Reliability Engineering & System Safety最新文献_第5页

Methodological guidance and quantitative measures regarding seismic capacity and safety of freestanding and inelastic anchored nonstructural elements housed in ordinary and critical facilities

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2025-03-15 DOI: 10.1016/j.ress.2025.111029

Danilo D'Angela , Gennaro Magliulo

{"title":"Methodological guidance and quantitative measures regarding seismic capacity and safety of freestanding and inelastic anchored nonstructural elements housed in ordinary and critical facilities","authors":"Danilo D'Angela , Gennaro Magliulo","doi":"10.1016/j.ress.2025.111029","DOIUrl":"10.1016/j.ress.2025.111029","url":null,"abstract":"<div><div>The study provides a comprehensive investigation of the seismic capacity and safety assessment of nonstructural elements (NEs). A general and extendable methodology is developed for robust assessment of two representative model categories of NEs: rocking-dominated rigid blocks (RBs) and inelastic single-degree-of-freedom (SDOF) systems, accounting for most acceleration-sensitive elements housed in ordinary and critical facilities. Numerical analysis is carried out implementing nonlinear dynamic models for NEs and accounting for multiple features, such as loading history types (ground and floor motions), NE dynamic properties, intensity measures (IMs), and multiple damage states (DSs). Quantitative capacity measures are provided, as well as novel closed-form equations are proposed for expeditious capacity assessment of NEs, including both capacity curves and surfaces. The seismic safety of the investigated NEs is estimated considering both ordinary and critical buildings, over low- to high-seismicity sites in Italy.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"260 ","pages":"Article 111029"},"PeriodicalIF":9.4,"publicationDate":"2025-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143683514","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A novel method for time-dependent small failure probability estimation of slope instability subjected to stochastic seismic excitations

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2025-03-15 DOI: 10.1016/j.ress.2025.111032

Sihan Li , Xingliang Wang , Rui Pang , Bin Xu

{"title":"A novel method for time-dependent small failure probability estimation of slope instability subjected to stochastic seismic excitations","authors":"Sihan Li , Xingliang Wang , Rui Pang , Bin Xu","doi":"10.1016/j.ress.2025.111032","DOIUrl":"10.1016/j.ress.2025.111032","url":null,"abstract":"<div><div>The computational demand limits the estimation of small failure probabilities in geotechnical engineering under seismic excitation. This study proposes a novel method to estimate the time-dependent small failure probability of slope instability under non-stationary random seismic excitation. Initially, the kernel density estimation (KDE) with appropriate bandwidth is employed for preliminary estimation with the permanent displacement time history is utilized as the evaluation metric for the extreme value distribution (EVD). Subsequently, the EVD is refined using a two-step approach: a shifted generalized lognormal distribution (SGLD) models the main components, while a quadratic function models the tail, enabling the derivation of probability of exceedance (POE) curves on a logarithmic scale. The proposed method's effectiveness is verified through examples of soil and rock slopes subjected to non-stationary random seismic excitation, comparing direct KDE and Monte Carlo simulation (MCS). Results show that the method accurately estimates small failure probabilities of slope instability, has strong numerical stability and flexibility for various slope conditions.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"260 ","pages":"Article 111032"},"PeriodicalIF":9.4,"publicationDate":"2025-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143703937","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Creation of a System Dynamics model of recovery of affected areas after radioactive contamination

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2025-03-15 DOI: 10.1016/j.ress.2025.111031

Anna Selivanova , Igor Krejčí , Tereza Sedlářová-Nehézová , Jiří Hůlka , Irena Češpírová , Petr Kuča

