake records necessary to achieve a stable correlation result is determined. Additionally, spatial correlations derived from different history earthquake events are compared. By addressing these critical issues, this research contributes to refining the reliability of probabilistic methods for regional resilience assessment.

Axial compressive behavior of corroded H-shaped steel columns

The unpredictability of corrosion in steel structures, especially in H-section columns, is a topic that has received limited research attention. Using Monte Carlo simulation (MCS) and finite element analysis (FEA), this study investigates the effect of random pitting corrosion on H-section steel columns' ultimate bearing capacity under axial compression. In this study, MCS was employed to address the random distribution of pitting corrosion. The reliability of MCS and FEA was validated through existing literature on compression tests of H-shaped steel columns. To comprehensively assess the detrimental effects of pitting corrosion, a total of 3650 models of H-shaped columns, each exhibiting different pitting corrosion characteristics, were subjected to analysis. These models incorporated three corrosion parameters (volume loss rate, size, and depth) and two geometric parameters (slenderness and section size). A nonlinear buckling analysis was carried out to investigate the ultimate bearing capacity of the columns. The findings indicate that a volume loss rate of 20% can cause a decrease in the column's ultimate bearing capacity by approximately 50%. Furthermore, the study reveals that at equal volume loss rates, corrosion depth and slenderness significantly influence the reduction factor, while corrosion size and section size exert a minimal impact. As a result, this paper introduces a probabilistic assessment method for estimating the reduction factor in ultimate bearing capacity for H-section steel columns. Notably, with every 10% increase in the volume loss rate, the mean reduction factor in the ultimate bearing capacity decreases by approximately 0.12.

A novel uncertainty propagation and probability assessment method for the frequency response function involving correlated uncertainties

A novel uncertainty propagation and probability assessment method for the frequency response function involving correlated uncertainties

A joint multi-model machine learning prediction approach based on confidence for ship stability

Since the traditional ship stability failure probability assessment method has many input parameters and cumbersome intermediate calculation process, this paper proposes a joint multi-model machine learning prediction method based on confidence. The method calculates the confidence of each machine learning model for the current prediction result, selects the top n models among them, and takes the average of their prediction results as the output. The confidence is calculated by a method inspired by semi-supervised learning. To reduce the number of ship features that need to be input for assessment, a sensitivity analysis is used to reject irrelevant features. Eight machine learning models that have good performance and are widely used in other fields, including Radial Basis Function Neural Network, Random Forest, eXtreme Gradient Boosting, and so on, are used as component learners in this study. The hyperparameters of each model are obtained by cross-validation and grid search. The advancedness of the proposed method is verified by comparing it with other models on a small self-built ship stability failure probability dataset. By conducting experiments that simply average the results of the component learners, it is confirmed that simple superposition different models does not necessarily improve the accuracy. At the same time, after pre-processing the input features in different ways, the comparison of the prediction performance was conducted, and the experimental results showed that the proposed method is not affected by the way the input features are preprocessed and therefore has some robustness.

Models and methods for probabilistic safety assessment of steel structures subject to fatigue

We review of the state of the art in probabilistic modelling for fatigue reliability of civil engineering and offshore structures. The modeling of randomness and uncertainty in fatigue resistance and fatigue load variables are presented in some detail. This is followed by a review of the specifics of reliability analysis for fatigue limit states and a background on the semi-probabilistic treatment of fatigue safety. We discuss the different life-cycle reliability concepts and give an overview on probabilistic inspection planning. We describe the choices made in the Probabilistic Model Code of the Joint Committee of Structural Safety, present alternatives to these choices and suggest areas of future research.

A non-probabilistic reliability analysis method with the fuzzy failure criterion

In engineering practice, the failure boundary of structures is not usually distinct due to some ambiguous information, which brings a tricky challenge to reliability assessment. The fuzzy theory is introduced to solve it with the traditional probability assessment method. For the non-probabilistic reliability system, however, this problem tends to be at a dead end because it lacks some necessary probability information. In this study, the non-probabilistic reliability model with fuzzy failure criterion is established, which involves two different evaluation indices in terms of the limit state surface. Based on this, a corresponding calculation method is proposed to deal with it, and the interval model and the ellipsoid model are shown for instance. In addition, the HRHL-RF method owning strong robustness is utilized to calculate the index of the ellipsoid model. Eventually, an established algorithm frame can achieve the calculation of the unified fuzzy reliability index, and three examples with the classical fuzzy membership functions are computed to show its feasibility and efficiency.

