Reliability Engineering & System Safety最新文献

Modeling urban transportation safety resilience under extreme rainstorms: A catastrophe theory approach 极端暴雨条件下城市交通安全弹性建模：一种突变理论方法

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2025-05-29 DOI: 10.1016/j.ress.2025.111301

Fei Ma , Yujie Zhang , Qing Liu , Yaru Guo , Zhijie Yang , Jiaju Zhang

{"title":"Modeling urban transportation safety resilience under extreme rainstorms: A catastrophe theory approach","authors":"Fei Ma , Yujie Zhang , Qing Liu , Yaru Guo , Zhijie Yang , Jiaju Zhang","doi":"10.1016/j.ress.2025.111301","DOIUrl":"10.1016/j.ress.2025.111301","url":null,"abstract":"<div><div>Extreme rainstorm disasters severely affect urban transportation safety. To scientifically assess urban transportation safety resilience (UTSR<span><span><sup>1</sup></span></span>) and its evolutionary process under extreme rainstorm disasters, this study proposes a novel assessment method by modeling the UTSR using the catastrophe theory approach. First, a safety framework for the urban transportation system is constructed, and catastrophe theory is applied to analyze catastrophic effects on the system. Second, factors affecting UTSR are identified, and their relationships are analyzed using a stock and flow model. Finally, the effectiveness of the UTSR dynamic simulation model is analyzed using the case study of an extreme rainstorm event in Xi'an, China. The results reveal that increasing the investment levels of government regulation effort (GRE<span><span><sup>2</sup></span></span>), information synergy degree (ISD<span><span><sup>3</sup></span></span>), and municipal drainage effectiveness (MDE<span><span><sup>4</sup></span></span>) leads to modeled increases in UTSR levels by 59.44%, 50.18%, and 16.79%, respectively. The results demonstrate that strengthening GRE and ISD significantly enhances UTSR, while MDE has a relatively minor impact. This study contributes a new theoretical perspective and practical modeling tool for capturing abrupt resilience transitions, offering detailed management strategies for enhancing UTSR when facing extreme rainstorms.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"263 ","pages":"Article 111301"},"PeriodicalIF":9.4,"publicationDate":"2025-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144184861","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

Dimension-independent single-loop Monte Carlo simulation method for estimate of Sobol’ indices in variance-based sensitivity analysis 基于方差的敏感性分析中Sobol指数估计的单环蒙特卡罗模拟方法

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2025-05-26 DOI: 10.1016/j.ress.2025.111236

Zhiqiang Wan , Silong Wang , Ziyan Wu , Xiuli Wang

引用次数: 0

A vision transformer-based method for predicting seismic damage states of RC piers: Database development and efficient assessment 基于视觉变换的钢筋混凝土桥墩震害状态预测方法：数据库开发与有效评估

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2025-05-24 DOI: 10.1016/j.ress.2025.111287

Yalin Li , Zhen Sun , Yaqi Li , Hao Yang , Xiaohang Liu , Weidong He

{"title":"A vision transformer-based method for predicting seismic damage states of RC piers: Database development and efficient assessment","authors":"Yalin Li , Zhen Sun , Yaqi Li , Hao Yang , Xiaohang Liu , Weidong He","doi":"10.1016/j.ress.2025.111287","DOIUrl":"10.1016/j.ress.2025.111287","url":null,"abstract":"<div><div>The structural safety of bridges, particularly the ability to predict the damage states of reinforced concrete (RC) piers under seismic action, has become a critical issue in structural engineering. This study employs deep learning techniques to enable efficient prediction and assessment of damage states in-service RC bridge piers subjected to seismic events. To support model training, a parametric sample set of 100 bridge piers is generated using Latin Hypercube Sampling, leading to the development of a comprehensive seismic response database containing 66,000 samples across 15 defined damage states. These databases account for inherent seismic randomness, complex failure modes, and time-dependent composite evaluation indicators. A novel deep learning framework, CC-ViT, based on the Vision Transformer architecture, is proposed. This framework integrates Continuous Wavelet Transform, Context Anchored Attention, and DropKey techniques to enhance feature extraction and model generalization. Multiple models are trained and evaluated in a supervised learning setting. Comparative analysis reveals that CC-ViT achieved the highest test accuracy at 85 %. Grad-CAM-based interpretability analysis further confirms that CC-ViT effectively captures critical regions in the seismic response spectrum, supporting informed and explainable decision-making. To facilitate practical implementation, an end-to-end interactive software tool has been developed for efficient prediction of pier damage states. The findings contribute valuable insights for data-driven decision-making aimed at enhancing infrastructure safety and maintenance in smart cities.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"263 ","pages":"Article 111287"},"PeriodicalIF":9.4,"publicationDate":"2025-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144168327","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