{"title":"Creation of a System Dynamics model of recovery of affected areas after radioactive contamination","authors":"Anna Selivanova , Igor Krejčí , Tereza Sedlářová-Nehézová , Jiří Hůlka , Irena Češpírová , Petr Kuča","doi":"10.1016/j.ress.2025.111031","DOIUrl":"10.1016/j.ress.2025.111031","url":null,"abstract":"<div><div>The presented work focuses on current state-of-the-art in mathematical modeling, specifically addressing atmospheric dispersion, radiation transport, and related issues. Central to this research is the development of a mathematical model designed to support recovery processes following extensive contamination by radionuclides. The model of recovery employs the System Dynamics methodology, recognized for its suitability in addressing complex problems characterized by non-linear behaviors, e.g., radioactive decay. The model is developed utilizing Vensim software. Consequently, the recovery model integrates dosimetry estimates with economic analyses. It forecasts contamination impacts on a variety of objects including buildings, agricultural lands, forests, and transportation infrastructure. To compile the necessary input data for the model, simulations were conducted using specialized codes, i.e., JRODOS and MCNP. Furthermore, empirical data concerning the Czech demographic profile, basic characteristics of buildings, and land-use data were employed. Subsequent to these preparatory steps, the model underwent a comprehensive cost-benefit analysis of relevant countermeasures, adapted to the actual conditions in Czechia. Considering very low atmospheric releases, no substantial decontamination actions would be required. For severe accidents, the results of simulated decontamination corresponded to real-case data obtained from the Fukushima clean-up. The obtained results can be used for decision-making by stakeholders and policymakers.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"260 ","pages":"Article 111031"},"PeriodicalIF":9.4,"publicationDate":"2025-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143683519","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Long-term extreme response evaluation of stochastic models using adaptive stochastic importance sampling

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2025-03-14 DOI: 10.1016/j.ress.2025.111028

Tongzhou Zhang , Weifei Hu , Feng Zhao , Jiquan Yan , Ning Tang , Ikjin Lee , Jianrong Tan

{"title":"Long-term extreme response evaluation of stochastic models using adaptive stochastic importance sampling","authors":"Tongzhou Zhang , Weifei Hu , Feng Zhao , Jiquan Yan , Ning Tang , Ikjin Lee , Jianrong Tan","doi":"10.1016/j.ress.2025.111028","DOIUrl":"10.1016/j.ress.2025.111028","url":null,"abstract":"<div><div>The long-term extreme response, such as those observed over 20- or 50-year return periods, is critically important for extreme and reliability analysis as well as design optimization. However, it is often challenging to accurately evaluate this response due to the lack of extreme data in the tail of the response distribution. Monte-Carlo simulation, widely used for this purpose, typically involves complicated simulation models that cause substantial computational costs. In addition, most existing research treats these simulation models as deterministic, neglecting their intrinsic uncertainty. To address these challenges, this paper proposes a new method for evaluating long-term extreme response, which considers stochastic models and utilizes an adaptive weighted kernel density. This approach proposes the adaptive weighted kernel density for obtaining the optimal stochastic importance sampling function, which significantly reduces the required number of simulation samples while maintaining the accuracy of the extreme response evaluation. The bandwidth parameter in the kernel density estimation is optimized through a modification of the integrated square error. The proposed method is validated and compared with some state-of-the-art methods using two numerical examples and an engineering case that evaluates the extreme responses of a 5 mega-watt wind turbine.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"260 ","pages":"Article 111028"},"PeriodicalIF":9.4,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143683516","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Data-driven global sensitivity analysis for group of random variables through knowledge-enhanced machine learning with normalizing flows

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2025-03-13 DOI: 10.1016/j.ress.2025.111007

Ziluo Xiong, Gaofeng Jia

{"title":"Data-driven global sensitivity analysis for group of random variables through knowledge-enhanced machine learning with normalizing flows","authors":"Ziluo Xiong, Gaofeng Jia","doi":"10.1016/j.ress.2025.111007","DOIUrl":"10.1016/j.ress.2025.111007","url":null,"abstract":"<div><div>Different approaches have been developed for evaluating Sobol’ indices for global sensitivity analysis (GSA). Among them sample-based approaches are extremely attractive because they can be purely driven by data and estimate various Sobol’ indices (e.g., first-order, higher-order, total-effects) for any individual or group of random variables using only one set of samples. However, such approaches usually rely on an accurate density estimation for the interested groups of random variables, which can be challenging for high-dimensional groups. For example, the commonly used kernel density estimation (KDE) suffers from curse of dimensionality. In this regard, this paper proposes a novel knowledge-enhanced machine learning approach for data-driven GSA for groups of random variables using sample-based approach and an emerging generative machine learning model, i.e., normalizing flows (NFs), for high-dimensional density estimation. To facilitate reliable and robust NFs training, a knowledge distillation-based two-stage training strategy is developed. Two customized loss functions are introduced, which are inspired by domain knowledge in the context of sample-based approach for GSA. Two examples are considered to illustrate and verify the efficacy of the proposed approach. Results show that introducing NFs can significantly alleviate the curse of dimensionality in the traditional sample-based approach for GSA and improve accuracy of density estimation and estimation of Sobol’ indices.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"260 ","pages":"Article 111007"},"PeriodicalIF":9.4,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143637291","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Probabilistic analysis of ship-bridge allisions when designing bridges