A failure probability assessment method for train derailments in railway yards based on IFFTA and NGBN

Derailment is one of the main hazards during train passes through railway turnouts (RTs) in classification yards. The complexity of the train-turnout system (TTS) and unfavorable operating conditions frequently cause freight wagons to derail at RTs. Secondary damages such as hazardous material spillage and train collisions can result in loss of life and property. Therefore, the primary goal is to assess the derailment risk and identify the root causes when trains pass through RTs in classification yards. To address this problem, this paper proposes a failure probability assessment approach that integrates intuitionistic fuzzy fault tree analysis (IFFTA) and Noisy or gate Bayesian network (NGBN) for quantifying the derailment risk at RTs. This method can handle the fact that the available information on the components of the TTS is imprecise, incomplete, and vague. The proposed methodology was tested through data analysis at Taiyuan North classification yard in China. The results demonstrate that the method can efficiently evaluate the derailment risk and identify key risk factors. To reduce the derailment risk at RTs and prevent secondary damage and injuries, measures such as optimizing turnout alignment, controlling impact between wagons, lubricating the rails, and regularly inspecting the turnout geometries can be implemented. By developing a risk-based model, this study connects theory with practice and provides insights that can help railway authorities better understand the impact of poor TTS conditions on train safety in classification yards.

A new probabilistic assessment process for human health risk (HHR) in groundwater with extensive fluoride and nitrate optimized by non parametric estimation method.

Excessive amounts of fluoride (F-) and nitrate (NO3-) in groundwater pose a significant threat to human health. However, a quantitative approach to assessing the human health risks caused by these harmful substances is lacking. To optimize the probabilistic assessment process for human health risk (HHR), this study introduced kernel density estimation (KDE) into the stochastic simulation of F- and NO3- content in groundwater samples. The potential HHRs caused by F- and NO3- in Songyuan City were summarized by combining the probabilistic and deterministic assessments. This study found that the concentrations of F- and NO3- did not follow common probability density functions (PDFs), but the KDE method passed the Kolmogorov-Smirnov test with the critical value of 0.067 and 0.062, showing high fitting accuracy. Monte Carlo simulation indicated that the probability of NO3- for children and adult exceeding the standard is 21.95% and 15.14%, respectively, which is comparable with the results of the deterministic assessment, but the probabilistic assessment emphasized lower probability of HHRs in children caused by excess F-(4.14%). Global sensitivity analysis revealed that excessive NO3- in groundwater has the highest sensitivity of the HHR (>0.1), followed by other factors representing water use habits (>0.01). This study presents a refined probabilistic assessment method for HHR and provides a scientific reference for understanding the state of groundwater environments.

An efficient surrogate model for reliability analysis of the marine structure piles

A hybrid random-forest-based subset simulation (RFSS) method for probabilistic assessment of scour around pile groups under waves is proposed. In the RFSS, a random forest is used to replace the true limit state function (LSF); it is updated based on design samples in the first and last levels of the subset simulation method. For modelling, 127 laboratory datasets collected from the literature were used. First, an existing equation for predicting the scour depth around piles was modified using a metaheuristic approach. The performance of the modified equation was compared with existing equations and models. The modified equation was found to be more accurate than the existing formulas and AI-based models. A probabilistic model based on the RFSS was then developed by considering the modified formula as the LSF of scour depth. Solving two numerical problems, one hydraulic engineering problem and one scour problem validated the robustness and accuracy of the structural reliability method. The results showed that the RFSS is a robust and efficient method for solving high-dimensional real-world problems. Furthermore, compared to the Monte Carlo simulation, the RFSS was able to estimate the reliability index with less computational cost and the same accuracy.

