Structural Safety最新文献

{"title":"Nonparametric sector dependence modelling for the directional synthesis of local wind climate and building aerodynamic responses: Adaptive kernel-based approach","authors":"Nahom K. Berile ,&nbsp;Matiyas A. Bezabeh ,&nbsp;Seifu A. Bekele","doi":"10.1016/j.strusafe.2025.102671","DOIUrl":"10.1016/j.strusafe.2025.102671","url":null,"abstract":"<div><div>Accounting for the directionality of wind is crucial in estimating the response of buildings to wind load. Sector-based directionality techniques are widely used for analyzing directionality effects. In single- and multi-sector methods, directional sectors of the local wind climate and building aerodynamic responses are analyzed separately, while their statistical correlation is assumed to be fully dependent or independent, respectively. The multi-sector method, which is preferred for structural design due to its relative conservatism, requires the use of wide sectors to ensure the statistical independence assumption holds. This, in turn, requires interpolating aerodynamic response parameters, which is prone to errors due to rapid variations with small directional changes. Moreover, performance-based wind design (PBWD) approaches, as outlined in the American Society of Civil Engineers Prestandard for PBWD, require 10-degree or narrower sectors in aerodynamic response representation for detailed directional resolution. Narrow wind sectors often exhibit correlation, necessitating accurate dependence modelling. Parametric copula-based methods have been used to model sector correlations; however, they impose restrictive assumptions on dependence patterns. Therefore, this paper proposes a sector-based directionality technique with nonparametric dependence modelling using adaptive kernel density estimators. To demonstrate the applicability and accuracy of the method, wind responses of a prototype mass-timber building hypothetically located in three cities: i.e., Toronto (Canada), Melbourne (Australia), and Baltimore (USA), were predicted. The predictions were compared with responses empirically computed from historical records. The results demonstrated that the method extends the applicability of sector-based directionality analysis to narrow sectors, making it suitable for PBWD approaches.</div></div>","PeriodicalId":21978,"journal":{"name":"Structural Safety","volume":"119 ","pages":"Article 102671"},"PeriodicalIF":6.3,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145614543","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}

{"title":"Life-cycle fragility analysis of aging reinforced concrete bridges: A dynamic Bayesian network approach","authors":"Filippo Molaioni ,&nbsp;Charalampos P. Andriotis ,&nbsp;Zila Rinaldi","doi":"10.1016/j.strusafe.2025.102654","DOIUrl":"10.1016/j.strusafe.2025.102654","url":null,"abstract":"<div><div>Reinforced concrete bridges are predominant structural systems in transportation infrastructure. Their exposure to chronic and sudden stressors, such as corrosion and earthquakes, make them prone to risks with severe socioeconomic consequences. While time-dependent single-component seismic fragility formulations have advanced the frontier of life-cycle probabilistic risk assessment, state-dependent multi-component representations of damage and deterioration, paramount for structural integrity management, still lack a systematic probabilistic framework. This paper develops a novel dynamic Bayesian network to evaluate the life-cycle fragility functions of aging bridges, encapsulating the impacts of corrosion and seismic phenomena over time. The network establishes Markovian transitions among deterioration states for various bridge components integrating chloride diffusion and corrosion propagation models with non-stationary Gamma processes. A methodology for deriving and state-dependent fragility at the component and system levels depending on several deterioration scenarios is presented. Our framework is exemplified in an archetypical 4-span bridge, demonstrating the longitudinal effects of corrosion on the system’s seismic fragility for splash and atmospheric conditions. Insights from the multi-component analysis highlight the capabilities in understanding the pathologies and evolving mechanical interactions among components. The adaptability in accommodating on-site observations and advanced decision-making algorithms is discussed, demonstrating the suitability of the framework for applications requiring flexible and updatable virtual environments.</div></div>","PeriodicalId":21978,"journal":{"name":"Structural Safety","volume":"119 ","pages":"Article 102654"},"PeriodicalIF":6.3,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145467586","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}