Optimal test termination time in reliability growth management for systems with multiple failure modes 多故障模式系统可靠性增长管理中的最优试验终止时间

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2025-05-23 DOI: 10.1016/j.ress.2025.111226

Wenjie Dong , Yingsai Cao , Linhan Ouyang

{"title":"Optimal test termination time in reliability growth management for systems with multiple failure modes","authors":"Wenjie Dong , Yingsai Cao , Linhan Ouyang","doi":"10.1016/j.ress.2025.111226","DOIUrl":"10.1016/j.ress.2025.111226","url":null,"abstract":"<div><div>Reliability growth management is the positive improvement in a reliability metric due to implementation of corrective actions upon system design, operation or maintenance process through a dedicated test-analyze-and-fix (TAAF) procedure. Taking the reliability of a complex system is in fact a multidimensional outcome which is a function of various failure modes into account, the mixed-AMSAA model is constructed in this current research based on the mixed Weibull distribution. The parameters in the mixed-AMSAA model are estimated based on the Weibull probability plot (WPP) in the presence of limited failure data and the goodness-of-fit is tested with the Kolmogorov–Smirnov (K-S) statistic. To determine the optimal termination time of the reliability growth test plan for systems with multiple failure modes, a joint optimization framework under the planing objective of minimizing the total cost by considering the release time in terminating the reliability growth test and the quantity of spare parts inventory for corrective actions is proposed. Numerical solutions to the joint optimization model are theoretically analyzed and two cases from real engineering systems are validated to verify the proposed model. Illustrative results show that the mixed-AMSAA model is capable to capture the growth characteristic of systems with multiple failure modes and is effective in determining the optimal termination time of a reliability growth test program.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"263 ","pages":"Article 111226"},"PeriodicalIF":9.4,"publicationDate":"2025-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144135164","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

Physics-embedding multi-response regressor for time-variant system reliability assessment 时变系统可靠性评估的物理嵌入多响应回归量

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2025-05-23 DOI: 10.1016/j.ress.2025.111262

Lu-Kai Song , Fei Tao , Xue-Qin Li , Le-Chang Yang , Yu-Peng Wei , Michael Beer

{"title":"Physics-embedding multi-response regressor for time-variant system reliability assessment","authors":"Lu-Kai Song , Fei Tao , Xue-Qin Li , Le-Chang Yang , Yu-Peng Wei , Michael Beer","doi":"10.1016/j.ress.2025.111262","DOIUrl":"10.1016/j.ress.2025.111262","url":null,"abstract":"<div><div>Efficient time-variant reliability assessment for complex systems is of great interest but challenging as the highly complex multiple output responses under time-variant uncertainties are hard to quantify. The task becomes even more challenging if the interconnected dependencies between multiple failure modes are involved. In this study, an eXtreme physics-embedding multi-response regressor (X-PMR) is presented for time-variant system reliability assessment. Firstly, by transforming time-variant multiple responses to time-invariant extreme values, an eXtreme multi-domain transformation concept is presented, to establish the time-invariant multi-input multi-output (TiMIMO) dataset; moreover, by embedding physics/mathematics knowledge into multi-objective ensemble modeling, a physics-embedding multi-response regressor is proposed, to synchronously construct the surrogate model for highly complex multiple output responses. The validation effectiveness and benefit illustration of the X-PMR method are revealed by introducing three numerical systems (i.e., series system, parallel system and series/parallel hybrid system) and a real application system (i.e., dynamic aeroengine turbine blisk), in comparison with a number of state-of-the-art methods investigated in the literature. The current efforts can provide a novel sight to address the time-variant system reliability assessment problems.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"263 ","pages":"Article 111262"},"PeriodicalIF":9.4,"publicationDate":"2025-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144168326","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

