Reliability Engineering & System Safety最新文献_第4页

A novel integrated dependence assessment method for human reliability analysis under uncertainty and expert disagreement 一种新的不确定和专家分歧下的人的可靠性综合依赖评估方法

IF 11 1区工程技术

Reliability Engineering & System Safety Pub Date : 2026-09-01 Epub Date: 2026-01-30 DOI: 10.1016/j.ress.2026.112299

Fei Gao

{"title":"A novel integrated dependence assessment method for human reliability analysis under uncertainty and expert disagreement","authors":"Fei Gao","doi":"10.1016/j.ress.2026.112299","DOIUrl":"10.1016/j.ress.2026.112299","url":null,"abstract":"<div><div>Dependence assessment is a critical concern in human reliability analysis (HRA), particularly under uncertain and conflicting expert evaluations. This study operationalizes a structured integration of a consensus-reaching process (CRP), the full consistency method (FUCOM), and Dempster-Shafer evidence theory (DSET) to support dependence assessment and the calculation of conditional human error probability (CHEP) in a THERP-compatible manner. First, expert judgments on dependence levels are represented as basic belief assignments and reconciled through a fidelity-constrained CRP that reduces disagreement under an explicit agreement requirement while remaining close to the original assessments. Subsequently, FUCOM is used to derive internally consistent importance coefficients for dependence-influencing factors, reducing elicitation burden and mitigating incoherent factor prioritization. The consensus-refined belief structures and FUCOM-based coefficients are then aggregated using DSET and mapped to CHEP. A case study demonstrates the feasibility of the proposed scheme and shows that the resulting CHEP is numerically plausible and comparable to those obtained by representative dependence assessment methods, while providing a more transparent and traceable chain from elicited expert inputs to the final conditional probability. Sensitivity analyses further indicate that the results remain stable under reasonable variations of key consensus parameters and factor weights. The proposed framework offers a defensible process for panel-based dependence assessment in safety-critical applications where disagreement management and weighting consistency is required.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"273 ","pages":"Article 112299"},"PeriodicalIF":11.0,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146174842","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

Active learning Kriging with functional dimension reduction for reliability analysis of stochastic dynamical systems 基于功能降维的主动学习Kriging随机动力系统可靠性分析

IF 11 1区工程技术

Reliability Engineering & System Safety Pub Date : 2026-09-01 Epub Date: 2026-02-05 DOI: 10.1016/j.ress.2026.112360

Zhouzhou Song , Chao Dang , Marcos A. Valdebenito , Matthias G.R. Faes

{"title":"Active learning Kriging with functional dimension reduction for reliability analysis of stochastic dynamical systems","authors":"Zhouzhou Song , Chao Dang , Marcos A. Valdebenito , Matthias G.R. Faes","doi":"10.1016/j.ress.2026.112360","DOIUrl":"10.1016/j.ress.2026.112360","url":null,"abstract":"<div><div>In engineering applications, it is important to evaluate the reliability of dynamical systems under various uncertainties arising from materials, manufacturing processes, and external excitations. Surrogate models are widely employed to enable efficient reliability analysis in complex, computationally intensive engineering problems. However, building high-accuracy surrogate models for estimating dynamic systems’ reliability with limited computational resources remains a significant challenge. This paper presents a novel active learning Kriging method based on functional dimension reduction (AKFDR) for the efficient estimation of first-passage failure probabilities of stochastic dynamical systems. In this approach, Kriging surrogate models are constructed in a latent functional space obtained through functional dimension reduction, enabling probabilistic predictions of dynamic responses. By leveraging the prediction uncertainty and the concept of trajectory misclassification probability (TMP), a new learning function incorporating a weighted correlation criterion is then developed to guide the selection of the best next sample for model enhancement. Furthermore, an error-based stopping criterion is proposed to judge the convergence of the active learning process. The final surrogate model is then used to estimate the first-passage failure probability via Monte Carlo simulation. Through three numerical examples of varying dimensionality and complexity, it is shown that the proposed method is efficient and accurate for first-passage probability evaluation of stochastic dynamical systems.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"273 ","pages":"Article 112360"},"PeriodicalIF":11.0,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146174995","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 hybrid neural network-based concrete gravity dam seismic response prediction method quantifying reservoir water level uncertainty 基于混合神经网络的混凝土重力坝地震反应预测方法，量化水库水位不确定性