{"title":"A composition of simplified physics-based model with neural operator for trajectory-level seismic response predictions of structural systems","authors":"Jungho Kim ,&nbsp;Sang-ri Yi ,&nbsp;Ziqi Wang","doi":"10.1016/j.strusafe.2025.102668","DOIUrl":"10.1016/j.strusafe.2025.102668","url":null,"abstract":"<div><div>Accurate prediction of nonlinear structural responses is essential for earthquake risk assessment and management. While high-fidelity nonlinear time history analysis provides the most comprehensive and accurate representation of the responses, it becomes computationally prohibitive for complex structural system models and repeated simulations under varying ground motions. To address this challenge, we propose a composite learning framework that integrates simplified physics-based models with a Fourier neural operator to enable efficient and accurate trajectory-level seismic response prediction. In the proposed architecture, a simplified physics-based model, obtained from techniques such as linearization, modal reduction, or solver relaxation, serves as a preprocessing operator to generate structural response trajectories that capture coarse dynamic characteristics. A neural operator is then trained to correct the discrepancy between these initial approximations and the true nonlinear responses, allowing the composite model to capture hysteretic and path-dependent behaviors. Additionally, a linear regression-based postprocessing scheme is introduced to further refine predictions and quantify associated uncertainty with negligible additional computational effort. The proposed approach is validated on three representative structural systems subjected to synthetic or recorded ground motions. Results show that the proposed approach consistently improves prediction accuracy over baseline models, particularly in data-scarce regimes. These findings demonstrate the potential of physics-guided operator learning for reliable and data-efficient modeling of nonlinear structural seismic responses.</div></div>","PeriodicalId":21978,"journal":{"name":"Structural Safety","volume":"119 ","pages":"Article 102668"},"PeriodicalIF":6.3,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145419158","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}

{"title":"Resilience based seismic design of CLT coupled walls and Glulam moment resisting frame system","authors":"Biniam Tekle Teweldebrhan ,&nbsp;Solomon Tesfamariam","doi":"10.1016/j.strusafe.2025.102667","DOIUrl":"10.1016/j.strusafe.2025.102667","url":null,"abstract":"<div><div>Seismic design philosophies have evolved significantly over the past several decades, shifting from life safety focused – prescriptive methods – towards approaches that also consider post-earthquake recovery, economic losses, and social impacts. This transition has led to the emergence of Resilience-Based Seismic Design (RBSD). While RBSD has been explored for concrete- and steel-based structural systems, its application to timber structures remains limited. Accordingly, this study develops a novel RBSD framework for a 20-storey timber building combining Cross-Laminated Timber Coupled Walls (CLTCWs) and a Glulam Moment-Resisting Frame (GMRF) to resist lateral loads. A baseline system is designed and assessed using FEMA P-58 methodology and the TREADS repair time model under multiple seismic intensity levels. Using this baseline, a Multi-Objective Optimization (MOO) framework is formulated with conflicting objectives: minimizing structural strength demands while maximizing its resilience. A dynamic deep learning-based surrogate model is trained to predict seismic performance across varying design parameters. Non-dominated Pareto-optimal solutions are obtained using a genetic algorithm and further evaluated through nonlinear time–history analyses. Results show that the optimized solutions achieve notable improvements in both structural efficiency and resilience performance compared to the baseline system. This research contributes a flexible and data-driven methodology for advancing the design of resilient, high-performance tall timber buildings.</div></div>","PeriodicalId":21978,"journal":{"name":"Structural Safety","volume":"119 ","pages":"Article 102667"},"PeriodicalIF":6.3,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145365538","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}

