Structural Safety最新文献

{"title":"A practical framework for determining target reliability indices for the assessment of existing structures based on risk-informed decision-making","authors":"Jianxu Su ,&nbsp;Junping Zhang ,&nbsp;Colin C. Caprani ,&nbsp;Junyong Zhou","doi":"10.1016/j.strusafe.2025.102583","DOIUrl":"10.1016/j.strusafe.2025.102583","url":null,"abstract":"<div><div>Target reliability levels define structural safety requirements. Most current studies on target reliability indices (<span><math><mrow><msub><mi>β</mi><mi>t</mi></msub></mrow></math></span>) have focused on reliability-based design for new structures. However, existing structures face significant safety challenges due to ongoing aging and financial constraints that limit maintenance and reinforcement efforts. Therefore, determining appropriate <span><math><mrow><msub><mi>β</mi><mi>t</mi></msub></mrow></math></span> for the assessment of existing structures is crucial to balance the tradeoff between safety and economy. This study develops a practical, risk-informed framework to streamline the determination of <span><math><mrow><msub><mi>β</mi><mi>t</mi></msub></mrow></math></span> for the reliability assessment of existing structures. It involves six critical steps including context definition, structural system modeling, failure statistics analysis, risk criteria establishment, and <span><math><mrow><msub><mi>β</mi><mi>t</mi></msub></mrow></math></span> selection. The framework’s practical application is carefully demonstrated through a case study centered on the reliability assessment of existing medium- and small-span (MS) bridges in China. A database was compiled for failure statistics of MS bridges, documenting 241 bridge collapse incidents in China spanning from 1983 to 2024. The statistical analysis of lethality ratios and fatalities from these failure events is incorporated into individual risk criteria, group risk criteria, cost optimization, and the marginal lifesaving cost principle. Using these criteria, alongside a refined as low as reasonably practicable (ALARP) principle, informed decisions are made on selecting <span><math><mrow><msub><mi>β</mi><mi>t</mi></msub></mrow></math></span> for reliability differentiation. Finally, three safety levels of <span><math><mrow><msub><mi>β</mi><mi>t</mi></msub></mrow></math></span> are recommended for the bridge system as well as individual components. The proposed methodology framework, as demonstrated in the case study on MS bridges in China, can be readily applicable to the determination of <span><math><mrow><msub><mi>β</mi><mi>t</mi></msub></mrow></math></span> for various other existing civil structures.</div></div>","PeriodicalId":21978,"journal":{"name":"Structural Safety","volume":"114 ","pages":"Article 102583"},"PeriodicalIF":5.7,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143527432","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":"Integrating risk perceptions in a value of information framework using cumulative prospect theory","authors":"Zaid Y Mir Rangrez ,&nbsp;Jayadipta Ghosh ,&nbsp;Colin Caprani ,&nbsp;Siddhartha Ghosh","doi":"10.1016/j.strusafe.2025.102573","DOIUrl":"10.1016/j.strusafe.2025.102573","url":null,"abstract":"<div><div>Value of information (VoI) analysis provides a framework that can be used to decide on an optimal monitoring strategy, to carry out an efficient maintenance of civil infrastructure. Existing VoI frameworks adopt utility functions to characterize the risk appetite of an asset manager based on expected utility theory (EUT). However, these utility functions cannot predict the decision choices under uncertainty resulting from failure risk perceptions. Cumulative prospect theory (CPT) is a comprehensive model for characterizing an asset manager’s risk appetite and perception. CPT captures both, the preference for different action outcomes using a value function and corresponding risk perceptions exhibited by an asset manager using a probability weight function. The present study proposes a CPT-based VoI framework which integrates risk perceptions and appetite within the VoI analysis. The proposed framework is implemented to investigate the sensitivity of the resulting expected VoI and the monitoring decisions to risk perception profiles. It is observed that the VoI is sensitive to the risk perception profile of an asset manager. An in-depth analysis of the decision patterns reveal that the risk profile affects the choice of prior optimal action that in turn dictates which type of posterior actions contribute positively or negatively towards the cost savings when referenced to the cost of prior optimal action. Based on these finding, the paper recommends to calibrate an asset manager’s risk perception profile to predict the decisions that an asset manager perceives as optimal for a given failure risk, and to evaluate the expected VoI resulting from such decisions.