IF 11 1区工程技术

Reliability Engineering & System Safety Pub Date : 2026-09-01 Epub Date: 2026-02-05 DOI: 10.1016/j.ress.2026.112366

Bo Liu , Qiang Xu , Jianying Xing , Jianyun Chen , Mingming Wang , Jing Li , Tianran Zhang

{"title":"A hybrid neural network-based concrete gravity dam seismic response prediction method quantifying reservoir water level uncertainty","authors":"Bo Liu , Qiang Xu , Jianying Xing , Jianyun Chen , Mingming Wang , Jing Li , Tianran Zhang","doi":"10.1016/j.ress.2026.112366","DOIUrl":"10.1016/j.ress.2026.112366","url":null,"abstract":"<div><div>Accurate real-time prediction of the seismic response of gravity dams is critical for their safety assessment, however, the seismic response characteristics of gravity dams are highly variable due to variations in the gravity dam-reservoir-foundation system (e.g., reservoir water level) and seismic characteristics (e.g., impulse characteristics). To address these challenges, this study proposes a hybrid neural network model (SW-HNN) that integrates a new system feature attention mechanism and a unique wavelet decomposition-based impulse identification module to predict the impulse seismic response of gravity dams, considering reservoir water level variability. The model effectively captures the complex interrelations between system characteristics of the gravity dam-reservoir-foundation system, ground motion impulse characteristics, and dam responses. To further enhance model performance, an improved balanced sampling technique is developed for ground motion datasets, which enriches the feature set and mitigates the influence of imbalanced feature distributions. The required datasets for model training and validation are generated through nonlinear time-history analyses of gravity dam-reservoir-foundation systems with varying reservoir water levels. Experimental results confirm the accuracy and robustness of the SW-HNN model. The validity and superiority of the SW-HNN model are further verified by ablation analysis and comparison experiments. Additionally, the SW-HNN is an interpretable deep learning model capable of ranking system features by evaluating changes in its internal parameters.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"273 ","pages":"Article 112366"},"PeriodicalIF":11.0,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146174951","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

Robust decision-based proactive prevention method for renewable distribution systems under typhoon involved with rainstorm events 基于鲁棒决策的台风卷入暴雨条件下可再生配电系统主动预防方法

IF 11 1区工程技术

Reliability Engineering & System Safety Pub Date : 2026-09-01 Epub Date: 2026-01-23 DOI: 10.1016/j.ress.2026.112273

Wei Zhang , Cong Zhang , Dapeng Wang , Jiayong Li , Lipeng Zhu , Ke Zhou , Zhikang Shuai

{"title":"Robust decision-based proactive prevention method for renewable distribution systems under typhoon involved with rainstorm events","authors":"Wei Zhang , Cong Zhang , Dapeng Wang , Jiayong Li , Lipeng Zhu , Ke Zhou , Zhikang Shuai","doi":"10.1016/j.ress.2026.112273","DOIUrl":"10.1016/j.ress.2026.112273","url":null,"abstract":"<div><div>This paper proposes a novel proactive prevention method for renewable distribution systems (RDSs) to address the limited flexibility and economic inefficiency of existing approaches due to insufficient consideration of typhoon rainstorm evolution and component status uncertainties. First, a mechanism-based and data-driven uncertainty model for typhoon rainstorm is established, and a dual-uncertainty scenario generation method for disaster evolution paths and component statuses is further developed to capture their spatiotemporal impacts on the RDSs. Then, a robust decision-based proactive prevention framework for the RDSs is proposed considering the coupling among typhoon rainstorm disasters, system operating statuses, and proactive dispatching. This framework dynamically prioritizes scheduling schemes that maximize the number of out-of-service vulnerable components while meeting the system’s total load demand. Based on this, a multi-objective proactive prevention model is further formulated as a mixed-integer nonlinear programming (MINLP) problem, and a convex solution algorithm is developed to enhance computational efficiency. Finally, the proposed method is tested on the modified IEEE 33-bus system and the 10 kV actual distribution system in Southern China to validate its effectiveness. Numerical results show that the proposed method effectively reduces overall system vulnerability under typhoon rainstorm and improves the proactive prevention capability of the RDSs.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"273 ","pages":"Article 112273"},"PeriodicalIF":11.0,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146174919","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