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2025-03-13 DOI: 10.1016/j.ress.2025.111026

Axel Hörteborn , Jonas W. Ringsberg , Olov Lundbäck , Wengang Mao

{"title":"Probabilistic analysis of ship-bridge allisions when designing bridges","authors":"Axel Hörteborn , Jonas W. Ringsberg , Olov Lundbäck , Wengang Mao","doi":"10.1016/j.ress.2025.111026","DOIUrl":"10.1016/j.ress.2025.111026","url":null,"abstract":"<div><div>The advances in civil engineering with novel bridge designs between islands and across fjords with long spans, increasing ship traffic density and larger ships in coastal areas, have resulted in an increased frequency of ship-bridge allision accidents worldwide. It is thus essential to have reliable models and methods for engineers to create safe designs of these new bridges to simulate and analyse early pro-active mitigation measures. This study presents a new ship traffic allision probabilistic simulation mid fidelity model (STAPS), which includes a ship's manoeuvrability and motion physics and uses the Monte Carlo simulation method in the probabilistic calculations. It is compared with the low fidelity model IWRAP Mk2, which is used to analyse the risk of ship allisions with structures. Two case studies with ship-allision scenarios are presented to compare how the model fidelity levels of STAPS and IWRAP Mk2 affect the calculated probability levels of ship-bridge allision events. On a general level, the results show that IWRAP Mk2 overestimates the accident probability, for example IWRAP Mk2 predicts a 4.5 times higher probability of allisions compared to STAPS in the base case, and that the failure's duration and route layouts significantly influence both models. The study concludes that IWRAP Mk2 is preferred in the early phase of bridge design and STAPS is preferred in later stages.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"260 ","pages":"Article 111026"},"PeriodicalIF":9.4,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143643657","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Reconstruction-based Deep Unsupervised Adaptive Threshold Support Vector Data Description for wind turbine anomaly detection

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2025-03-13 DOI: 10.1016/j.ress.2025.110995

Dandan Peng , Wim Desmet , Konstantinos Gryllias

{"title":"Reconstruction-based Deep Unsupervised Adaptive Threshold Support Vector Data Description for wind turbine anomaly detection","authors":"Dandan Peng , Wim Desmet , Konstantinos Gryllias","doi":"10.1016/j.ress.2025.110995","DOIUrl":"10.1016/j.ress.2025.110995","url":null,"abstract":"<div><div>The global deployment of wind turbines as a sustainable and clean energy source underscores the criticality of early anomaly detection to ensure their safe operation, improve power generation efficiency, and reduce downtime costs. Yet, acquiring sufficient labeled and faulty data is time-consuming and expensive in practical applications, limiting the use of supervised learning methods. To this end, this paper introduces a new approach, namely the Reconstruction-based Deep Unsupervised Adaptive Threshold Support Vector Data Description (DUA-SVDD) model, for wind turbine anomaly detection. DUA-SVDD integrates reconstruction-based and boundary-based anomaly detection paradigms, synthesizing comprehensive and detailed representation information from dynamic monitoring data, encoding the distribution and patterns of normal samples across multiple levels. This model employs a joint optimization mechanism to minimize reconstruction errors and hypersphere volume simultaneously in the latent space, resolving the hypersphere collapse issue observed in Deep Support Vector Data Description (DeepSVDD). It constructs a well-structured latent space proficient in handling data noise and variations, allowing SVDD to establish more robust spherical boundaries. Additionally, it proposes an adaptive threshold algorithm based on pseudo-data to accurately differentiate abnormal from normal patterns. The method is tested and evaluated on real wind farm SCADA datasets. A comparative analysis against state-of-the-art methods highlights the superior performance of the proposed model in detecting blade icing on wind turbines, achieving average AUC values of 97.54% and 99.45% across two specific cases.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"260 ","pages":"Article 110995"},"PeriodicalIF":9.4,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143637290","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Resilience quantification method of high-speed railway train diagram under operation section interference: Strategies and practices

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2025-03-13 DOI: 10.1016/j.ress.2025.111020