</div></div>","PeriodicalId":21978,"journal":{"name":"Structural Safety","volume":"115 ","pages":"Article 102573"},"PeriodicalIF":5.7,"publicationDate":"2025-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143576880","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":"In Memoriam of Ove Dalager Ditlevsen","authors":"Armen Der Kiureghian","doi":"10.1016/j.strusafe.2025.102585","DOIUrl":"10.1016/j.strusafe.2025.102585","url":null,"abstract":"","PeriodicalId":21978,"journal":{"name":"Structural Safety","volume":"114 ","pages":"Article 102585"},"PeriodicalIF":5.7,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143508593","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":"Response probability distribution estimation of expensive computer simulators: A Bayesian active learning perspective using Gaussian process regression","authors":"Chao Dang ,&nbsp;Marcos A. Valdebenito ,&nbsp;Nataly A. Manque ,&nbsp;Jun Xu ,&nbsp;Matthias G.R. Faes","doi":"10.1016/j.strusafe.2025.102579","DOIUrl":"10.1016/j.strusafe.2025.102579","url":null,"abstract":"<div><div>Estimation of the response probability distributions of computer simulators subject to input random variables is a crucial task in many fields. However, achieving this task with guaranteed accuracy remains an open computational challenge, especially for expensive-to-evaluate computer simulators. In this work, a Bayesian active learning perspective is presented to address the challenge, which is based on the use of the Gaussian process (GP) regression. First, estimation of the response probability distributions is conceptually interpreted as a Bayesian inference problem, as opposed to frequentist inference. This interpretation provides several important benefits: (1) it quantifies and propagates discretization error probabilistically; (2) it incorporates prior knowledge of the computer simulator, and (3) it enables the effective reduction of numerical uncertainty in the solution to a prescribed level. The conceptual Bayesian idea is then realized by using the GP regression, where we derive the posterior statistics of the response probability distributions in semi-analytical form and also provide a numerical solution scheme. Based on the practical Bayesian approach, a Bayesian active learning (BAL) method is further proposed for estimating the response probability distributions. In this context, the key contribution lies in the development of two crucial components for active learning, i.e., stopping criterion and learning function, by taking advantage of the posterior statistics. It is empirically demonstrated by five numerical examples that the proposed BAL method can efficiently estimate the response probability distributions with desired accuracy.</div></div>","PeriodicalId":21978,"journal":{"name":"Structural Safety","volume":"114 ","pages":"Article 102579"},"PeriodicalIF":5.7,"publicationDate":"2025-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143464183","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}

{"title":"A novel simulation method for the multivariate non-stationary non-Gaussian wind speed based on KL expansion and translation process theory","authors":"Fengbo Wu ,&nbsp;Yu Wu ,&nbsp;Ning Zhao","doi":"10.1016/j.strusafe.2025.102584","DOIUrl":"10.1016/j.strusafe.2025.102584","url":null,"abstract":"<div><div>Accurate simulation of multivariate non-stationary non-Gaussian wind speed is the premise of evaluating the response of nonlinear structures. The methods based on Karhunen-Loève (KL) expansion and translation process method are extensively applied to predict non-stationary non-Gaussian simulation because it is easy for use and has relatively satisfactory simulation efficiency. However, these methods perform poorly in simulating the non-stationary strongly non-Gaussian process, especially the wind speed processes with highly skewed or bimodal distributions. This study comprehensively utilizes the KL expansion, the maximum entropy methods (MEM), and piecewise Hermite polynomial model (PHPM) to formulate a novel approach for simulating multivariate non-stationary non-Gaussian wind speed. In this method, the KL expansion is firstly used to generate the non-stationary Gaussian process. Then, a new strategy, the MEM is used to approximate the probability density function (PDF) of the target process which is then used to establish PHPM, is proposed to achieve the accurate and efficient simulation of non-stationary non-Gaussian process. The numerical results show that the proposed method has better simulation accuracy than traditional KL-based methods for non-stationary strongly non-Gaussian wind speed processes, especially the processes with highly skewed or bimodal distributions. Note that the proposed method can also be applied to simulate other non-Gaussian non-stationary excitations such as the wind pressure processes influenced by complex effects such as interference effect.