Analysis of quantitative metrics for assessing resilience of human-centered CPPS workstations

Manufacturing companies’ preparedness level against external and internal disruptions is complex to assess due to a lack of widely recognized or standardized models. Resilience as the measure to characterize preparedness against disruptions is a concept with various numerical approaches, but still lacking in the industry standard. Therefore, the main contribution of the research is the comparison of existing resilience metrics and the selection of the practically usable quantitative metric that allows manufacturers to start assessing the resilience in digitally supported human-centered workstations more easily. An additional contribution is the detection and highlighting of disruptions that potentially influence manufacturing workstations the most. Using five weighted comparison criteria, the resilience metrics were pairwise compared based on multi-criteria decision-making Analytic Hierarchy Process analysis on a linear scale. The general probabilistic resilience assessment method Penalty of Change that received the highest score considers the probability of disruptions and related cost of potential changes as inputs for resilience calculation. Additionally, manufacturing-related disruptions were extracted from the literature and categorized for a better overview. The Frequency Effect Sizes of the extracted disruptions were calculated to point out the most influencing disruptions. Overall, resilience quantification in manufacturing requires further research to improve its accuracy while maintaining practical usability.

A novel multi-ship collision probability estimation method considering data-driven quantification of trajectory uncertainty

The collision risk prediction is crucial for the safety management of maritime transportation. Previous studies have primarily focused on the near-miss collision risk of two ships, yet the risk due to congestion caused by multiple ships is complex and also very challenging for safe management. In this paper, a novel multi-ship collision probability assessment method is proposed. To consider the uncertainty of ship motion, a deep learning multi-model integration method is proposed to predict the ship motion trajectory to quantify the time-varying stochasticity of ship motion. Then, a method for calculating collision probability is proposed based on Monte Carlo simulation by integrating importance sampling techniques with the cross-entropy method. The model is combined with the ship trajectory estimator to quantify the collision probability. To prove the validity of the developed model, a case study based on Victoria Harbour is illustrated. The results show that this method can provide early warning of multi-ship collision risk. The method can offer an important basis for maritime collision risk monitoring and ship navigation risk assessment for maritime administration departments and shore-based center of maritime autonomous ships.

Reliability Assessment of the Configuration of Dynamic Uninterruptible Power Sources: A Case of Data Centers

The number of data centers worldwide is increasing year by year, mostly because of the development of cloud services and applications. In the near future, the rate of construction of data centers will grow, with a corresponding increase in their electrical energy consumption. The requirements of the reliability of the electrical power supply of data centers are one of the highest among industrial power consumers, since uninterrupted power supply is critically important for the continuous functioning of server hardware. The assessment of electrical power supply reliability is one of the most important parts of the design process of data centers. However, the speed of the development of new power equipment does not always make it possible to use classical probabilistic and statistical methods for reliability assessment. Therefore, the development of new methods for reliability assessment based on alternative approaches, which can eliminate the disadvantages of probabilistic and statistical methods, are of great interest. This paper discusses the alternative method for analyzing the reliability of electrical power supply for the case of data centers. The method defines the reliability through the internal information of the system that characterizes the system’s topology, flows of information, energy, and matter in the system.

Constructing model-averaging species sensitivity distributions of Phenanthrene based on reproductive fitness: Implications for assessing ecological risk in urban watershed.

The challenge in optimizing the method of constructing species sensitivity distribution (SSD) remains. In this study, a model-averaging SSD was created to evaluate the ecological risk of Phenanthrene (PHE) in urban watershed based on reproductive fitness. Specifically, concentrations of PHE were measured in surface water samples collected from various watersheds of Wuhan, including five lake watersheds and the Wuhan reach of the Yangtze River and Han River. The reproductive endpoint of aquatic species was calculated to be most sensitive to PHE exposure, with the value of predict no-effect concentration (PNEC) at 0.19μg/L. The results of probabilistic assessment methods, including joint probability curve (JPC), overall risk probability (ORP), and distribution-based quotient (DBQ), indicated that the ecological risks of PHE in large lakes have dropped significantly with distance from the downtown area of Wuhan, and the long-term effects of industrial activities may increase the risks in the lake watersheds. Basically, the ecological risks in Yangtze River are negligible; however, there is a relatively high risk of PHE in the Han River and some lake watersheds. The cosθ similarity analysis indicated the Yangtze River is strongly connected to the low-risk lake watersheds, and that in part reflects the risk in the Yangtze River being controlled by its surrounding these lake watersheds.