Stochastic modeling of crack branching under uncertainties: A degradation branching framework 不确定条件下裂纹分支的随机建模：退化分支框架

IF 11 1区工程技术

Reliability Engineering & System Safety Pub Date : 2026-09-01 Epub Date: 2026-01-30 DOI: 10.1016/j.ress.2026.112316

Tong Wu , Ting Wang , Hanyu Li , Changxi Wang , Kang Li

{"title":"Stochastic modeling of crack branching under uncertainties: A degradation branching framework","authors":"Tong Wu , Ting Wang , Hanyu Li , Changxi Wang , Kang Li","doi":"10.1016/j.ress.2026.112316","DOIUrl":"10.1016/j.ress.2026.112316","url":null,"abstract":"<div><div>This paper presents a stochastic model for dynamic fracture branching propagation under uncertainties (DFBPU) that addresses key limitations of existing degradation branching models. Existing work often assumes a single branch initiation per event and thereby understates how the initiation of multiple branches in one branching impacts total degradation. DFBPU generalizes this setting by allowing a random number of initial branches, a random number of offspring branches at each branching instant, generation-dependent crack growth rates, and random branching times, yielding a comprehensive framework for dynamic fracture processes. We derive statistical properties of total degradation, including the mean and variance, the expected number of branches, and the system reliability function. The model is validated with experimental crack branching and propagation data and complemented by a single-factor Monte Carlo sensitivity analysis, which shows that the failure threshold, initial branches and generation-dependent growth parameters dominate the variability of system reliability, while other parameters play a comparatively minor role. Finally, because it treats total crack length as the degradation indicator, the framework is directly applicable to systems where leakage risk scales with the cumulative crack extent in length or area, such as underground nuclear/medical waste repository coatings.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"273 ","pages":"Article 112316"},"PeriodicalIF":11.0,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146175267","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

Investigation on new definition and proactive identification method of hazard 新危害定义及主动识别方法探讨

IF 11 1区工程技术

Reliability Engineering & System Safety Pub Date : 2026-09-01 Epub Date: 2026-02-04 DOI: 10.1016/j.ress.2026.112361

Qianru Lei, Cunbao Deng, Yansheng Wang, Zihao Zhao, Zhixin Jin

{"title":"Investigation on new definition and proactive identification method of hazard","authors":"Qianru Lei, Cunbao Deng, Yansheng Wang, Zihao Zhao, Zhixin Jin","doi":"10.1016/j.ress.2026.112361","DOIUrl":"10.1016/j.ress.2026.112361","url":null,"abstract":"<div><div>Hazard identification is a crucial task for preventing industrial accidents. However, traditional methods often treat hazards as isolated entities or static deviations, relying on historical data and linear logic, making it difficult to address dynamic interaction risks in complex systems. Although modern system safety theories emphasize systemicity, issues of insufficient practical applicability persist. This investigation holds that interaction between production activities (human, machine, management) and activity environment (environment factors in activity restriction domain) is the cause of safety issues. The safety structure composed of interaction between atomic activity factor (AAF) and atomic environment factor (AEF) is regarded as basic unit for analyzing safety issues. Hazards are redefined as safety structures that may lose stability, and classified into simple, combined, radiation and new hazards. Furthermore, the mechanisms through which hazards trigger single or multiple accidents are elucidated. Three identification criteria are established for different hazards. Its core idea is determining whether there are intersections between state set of the AEF and the environment instability domain. Finally, a hazard identification method is presented and applied to the coal mine production system. The results lay a foundation for precise control of hazards.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"273 ","pages":"Article 112361"},"PeriodicalIF":11.0,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146174759","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

An interpretable transfer bayesian method for remaining useful life prediction 剩余使用寿命预测的可解释迁移贝叶斯方法

