Structural Safety最新文献

{"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}

{"title":"Multi-output stochastic emulation with applications to seismic response correlation estimation","authors":"Sang-ri Yi ,&nbsp;Alexandros A. Taflanidis","doi":"10.1016/j.strusafe.2025.102578","DOIUrl":"10.1016/j.strusafe.2025.102578","url":null,"abstract":"<div><div>Stochastic emulation techniques represent a specialized surrogate modeling branch that is appropriate for applications for which the relationship between input and output is stochastic in nature. Their objective is to address the stochastic uncertainty sources by directly predicting the output distribution for a given input. An example of such application, and the focus of this contribution, is the estimation of structural response (engineering demand parameter) distribution in seismic risk assessment. In this case, the stochastic uncertainty originates from the aleatoric variability in the seismic hazard description. Note that this is a different uncertainty-source than the potential parametric uncertainty associated with structural characteristics or explanatory variables for the seismic hazard (for example, intensity measures), that are treated as the parametric input in surrogate modeling context. The key challenge in stochastic emulation pertains to addressing heteroscedasticity in the output variability. Relevant approaches to-date for addressing this challenge have focused on scalar outputs. In contrast, this paper focuses on the multi-output stochastic emulation problem and presents a methodology for predicting the output correlation matrix, while fully addressing heteroscedastic characteristics. This is achieved by introducing a Gaussian Process (GP) regression model for approximating the components of the correlation matrix, and coupling this approximation with a correction step to guarantee positive definite properties for the resultant predictions. For obtaining the observation data to inform the GP calibration, different approaches are examined, relying-or-not on the existence of replicated samples for the response output. Such samples require that, for a portion of the training points, simulations are repeated for the same inputs and different descriptions of the stochastic uncertainty. This information can be readily used to obtain observation for the response statistics (correlation or covariance in this instance) to inform the GP development. An alternative approach is to use as observations noisy covariance samples based on the sample deviations from a primitive mean approximation. These different observation variants lead to different GP variants that are compared within a comprehensive case study. A computational framework for integrating the correlation matrix approximation within the stochastic emulation for the marginal distribution approximation of each output component is also discussed, to provide the joint response distribution approximation.</div></div>","PeriodicalId":21978,"journal":{"name":"Structural Safety","volume":"115 ","pages":"Article 102578"},"PeriodicalIF":5.7,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143838094","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":"Serviceability limit state target reliability for concrete structures","authors":"Andrew Way ,&nbsp;Frederik Bakker ,&nbsp;Dirk Proske ,&nbsp;Celeste Viljoen","doi":"10.1016/j.strusafe.2024.102572","DOIUrl":"10.1016/j.strusafe.2024.102572","url":null,"abstract":"<div><div>The balance between safety and economy, referred to as target reliability, forms the basis of modern structural design. Target reliability is determined by economic minimisation, either directly through generic cost optimisation or by back calibration to existing practice. However, the currently codified annual target reliability indices for serviceability limit state (SLS), differ by as much as <span><math><mrow><mi>Δ</mi><mi>β</mi><mo>=</mo><mn>1.6</mn></mrow></math></span> between those from generic cost optimisation and back calibration. Various assumptions are made in the generic cost optimisation which may not be appropriate to determine SLS target reliability. Target reliability from back calibration is likely to be closer to actual SLS failure rates, however, no literature exists which details the process or rationale by which the back calibration was performed. It is therefore uncertain if either of these methods produce cost optimal SLS target reliability. This research aims to evaluate currently codified SLS target reliability for cost optimality. SLS failure costs from existing research and engineering practice are used with an amended cost optimisation procedure which overcomes the deficiencies identified in the generic formulation to specifically determine SLS target reliability. The amended cost optimisation also considers parameter variation and decision parameter form for typical SLS cases. Results indicate that overall, the target reliability indices for annual irreversible SLS from back calibration to existing practice (<span><math><mrow><mi>β</mi><mo>=</mo><mn>2.9</mn></mrow></math></span>) represents the range of considered SLS cases (<span><math><mrow><mn>2.5</mn><mo>≤</mo><mi>β</mi><mo>≤</mo><mn>3.3</mn></mrow></math></span>) well, whereas those from generic cost optimisation are notably lower (<span><math><mrow><mn>1.3</mn><mo>≤</mo><mi>β</mi><mo>≤</mo><mn>2.3</mn></mrow></math></span>). In some cases, target reliability varied sufficiently from <span><math><mrow><mn>2.9</mn></mrow></math></span> to warrant adjustments being made for better cost optimality.</div></div>","PeriodicalId":21978,"journal":{"name":"Structural Safety","volume":"114 ","pages":"Article 102572"},"PeriodicalIF":5.7,"publicationDate":"2024-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143151076","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":"Wind fragility modeling of transmission tower-line system based on threat-dependent structural robustness index","authors":"Xiao Zhu ,&nbsp;Ge Ou","doi":"10.1016/j.strusafe.2024.102571","DOIUrl":"10.1016/j.strusafe.2024.102571","url":null,"abstract":"<div><div>Transmission tower fragility models play a crucial role in analyzing the resilience of power grids subjected to various environmental loads such as wind, earthquake, and ice. The limit state of the dominant transmission tower-line fragility model relies on the threshold of tower tip displacement. This paper proposes a transmission tower fragility model based on a threat-dependent structural robustness measure. The proposed methodology evaluates and quantifies the robustness of the transmission tower in the tower-line system under gravity after removing the local failed elements identified from the dynamic wind analysis. Different limit states of the transmission tower are identified by the distribution of the robustness index and the deformed tower configuration. By comparing the developed fragility curves of the tower for different limit states with the tower tip displacement-based and the element failure-based methodologies, the results indicate that the tip displacement-based fragility model overestimates the failure probability of the tower. The proposed methodology, on the other hand, leads to a probabilistic assessment of transmission tower failure subjected to wind loading with a more distinctive physical meaning.</div></div>","PeriodicalId":21978,"journal":{"name":"Structural Safety","volume":"114 ","pages":"Article 102571"},"PeriodicalIF":5.7,"publicationDate":"2024-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143151075","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}