Xinxin Li , Wencheng Huang

{"title":"Resilience quantification method of high-speed railway train diagram under operation section interference: Strategies and practices","authors":"Xinxin Li , Wencheng Huang","doi":"10.1016/j.ress.2025.111020","DOIUrl":"10.1016/j.ress.2025.111020","url":null,"abstract":"<div><div>In this paper, we propose a method for quantifying the resilience of high-speed railway train diagram (HSRTD) under section interference. The resilience of HSRTD is defined as the ability to resist, adapt to the impact of section interference, and quickly recover to normal operation state from the impact. Firstly, by establishing a HSR train operation control model based on cellular automata, the real-time control and feedback of affected train number and affected time under section interference can be obtained. Then, from the three aspects of resistance ability, adaptation ability and recovery ability, six indicators including vulnerability, redundancy, absorbability, survivability, sensitivity and dependency are selected to quantify the resilience of HSRTD. Next, three strategies including removing some operation lines, moving some operation lines, removing and adding some operation lines are proposed for restoring and adjusting the operation of HSR trains under section interference. Finally, the Xi'an North-Baoji South HSRTD is selected as a case study to compare and analyze the resilience quantification results with different recovery and adjustment strategies under section interference. Among the three strategies for adjusting the HSRTD, removing some operation trains in the affected area has the most significant effect on improving the resilience of the HSRTD.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"260 ","pages":"Article 111020"},"PeriodicalIF":9.4,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143642855","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A review of prognostics and health management techniques in wind energy

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2025-03-12 DOI: 10.1016/j.ress.2025.111004

Jokin Cuesta , Urko Leturiondo , Yolanda Vidal , Francesc Pozo

{"title":"A review of prognostics and health management techniques in wind energy","authors":"Jokin Cuesta , Urko Leturiondo , Yolanda Vidal , Francesc Pozo","doi":"10.1016/j.ress.2025.111004","DOIUrl":"10.1016/j.ress.2025.111004","url":null,"abstract":"<div><div>This review aims to provide a holistic understanding of prognostics and health management (PHM) techniques in wind energy, particularly in the estimation of remaining useful life (RUL) of wind turbine (WT) components. The study begins with an introduction that discusses the principles of PHM and its critical role in the wind energy sector. This is followed by an overview of WT systems and the importance of accurate RUL predictions for specific failure modes. Then, various data sources, methods of feature extraction, and criteria for constructing health indices are explored, along with techniques for threshold determination. Degradation modeling techniques, essential for RUL prediction, are examined through three approaches: physics-based models, data-driven methods (including statistical and artificial intelligence techniques), and hybrid models. The performance of these models is evaluated using specific metrics which have been explored. Next, predictive maintenance strategies, optimized using RUL predictions, are presented to minimize downtime and maintenance costs. The paper concludes by identifying future research directions, emphasizing the need to manage uncertainty, integrate physical knowledge, address variable environmental and operational conditions, overcome data issues, and handle system complexity.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"260 ","pages":"Article 111004"},"PeriodicalIF":9.4,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143643656","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Double layer blockchain-assisted trusted data flow model for industrial control systems

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2025-03-12 DOI: 10.1016/j.ress.2025.111013

Xiangzhen Peng , Chengliang Zheng , Yidi Wang , Xiaohui Cui , Zhidong Shen

{"title":"Double layer blockchain-assisted trusted data flow model for industrial control systems","authors":"Xiangzhen Peng , Chengliang Zheng , Yidi Wang , Xiaohui Cui , Zhidong Shen","doi":"10.1016/j.ress.2025.111013","DOIUrl":"10.1016/j.ress.2025.111013","url":null,"abstract":"<div><div>With the development of information technology (IT), the blurred network boundary between the Operational Technology (OT) network and the IT network poses a higher risk of cyber-attacks on the flow of data in Industrial Control System (ICS). Deep isolation of ICS, enhanced data access control in ICS, and proactive defense against cyber-attacks in ICS can help achieve the secure flow of highly sensitive data in ICS. This article proposes a dual-layer blockchain-assisted data flow protection framework for ICS, driven by blockchain, and conducts simulation and analysis. Firstly, OT-blockchain and IT-blockchain were designed to redefine the network boundary of ICS. Secondly, an identity-assisted authentication mechanism based on Bloom filters and trusted databases was designed to rapidly identify dishonest nodes. Then, an ICS-RBAC zero-trust access control mechanism based on RBAC was designed to ensure the security of the OT blockchain and achieve zero-trust data exchange between the IT-blockchain and ICS physical devices. And, an active defense mechanism for ICS was designed based on the principle of the heartbeat mechanism. Finally, model analysis and simulation verification are conducted. The results indicate that this study can achieve trusted data flow in ICS and fine-grained zero-trust access control, providing security guarantees.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"260 ","pages":"Article 111013"},"PeriodicalIF":9.4,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143643658","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0