{"title":"Enhancing reliability analysis with limited observations: A statistical framework for system safety margins","authors":"Guillaume Perrin ,&nbsp;Julien Reygner ,&nbsp;Vincent Chabridon","doi":"10.1016/j.strusafe.2025.102670","DOIUrl":"10.1016/j.strusafe.2025.102670","url":null,"abstract":"<div><div>The reliability analysis of complex systems is crucial for cost-effective evaluations, particularly when using deterministic black-box models. This study examines system performance under uncertainty, where the input vector <span><math><mrow><mi>x</mi><mo>∈</mo><mi>X</mi><mo>⊂</mo><msup><mrow><mi>R</mi></mrow><mrow><mi>d</mi></mrow></msup></mrow></math></span> defines both system and environmental conditions, and failure is characterized by <span><math><mrow><mi>F</mi><mo>=</mo><mfenced><mrow><mi>x</mi><mo>∈</mo><mi>X</mi><mo>∣</mo><mi>y</mi><mrow><mo>(</mo><mi>x</mi><mo>)</mo></mrow><mo>⩽</mo><msup><mrow><mi>s</mi></mrow><mrow><mo>⋆</mo></mrow></msup></mrow></mfenced></mrow></math></span>, with <span><math><mi>y</mi></math></span> the variable of interest and <span><math><mrow><msup><mrow><mi>s</mi></mrow><mrow><mo>⋆</mo></mrow></msup><mo>∈</mo><mi>R</mi></mrow></math></span> a given threshold. Since <span><math><mi>x</mi></math></span> is uncertain, a probabilistic analysis is required to ensure robust safety assessments. Such an analysis typically involves two key steps: first, estimating the system’s probability of failure (noted <span><math><msub><mrow><mi>p</mi></mrow><mrow><mtext>f</mtext></mrow></msub></math></span>), and then, evaluating it against safety standards or expert knowledge. While considerable effort has been invested in proposing efficient methods for estimating <span><math><msub><mrow><mi>p</mi></mrow><mrow><mtext>f</mtext></mrow></msub></math></span>, little attention has been paid to the decision phase, which should take into account the uncertainties. This work focuses on the definition and use of safety margins in system reliability analysis with a final decision making purpose, especially when the knowledge of the input vectors <span><math><mi>x</mi></math></span> is limited to a finite set of <span><math><mi>n</mi></math></span> observations. A key distinction is made between cases where <span><math><mi>n</mi></math></span> is large or small relative to <span><math><mrow><mn>1</mn><mo>/</mo><msub><mrow><mi>p</mi></mrow><mrow><mtext>f</mtext></mrow></msub></mrow></math></span>. The main contributions of the paper focus on scenarios with small <span><math><mi>n</mi></math></span> and propose two approaches for defining reasonable safety margins. The first estimates the probability distribution of <span><math><mi>x</mi></math></span>, while the second, based on extreme value theory, directly assesses the tail behavior of the output distribution. The proposed framework is validated through numerical case studies.</div></div>","PeriodicalId":21978,"journal":{"name":"Structural Safety","volume":"119 ","pages":"Article 102670"},"PeriodicalIF":6.3,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145569093","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}

{"title":"Probabilistic failure mode-dependent reconstruction of the force-deformation response in reinforced concrete shear walls","authors":"Pouya Ebrahimi ,&nbsp;Amir Hossein Asjodi ,&nbsp;Kiarash M. Dolatshahi ,&nbsp;Henry V. Burton","doi":"10.1016/j.strusafe.2025.102669","DOIUrl":"10.1016/j.strusafe.2025.102669","url":null,"abstract":"<div><div>This paper aims to probabilistically reconstruct the force–deformation envelope response (or backbone curve) of reinforced concrete shear walls (RCSW) considering the uncertainties in the predictive model accuracy and material properties. A mean backbone curve and dispersion band are used to describe the probability distribution for the complete RCSW backbone curve, based on the mechanical and geometric properties of the wall. A database that includes the mechanical characteristics, cyclic data, and crack patterns for 249 RCSWs subjected to quasi-static cyclic loading is utilized. Based on the observed damage and cyclic behavior, each specimen is classified into one of three failure modes: shear, shear-flexure, or flexural. Then, a clustering algorithm is used to identify the failure mode based on critical points along the backbone curve. Using the similarity of backbone curves within each group, a hybridity index is derived to indicate the contribution of specific failure modes to the ultimate cyclic behavior. The hybridity index, along with wall geometry and mechanical properties, are used to reconstruct the full nonlinear backbone curve. The results show that considering the failure mode significantly improves the accuracy of the reconstructed mean backbone cure. Specifically, the coefficient of determination is increased by up to 0.46 relative to when the failure mode is not considered. The variability in the reconstructed backbone curve due to uncertainties in the material properties and predictive model accuracy are compared.</div></div>","PeriodicalId":21978,"journal":{"name":"Structural Safety","volume":"119 ","pages":"Article 102669"},"PeriodicalIF":6.3,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145569153","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}