The mean number of failed components in discrete time consecutive k-out-of-n: F system and its application to parameter estimation and optimal age-based preventive replacement 离散时间连续k- of-n: F系统中失效部件的平均数目及其在参数估计和基于年龄的最优预防性替换中的应用

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2025-05-23 DOI: 10.1016/j.ress.2025.111229

Serkan Eryilmaz , Cihangir Kan

引用次数: 0

Uncertainty evaluation of the debris flow impact considering spatially varying basal friction and solid concentration 考虑空间变化基础摩擦和固体浓度的泥石流影响的不确定性评价

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2025-05-23 DOI: 10.1016/j.ress.2025.111283

Hongyu Luo , Limin Zhang , Jian He , Jiawen Zhou

{"title":"Uncertainty evaluation of the debris flow impact considering spatially varying basal friction and solid concentration","authors":"Hongyu Luo , Limin Zhang , Jian He , Jiawen Zhou","doi":"10.1016/j.ress.2025.111283","DOIUrl":"10.1016/j.ress.2025.111283","url":null,"abstract":"<div><div>The inherent spatial variability of soil is reported to significantly impact landslide debris behaviors. In this study, the effect of spatial variability on the inundation and impact processes of debris flow is investigated using a multi-phase depth-averaged model. The dynamic process of a debris flow, considering spatial variabilities of basal friction and initial solid concentration, is explored via Monte Carlo simulation. The results show that due to the flow channel constrain and spatial averaging, the influences of spatial variability on the global impact of debris flow are not significant. However, remarkable influences on the local impact are found. From the upstream of flow channel to the downstream of river, there is a decreasing trend in uncertainties regarding the material composition and flow dynamics at local spots. In the flow channel, the mean values of flow depths are smaller than those in the deterministic analysis, while those of flow velocities are larger. In the river, both the mean values of flow depths and velocities are close to those in the deterministic analysis while their variations remain significant even downstream of river. The findings provide insights into the spatial variability effects on debris flow impact and facilitate risk assessment.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"263 ","pages":"Article 111283"},"PeriodicalIF":9.4,"publicationDate":"2025-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144147878","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

Estimation of economic loss and recover process after earthquake base on nighttime light data and time series model 基于夜间灯光数据和时间序列模型的地震后经济损失及恢复过程估算

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2025-05-23 DOI: 10.1016/j.ress.2025.111244

Jinpeng Zhao , Xiaojun Li , Su Chen

{"title":"Estimation of economic loss and recover process after earthquake base on nighttime light data and time series model","authors":"Jinpeng Zhao , Xiaojun Li , Su Chen","doi":"10.1016/j.ress.2025.111244","DOIUrl":"10.1016/j.ress.2025.111244","url":null,"abstract":"<div><div>This study develops a framework for predicting Nighttime Light (NTL) data trends without earthquakes and estimating economic losses and recovery processes after an earthquake. Utilizing Geographic Information System (GIS) technology, the framework unfolds through five stages: data acquisition and extraction, data transformation and calibration, model training and tuning for each 500 m x 500 m grid in the Jiuzhaigou earthquake area in Sichuan Province, China, and calculation of economic losses by comparing NTL predictions and actual observations. By integrating socio-economic characteristics and Damage Index data with NTL trends, and adjusting for model performance through hyperparameter tuning, this research quantifies economic impacts after an earthquake. Final estimates of economic losses and recovery are derived by allocating losses according to each grid’s calculated weight. This study not only predicts NTL data trends in scenarios without earthquake but also contributes novel methods for evaluating economic losses and recovery from earthquakes, presenting an effective framework for similar disaster impact studies.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"263 ","pages":"Article 111244"},"PeriodicalIF":9.4,"publicationDate":"2025-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144167597","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

Enhancing power grid resilience during tropical cyclones: Deep learning-based real-time wind forecast corrections for dynamic risk prediction 增强热带气旋期间电网的恢复能力：基于深度学习的实时风预报校正动态风险预测