</div></div>","PeriodicalId":21978,"journal":{"name":"Structural Safety","volume":"114 ","pages":"Article 102584"},"PeriodicalIF":5.7,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143464184","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":"Advanced terrain-adaptive tropical cyclone wind field modeling using deep learning for infrastructure resilience planning","authors":"Yilin Shi ,&nbsp;Naiyu Wang ,&nbsp;Bruce R. Ellingwood","doi":"10.1016/j.strusafe.2025.102580","DOIUrl":"10.1016/j.strusafe.2025.102580","url":null,"abstract":"<div><div>Tropical cyclones pose significant threats to the resilience of coastal communities, underscoring the need for reliable wind field models to support robust hazard analyses. Parametric wind models (PWMs), despite their computational efficiency, often fall short in capturing intricate wind-terrain interactions, leading to inaccurate resilience evaluations for spatially-distributed civil infrastructure systems situated in complex terrains. This study introduces an innovative approach that integrates the strengths of numerical wind models to handle intricate terrain features into PWMs through a deep learning-based Convolutional Neural Network for Terrain Modification (CNN-TM). The CNN-TM model, trained over 3 million km² of numerically simulated high-resolution wind fields, enhances terrain representation in PWMs by generating 450 m-resolution terrain-modified wind fields for both wind speed and direction. The accuracy and efficiency of this integration are validated across multiple scales: grid (∼0.2 km²), patch (∼506 km²), and region (∼34,000 km²). Applications during Typhoon Hagupit (2020) in Zhejiang Province, China, demonstrate its practical effectiveness across a 105,000 km² area. By leveraging deep learning to synergize numerical and parametric models, the CNN-TM model addresses limitations of traditional PWMs and provides a robust tool for resilience-oriented decision-making for infrastructure systems in coastal regions characterized by complex terrains.</div></div>","PeriodicalId":21978,"journal":{"name":"Structural Safety","volume":"114 ","pages":"Article 102580"},"PeriodicalIF":5.7,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143480618","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":"Enhanced sequential directional importance sampling for structural reliability analysis","authors":"Kai Cheng,&nbsp;Iason Papaioannou,&nbsp;Daniel Straub","doi":"10.1016/j.strusafe.2025.102574","DOIUrl":"10.1016/j.strusafe.2025.102574","url":null,"abstract":"<div><div>Sequential directional importance sampling (SDIS) Kai Cheng et al. (2023) is an efficient adaptive simulation method for estimating failure probabilities. It expresses the failure probability as the product of a group of integrals that are easy to estimate, wherein the first one is estimated with Monte Carlo simulation (MCS), and all the subsequent ones are estimated with directional importance sampling. In this work, we propose an enhanced SDIS method for structural reliability analysis. We discuss the efficiency of MCS for estimating the first integral in standard SDIS and propose using Subset Simulation as an alternative method. Additionally, we propose a Kriging-based active learning algorithm tailored to identify multiple roots in certain important directions within a specificed search interval. The performance of the enhanced SDIS is demonstrated through various complex benchmark problems. The results show that the enhanced SDIS is a versatile reliability analysis method that can efficiently and robustly solve challenging reliability problems.</div></div>","PeriodicalId":21978,"journal":{"name":"Structural Safety","volume":"114 ","pages":"Article 102574"},"PeriodicalIF":5.7,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143349035","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}