{"title":"A probabilistic framework to construct tropical cyclone loss models for building portfolios","authors":"Yu Liang ,&nbsp;Hao Zhang ,&nbsp;Cao Wang ,&nbsp;Diqi Zeng","doi":"10.1016/j.strusafe.2025.102665","DOIUrl":"10.1016/j.strusafe.2025.102665","url":null,"abstract":"<div><div>Tropical cyclones (TCs) evolve over time and space and can cause substantial damage to building portfolios. Therefore, timely and accurate TC damage assessment is essential for effective risk management. One practical approach is to establish a relationship between hazard intensity (e.g., wind speeds) and regional damage. However, when the study area is large, spatial heterogeneity, such as clustered building distributions, terrain variability, and spatial variations in wind speeds, can hinder accurate modelling of the hazard-damage relationship. To address this challenge, the present study employs a spatial clustering algorithm to divide the entire area into multiple sub-regions with relatively homogeneous characteristics. For each sub-region, a TC loss model is developed as a function of wind speed at the sub-regional centroid and the corresponding building portfolio loss ratio. In practice, losses in all sub-regions are first assessed individually and then aggregated to estimate the total regional loss. This divide-and-aggregate approach significantly improves the accuracy and applicability of TC loss modelling and can be readily applied to various contexts, such as long-term risk management in large-scale communities.</div></div>","PeriodicalId":21978,"journal":{"name":"Structural Safety","volume":"119 ","pages":"Article 102665"},"PeriodicalIF":6.3,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145323615","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}

{"title":"Aleatory and epistemic uncertainty in reliability analysis: An engineering perspective","authors":"Pei-Pei Li ,&nbsp;Marcos A. Valdebenito ,&nbsp;Chao Dang ,&nbsp;Michael Beer ,&nbsp;Matthias G.R. Faes","doi":"10.1016/j.strusafe.2025.102666","DOIUrl":"10.1016/j.strusafe.2025.102666","url":null,"abstract":"<div><div>In engineering applications, aleatory and epistemic uncertainties often coexist and interact. Therefore, accurately modeling these two types of uncertainty is critical for reliability analysis and uncertainty-aware decision making. This is for instance the case when quantifying failure probabilities of engineering structures under consideration of incomplete, insufficient, imperfect, or imprecise data or knowledge. Indeed, in such a case, the failure probability can at best be described using set-theoretical or Bayesian descriptors, rather than as a crisp number to explicitly acknowledge this epistemic uncertainty. However, despite this problem being well-described in theory, we observe that there still exists a gap between the theoretical developments on the one hand, and practical engineering applications of the uncertainty modeling approaches on the other. More precisely, even though the treatment of aleatory and epistemic uncertainty is well understood, they are often still mixed implicitly, or even explicitly in engineering calculations. Therefore, this paper provides a practical engineering guide that should help select the appropriate modeling framework, be it p-boxes, fuzzy probability models, or hierarchical probability approaches, when faced with problems that are affected by both aleatory and epistemic uncertainty. By assessing the type and extent of the information and the purpose of the analysis, this work provides specific recommendations for choosing appropriate modeling methods and presents a comprehensive analysis of failure probability. Additionally, this work highlights the importance of sensitivity analysis in identifying the key parameters that most influence the failure probability. This focus enables engineers to prioritize target data collection, thereby reducing epistemic uncertainty and enhancing the credibility of reliability assessment.</div></div>","PeriodicalId":21978,"journal":{"name":"Structural Safety","volume":"119 ","pages":"Article 102666"},"PeriodicalIF":6.3,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145517509","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}