Меры доверия и правдоподобия при оценке рисков информационной безопасности

Purpose of the research: to develop methods for assessing information security risks under uncertainty, to describe the mechanism of propagating belief and plausibility in the attack graph.Research method: application of soft computing techniques, including the combination of Dempster-Shafer evidence theory, integration with respect to non-additive measures of belief and plausibility.Research result: risk assessment methods and methods for assessing expected losses have been developed in the case when risk factors are characterized by high uncertainty and do not allow sufficiently justified applying objective (probabilistic) assessment methods. The initial information is the upper and lower estimates of the probability of risk realization. Using the methods of the Dempster-Shafer evidence theory, belief and plausibility measures are built on the attack graph. An approach is described that allows building belief and plausibility measures in the space of attack scenarios based on probabilistic estimates of typical information security events. It is shown how the expected damage (severity) can be estimated by the expectation of damage with respect to to these measures using the Choquet integral. Scientific novelty: a method of propagation belief along the attack graph has been developed. The method is based on an original approach to evaluating logical combinations of evidence given on binary frames and represented by disjunctive normal forms.

Analisis Risiko Penyebaran Covid-19 Melalui Fasilitas dan Kegiatan di Universitas Sahid Menggunakan Metode HIRADC

Coronavirus Disease 19 (COVID-19) is an infectious disease caused by acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Facilities and activities at Sahid University Jakarta have the potential to spread Covid-19, so an assessment of the risk and hazard control level is carried out. This study aims to identify activities that have the potential to spread COVID-19, assess the level of risk of spreading COVID-19, and determine controls for the risk of spread and transmission at Sahid University. The probability and impact assessment method for 16 activities with 34 variables uses the Hazard Identification, Risk Assessment, and Determining Control (HIRADC) method. Processing the data to obtain results, variable risk 15%, high, low risk 6%, and 79% moderately high risk. The study's conclusion found that the main activities that potentially spread the Covid-19 virus are face-to-face lectures, administration, finance, and laboratory learning. Measures to control the risk of transmission through engineering and reorganization of work, administrative controls, and use of PPE. The research suggests that each need be aware to remind each other and carry out obligations in the health protocol for the sake of realizing a Covid-19 free environment at Sahid University Jakarta and its surroundings.

Transformer maintenance decision based on condition monitoring and fuzzy probability hybrid reliability assessment

Equipment maintenance decision is a very critical technology in power grid asset management. The traditional maintenance decision focuses on the analysis of the operation performance of the equipment itself, but lacks the analysis and description of the fuzziness of power equipment outage parameters. Therefore, this paper establishes the reliability assessment model based on equipment health condition monitoring, which makes the maintenance decision transition from equipment level to system level, and the fuzzy mathematics theory is introduced to establish a model describing the fuzziness of equipment failure rate parameter to make system reliability assessment and equipment maintenance decision scheme. Firstly, this paper proposes a fuzzy failure rate model of power transformer based on condition monitoring. Then the fuzzy parameters are combined with the conventional probabilistic reliability assessment method to establish the fuzzy probability hybrid reliability assessment model of the transmission system, and the fuzzy maintenance reliability benefit index is defined and deduced. Finally, a maintenance strategy of transformer based on fuzzy probability hybrid reliability assessment and fuzzy set theory is proposed, and the case study is carried out on a regional power grid in East China. The results show that the proposed model provides an improved maintenance strategy of power equipment.

Probabilistic Seismic Assessment for Reinforced Concrete Bridges

Seismic assessments of reinforced concrete (RC) bridges play an important role in the sustainable development of societies and the determination of the critical values of demand measures for various damage limit states is a key issue in seismic assessments. The results of a seismic assessment largely depend on which kind of demand measure is used, but few studies have investigated the nature of the relationships between damage measures in order to avoid inconsistency between seismic assessments. In this study, a finite element model of an RC bridge was constructed to act as a benchmark example. Three parameters, system-equivalent displacement, rotation angle of a plastic hinge, and material strain, were used as the engineering demand parameters (EDPs) to assess the seismic performance of different earthquake levels. Numerical examples demonstrated that the exceedance probabilities significantly depended on the selected EDP and the critical values of the EDPs for the three damage states. To obtain a consistent seismic assessment of the representative bridge for the different EDPs, an iterative probabilistic seismic assessment method was adopted. The EDP critical values could then be determined using the performance-based exceedance probabilities. It was suggested to the exceedance probabilities within 50 years for the three damage limit states (performance-based) at half of the exceedance probability within 50 years for three seismic levels of earthquakes (hazard-based), respectively.