IF 11 1区工程技术

Reliability Engineering & System Safety Pub Date : 2026-09-01 Epub Date: 2026-01-25 DOI: 10.1016/j.ress.2026.112283

Pengcheng Xu , Naipeng Li , Yaguo Lei , Xiang Li , Lei Song , Hao Sun

{"title":"An interpretable transfer bayesian method for remaining useful life prediction","authors":"Pengcheng Xu , Naipeng Li , Yaguo Lei , Xiang Li , Lei Song , Hao Sun","doi":"10.1016/j.ress.2026.112283","DOIUrl":"10.1016/j.ress.2026.112283","url":null,"abstract":"<div><div>Accurate and interpretable remaining useful life (RUL) prediction under domain shifts with streaming multi-sensor data remains challenging for field equipment. To address this challenge, an interpretable transfer Bayesian method is developed, integrating three key components: dynamic pseudo-domain generation (DPG), online Bayesian updating, and a pseudo-domain-based ensemble strategy. First, the DPG algorithm transforms source domains into pseudo-domain units whose degradation trajectories closely match those of the target domain. Second, a dual-scale distance is proposed to identify the cumulatively selected optimal pseudo-domain units, which are then utilized in the Bayesian updating of the degradation model. Third, adaptive weights are assigned to multi-sensor features based on the selected optimal pseudo-domain units, thereby improving RUL prediction accuracy and robustness. Finally, simulation studies and experimental validation on two real-world Stirling cryocooler datasets demonstrate that the proposed method outperforms existing methods in terms of both accuracy and robustness.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"273 ","pages":"Article 112283"},"PeriodicalIF":11.0,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146174763","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

Harnessing the integrated statistical machine learning for traffic crash injury-severity modeling 利用综合统计机器学习进行交通碰撞伤害严重程度建模

IF 11 1区工程技术

Reliability Engineering & System Safety Pub Date : 2026-09-01 Epub Date: 2026-01-29 DOI: 10.1016/j.ress.2026.112321

Pengfei Cui , Chenzhu Wang , Mohamed Abdel-Aty , Xiaobao Yang , Xingchen Zhang , Lishan Sun

{"title":"Harnessing the integrated statistical machine learning for traffic crash injury-severity modeling","authors":"Pengfei Cui , Chenzhu Wang , Mohamed Abdel-Aty , Xiaobao Yang , Xingchen Zhang , Lishan Sun","doi":"10.1016/j.ress.2026.112321","DOIUrl":"10.1016/j.ress.2026.112321","url":null,"abstract":"<div><div>Modeling the severity of traffic crash remains challenging due to the complexity, uncertainty, and heterogeneity inherent in crash datasets. Traditional statistical models often overlook interactions and structural dependencies, while machine learning methods, though effective with large datasets, struggle to capture spatial and temporal dynamics. To address these gaps, we propose the Latent Gaussian Process with Tree-Boosting Model (LGPBoost), which integrates tree-based machine learning with Gaussian process mixed effects models. This framework accounts for spatial, temporal, and grouped dependencies while capturing nonlinear feature–outcome relationships. To demonstrate the superiority of LGPBoost, we conducted a well-designed simulation experiment focused on datasets characterized by complex feature relationships and latent grouped random effects, as well as spatial and temporal variabilities. Applying the method to Florida motorcycle crashes (2014–2023) revealed that rural and less urbanized areas face significantly higher severe and fatal crash risks, underscoring the need for targeted enforcement and infrastructure improvements. Temporal instability analysis further showed evolving crash risks across regions, particularly in non-urban regions. By unifying spatial heterogeneity and temporal variability, LGPBoost provides a rigorous benchmark for reliability-oriented crash severity modeling, offering a comprehensive framework to identify risk factors, quantify non-linear effects, and capture intrinsic spatial-temporal dynamics.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"273 ","pages":"Article 112321"},"PeriodicalIF":11.0,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146174831","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

Nonlinear random walks on hypergraphs characterized by higher-order interactions 以高阶相互作用为特征的超图上的非线性随机行走

IF 11 1区工程技术

Reliability Engineering & System Safety Pub Date : 2026-09-01 Epub Date: 2026-01-31 DOI: 10.1016/j.ress.2026.112307