{"title":"MetaIMNet: A physics-informed neural network architecture for surrogate response and fragility modeling of structures subjected to time-varying hazard loads","authors":"Sushreyo Misra,&nbsp;Paolo Bocchini","doi":"10.1016/j.strusafe.2025.102650","DOIUrl":"10.1016/j.strusafe.2025.102650","url":null,"abstract":"<div><div>Extreme events such as earthquakes and hurricanes cause widespread damage and disruption to infrastructure assets such as buildings and bridges. Catastrophe modeling and accurate extreme event risk and resilience assessment require portfolio-level fragility functions of these assets, which involve the establishment of functional relationships between a relevant peak response quantity, also known as the engineering demand parameter (EDP), and select features characterizing the hazard. Given the computational demands of analyzing several statistical combinations of hazard and structural features, while running nonlinear time history analyses for each combination, surrogate demand models relating peak EDP to relevant intensity measures (IMs) of the input time history are popular. Although traditional IMs such as peak accelerations and velocities, average velocities, and peak spectral accelerations determined <em>a priori</em> have been traditionally found to be effective predictors of response and damage, their use in surrogate models in fragility model development introduces additional model uncertainties. In a bid to enable more robust and accurate surrogate modeling, we propose MetaIMNet; a physics-informed framework based on a neural network that simultaneously extracts key features from the time history of the load and leverages these features for structure specific response prediction. The framework is illustrated through a case study which shows that it outperforms traditional surrogate modeling strategies at a nominal added computational cost associated with model training, and can be used as an effective surrogate model for developing fragility functions for a wide range of hazards and structures.</div></div>","PeriodicalId":21978,"journal":{"name":"Structural Safety","volume":"118 ","pages":"Article 102650"},"PeriodicalIF":6.3,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145045448","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}

{"title":"Seismic risk assessment methodology for large-span CFST arch bridges in near-fault areas based on fragility analysis","authors":"Lihan Xu ,&nbsp;Lueqin Xu ,&nbsp;Dong Xie ,&nbsp;Jianting Zhou","doi":"10.1016/j.strusafe.2025.102656","DOIUrl":"10.1016/j.strusafe.2025.102656","url":null,"abstract":"<div><div>Large-span concrete-filled steel tube (CFST) arch bridges are widely built in high-seismicity mountainous areas in China due to their low maintenance costs and high adaptability to the challenging construction environments. The dynamic response of such bridges under seismic loading is highly complex, and their seismic performance is a major concern for multiple stakeholders. This study proposes a seismic risk assessment method for large-span CFST arch bridges from a risk perspective, based on seismic fragility analysis. The method begins with seismic hazard analysis of the bridge site, followed by seismic risk scenario identification of the bridge through fragility analysis, then quantifies the seismic risk scenarios from the perspective of economic losses, and finally evaluates the quantified results of discrete risk scenarios based on tolerance theory. A CFST arch bridge located in a near-fault area is analyzed as a case study, with two design schemes and five annual earthquake frequencies considered to validate the feasibility of the proposed method. The research results show that the seismic risk assessment method effectively identifies risk scenarios and their characteristics across different design schemes and seismic frequencies. Additionally, as the method presents results through macro risk tolerance zone divisions, it offers more intuitive and stakeholder-friendly outputs compared to traditional engineering-technology-based assessments (e.g., seismic fragility curves). Overall, the proposed method serves as a robust decision-making tool for the design, operation, and maintenance of large-span CFST arch bridges and similar structures with complex seismic responses.</div></div>","PeriodicalId":21978,"journal":{"name":"Structural Safety","volume":"118 ","pages":"Article 102656"},"PeriodicalIF":6.3,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145118453","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}