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2025-05-23 DOI: 10.1016/j.ress.2025.111284

You Wu , Naiyu Wang , Xiubing Huang , Zhenguo Wang

{"title":"Enhancing power grid resilience during tropical cyclones: Deep learning-based real-time wind forecast corrections for dynamic risk prediction","authors":"You Wu , Naiyu Wang , Xiubing Huang , Zhenguo Wang","doi":"10.1016/j.ress.2025.111284","DOIUrl":"10.1016/j.ress.2025.111284","url":null,"abstract":"<div><div>Tropical cyclones (TCs) pose severe risks to power transmission systems, yet conventional Numerical Weather Prediction (NWP) models lack the resolution to resolve sub-kilometer wind dynamics critical for infrastructure risk assessment. This study introduces a Real-time Wind Forecast Correction (RWFC) model, a deep learning framework that dynamically refines mesoscale NWP forecasts during TCs by assimilating multi-source observational data. The RWFC integrates Convolutional Neural Network and Bidirectional Long Short-Term Memory (CNN-BiLSTM) to capture spatiotemporal wind-terrain interactions, with a custom loss function balancing accuracy and conservative bias for proactive risk mitigation. Validated during Typhoon Hagupit (2020) in Zhejiang Province, China, the RWFC reduced wind speed and direction mean absolute errors (MAE) by 78 % (6.47 to 1.41 m/s) and 50 % (53.57° to 26.79°), respectively, compared to raw NWP forecasts. By interpolating corrections from sparse observational sites, it achieved province-scale MAE reductions of 56 %, demonstrating robust generalizability. When applied to Zhejiang’s transmission grid, RWFC lowered the number of projected high-risk towers by 98 %, enabling precise, terrain-sensitive risk predictions. The framework bridges NWP’s physical rigor with deep learning’s adaptive capacity, offering a scalable solution for enhancing grid resilience during evolving TCs. This work advances real-time disaster management by transforming coarse forecasts into actionable, high-resolution risk insights for critical infrastructure.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"263 ","pages":"Article 111284"},"PeriodicalIF":9.4,"publicationDate":"2025-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144184860","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

Deep auto-encoded Conditional Gaussian Mixture Model for warranty claims forecasting 用于保修索赔预测的深度自编码条件高斯混合模型

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2025-05-23 DOI: 10.1016/j.ress.2025.111230

Wael Hassanieh , Abdallah Chehade , Vasiliy Krivtsov

{"title":"Deep auto-encoded Conditional Gaussian Mixture Model for warranty claims forecasting","authors":"Wael Hassanieh , Abdallah Chehade , Vasiliy Krivtsov","doi":"10.1016/j.ress.2025.111230","DOIUrl":"10.1016/j.ress.2025.111230","url":null,"abstract":"<div><div>Forecasting warranty claims enables (i) identifying and rectifying quality and reliability problems at the early stages of production, (ii) improving future product designs, and (iii) efficiently allocating financial resources for warranty and maintenance. Nevertheless, precise forecasting of warranty claims is challenging due to the data maturation phenomenon, which is characterized by challenges that include market demand variability, reporting delays, heterogeneous production quality, and customer late claim rush. This paper proposes the Deep auto-encoded Conditional Gaussian Mixture Model (DCGMM) for warranty claims forecasting. DCGMM is a novel hybrid Bayesian and deep learning framework. The method learns a prior joint distribution between auto-encoded temporal latent embeddings extracted from early observed cumulative warranty claims and long-term mature warranty claims based on historical products. DCGMM then uses Bayesian inference to forecast the mature warranty claims of an in-service product of interest conditioned on its auto-encoded embeddings extracted from its limited early claims observations. DCGMM is robust to product variability and complex temporal trends because of its ability to identify different cluster or sub-population behaviors. Furthermore, learning a lower-dimensional space is essential to achieving tractable Bayesian inferences. We validate the performance of the DCGMM for warranty claims forecasting on a large-scale automotive case study and compare it to other state-of-the-art time-series forecasting algorithms.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"263 ","pages":"Article 111230"},"PeriodicalIF":9.4,"publicationDate":"2025-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144135157","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