Meng Li , Xin Lu , Shuiling Shi , Leyang Xue , Zengru Di

{"title":"Nonlinear random walks on hypergraphs characterized by higher-order interactions","authors":"Meng Li , Xin Lu , Shuiling Shi , Leyang Xue , Zengru Di","doi":"10.1016/j.ress.2026.112307","DOIUrl":"10.1016/j.ress.2026.112307","url":null,"abstract":"<div><div>Random walks, as one of the classical dynamics on networks, are capable of extracting information on the structure of interacting systems. While existing studies have extended classical network random walks to higher-order networks, prevailing models primarily assume linear relationships between transition probabilities and node hyperdegrees or hyperedge sizes, which inadequately represent the nonlinear properties of higher-order interactions. To address this, we propose a nonlinear random walk on hypergraphs that explicitly considers higher-order collaborative structures and nonlinear dynamics. By introducing a nonlinear mapping between transition probabilities and node hyperdegrees, we go beyond the linear assumption constraints of traditional random walks. Specifically, the probability of a node selecting a hyperedge is inversely proportional to a power function of its hyperdegree, where the power exponent is determined by the interaction order. We first conduct qualitative analysis of the model on star-clique structures, comparing it with classical random walk and linear random walk to reveal the mechanism by which higher-order interactions influence node importance rankings. Subsequently, we conducted node removal experiments on three large-scale datasets to validate the effectiveness of the model by comparing three structural integrity metrics. The results indicate that the proposed model consistently outperforms both Classical and Linear models across all datasets and metrics. These findings confirm that accounting for the nonlinear characteristics of higher-order interactions is essential for both accurately identifying critical nodes and understanding system robustness.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"273 ","pages":"Article 112307"},"PeriodicalIF":11.0,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146174835","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

Structural fragility curves for brick-concrete buildings subjected to debris flow loading in northern China using momentum flux approach 基于动量通量法的华北地区砖混结构在泥石流荷载作用下的易损性曲线

IF 11 1区工程技术

Reliability Engineering & System Safety Pub Date : 2026-09-01 Epub Date: 2026-02-01 DOI: 10.1016/j.ress.2026.112344

Xinren Zhang , Ying Wang , Mengxia Zhao , Jing Qi , Hao Chang , Yu Chen

{"title":"Structural fragility curves for brick-concrete buildings subjected to debris flow loading in northern China using momentum flux approach","authors":"Xinren Zhang , Ying Wang , Mengxia Zhao , Jing Qi , Hao Chang , Yu Chen","doi":"10.1016/j.ress.2026.112344","DOIUrl":"10.1016/j.ress.2026.112344","url":null,"abstract":"<div><div>Debris flows in northern China are increasing in frequency and intensity, highlighting the need for reliable vulnerability assessment frameworks. This study develops fragility curves for brick-concrete buildings by integrating momentum flux with structural lateral displacement response. Following Typhoon Doksuri in 2023, continuous rainfall triggered multiple debris flows in the Beijing–Tianjin–Hebei region. Field surveys of 583 buildings provided data supporting momentum flux-based fragility curves. Identified thresholds for complete, extensive, and moderate damage are 31.01, 22.64, and 13.74 m³/s². The model parameter <em>β</em>, empirically estimated from regional building damage data, enhances the predictive accuracy of vulnerability curves across hazard intensities and more realistically captures the variability among building populations than theoretical estimates. Results indicate that building stability increases with the number of floors, reducing damage probability—for instance, two-storey buildings show 5% lower damage probability than single-storey ones under moderate damage conditions. Below 80 m³/s², infilled-frame buildings (C3L) in northern China have up to 30% higher damage probability than U.S. bare-frame buildings (C1L), which neglect infill walls; above this, hazard intensity dominates and probabilities converge. Newer buildings show 15% lower damage probability, reflecting improved resilience. The proposed fragility curves serve as a physics-based probabilistic tool for risk assessment, supporting debris flow disaster prevention and mitigation in mountainous areas of northern China.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"273 ","pages":"Article 112344"},"PeriodicalIF":11.0,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146174840","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