{"title":"Sensitivity of ship hull reliability considering geometric imperfections and residual stresses","authors":"Aws Idris,&nbsp;Mohamed Soliman","doi":"10.1016/j.strusafe.2025.102575","DOIUrl":"10.1016/j.strusafe.2025.102575","url":null,"abstract":"<div><div>Initial geometric imperfections and welding-induced residual stresses are inevitable consequences of ship fabrication and manufacturing processes. This paper quantifies the effect of these imperfections, as well as other input parameters, on the reliability of ship hull girders. The paper introduces a comprehensive variance-based sensitivity analysis approach, assisted by artificial neural networks, to characterize the key input parameters influencing the failure probability under different operational conditions. A total of 16 input parameters related to load and capacity quantification are considered in the simulation. The ultimate strength of the hull girder is quantified using a high-fidelity nonlinear finite element model that accounts for initial geometric imperfections and residual stresses. The vertical bending moments acting on the ship during its service life are quantified probabilistically. The results indicate that although it is essential to account for initial geometric imperfections to properly establish the ultimate hull capacity, the uncertainty in their magnitude has low effect on the reliability of the investigated hull. Accordingly, their magnitude can be considered deterministically in the probabilistic simulations. It was also found that the influence of various input parameters on the variability of the ship reliability depends on the considered operational condition.</div></div>","PeriodicalId":21978,"journal":{"name":"Structural Safety","volume":"114 ","pages":"Article 102575"},"PeriodicalIF":5.7,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143418857","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":"Time-dependent reliability analysis for non-differentiable limit state functions due to discrete load processes","authors":"Hao-Peng Qiao ,&nbsp;Zhao-Hui Lu ,&nbsp;Chun-Qing Li ,&nbsp;Chao-Huang Cai ,&nbsp;Cao Wang","doi":"10.1016/j.strusafe.2025.102576","DOIUrl":"10.1016/j.strusafe.2025.102576","url":null,"abstract":"<div><div>In time-dependent structural reliability analysis it is not always the case that the limit state functions are continuous and differentiable. A commonly encountered case is the structures subjected to instantaneous loads with high intensity, such as seismic actions. This paper intends to propose an efficient method for time-dependent reliability analysis of structures with non-differentiable limit state functions. A new formulation is proposed for outcrossing rate based on the minimum value of limit state functions at the time of outcrossing. The developed method also considers the degradation of structural resistance. It is found in the paper that the instantaneous discrete load with high intensity can induce non-differentiable limit state functions and that the probabilistic information of such instantaneous loads significantly affects the time-dependent failure probabilities. The paper concludes that the proposed method can predict the time-dependent failure probability of structures for non-differentiable limit state functions accurately and efficiently. The proposed method contributes to the body of knowledge of time-dependent reliability with wider practical applications.</div></div>","PeriodicalId":21978,"journal":{"name":"Structural Safety","volume":"114 ","pages":"Article 102576"},"PeriodicalIF":5.7,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143349034","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":"Sequential and adaptive probabilistic integration for structural reliability analysis","authors":"Masaru Kitahara ,&nbsp;Pengfei Wei","doi":"10.1016/j.strusafe.2025.102577","DOIUrl":"10.1016/j.strusafe.2025.102577","url":null,"abstract":"<div><div>We propose the application of sequential and adaptive probabilistic integration (SAPI) to the estimation of the probability of failure in structural reliability. SAPI was originally developed to explore the posterior distribution and estimate its normalising constant in Bayesian model updating. The principle is to perform probabilistic integration on a sequence of distributions, moving from the prior to the posterior, to learn the normalising constant of each distribution. In structural reliability, SAPI can be used to sample an approximation of the optimal importance sampling (IS) density, and we present a particular choice of the intermediate distributions. The derived SAPI estimator is thus an IS estimator of the thought probability. The numerical uncertainty is propagated using random process sampling, and the induced posterior statistics are used to design a Bayesian active learning strategy. Four numerical examples demonstrate that SAPI outperforms other state-of-the-art active learning reliability methods using sequential Monte Carlo samplers.</div></div>","PeriodicalId":21978,"journal":{"name":"Structural Safety","volume":"114 ","pages":"Article 102577"},"PeriodicalIF":5.7,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143387441","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}