Structural Safety最新文献

{"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":"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}

{"title":"Stochastic modelling of non-stationary and dependent weather extremes for structural reliability analysis in the changing climate","authors":"Mahesh D. Pandey ,&nbsp;Sophie Mercier","doi":"10.1016/j.strusafe.2024.102569","DOIUrl":"10.1016/j.strusafe.2024.102569","url":null,"abstract":"<div><div>In recent times, the safety of infrastructure systems has been challenged by the increasing severity of extreme weather events caused by the effects of climate change . This trend is expected to continue, as shown by the simulations of future climate conditions under high-emission scenarios. The paper presents a general stochastic process, known as the Linear Extension of the Yule Process (LEYP), to model the non-stationary frequency and intensity of extremes. The LEYP model overcomes a major limitation of the classical Poisson process by including the statistical dependence among extreme events.</div><div>The paper presents a probabilistic framework for non-stationary structural reliability analysis, which includes new results for the return period, waiting time for the next event, correlation coefficient, and the distribution of the maximum load in a given time interval. The examples provided in the paper demonstrate that even a modest degree of dependence can significantly reduce the interval between events and increase the probability of failure with time. Furthermore, the paper illustrates the non-stationary modelling of future precipitation data, as simulated by the Canadian Earth Systems Model (CanESM5). The results of this study are expected to be useful for revising current ”stationary” design codes and ensuring structural safety in the changing climate.</div></div>","PeriodicalId":21978,"journal":{"name":"Structural Safety","volume":"114 ","pages":"Article 102569"},"PeriodicalIF":5.7,"publicationDate":"2024-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143151077","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":"Hierarchical Bayesian models with subdomain clustering for parameter estimation of discrete Bayesian network","authors":"Changuk Mun ,&nbsp;Jong-Wha Bai ,&nbsp;Junho Song","doi":"10.1016/j.strusafe.2024.102570","DOIUrl":"10.1016/j.strusafe.2024.102570","url":null,"abstract":"<div><div>Bayesian network (BN) is a powerful tool for the probabilistic modeling and inference of multiple random variables. While conditional probability tables (CPTs) of a discrete BN provide a unified representation facilitating closed-form inference by efficient algorithms, they pose challenges in parameter estimation, especially due to data sparsity resulting from the discretization of continuous parent variables. To address the challenges, this paper presents a novel BN modeling approach, which is the first attempt to apply hierarchical Bayesian modeling to quantify the CPT of a child variable with discretized multiple parent variables. In addition, given that discretization results in many subdomains showing strong correlation, the concept of subdomain clustering is introduced in both supervised and unsupervised learning schemes. The proposed procedure is demonstrated by its application to the BN model describing structural responses under a sequence of main and aftershocks. In the model, the structural dynamic response of interest is modeled by a CPT in discretized domains of six-dimensional ground motion features. Hierarchical Bayesian normal models are developed to quantify the conditional probability parameters in the subdomains, which are classified using the information of peak ground acceleration. The proposed approach facilitates robust parameter estimation of the CPT, especially in the subdomains with a small number of data points. This is thoroughly validated by comparing the inference results of the CPT by the proposed method with those by an alternative approach that does not consider the correlation between subdomains. Furthermore, the validation is performed on different subsets of the parent variables with various unsupervised learning schemes to demonstrate the general effectiveness of the subdomain clustering for the hierarchical Bayesian approach.</div></div>","PeriodicalId":21978,"journal":{"name":"Structural Safety","volume":"114 ","pages":"Article 102570"},"PeriodicalIF":5.7,"publicationDate":"2024-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143151074","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":"Evaluating the importance of spatial variability of corrosion initiation parameters for the risk-based maintenance of reinforced concrete marine structures","authors":"Romain Clerc ,&nbsp;Charbel-Pierre El-Soueidy ,&nbsp;Franck Schoefs","doi":"10.1016/j.strusafe.2024.102568","DOIUrl":"10.1016/j.strusafe.2024.102568","url":null,"abstract":"<div><div>In Risk-Based Maintenance (RBM) of Reinforced Concrete (RC) marine structures, modeling the spatial variability of corrosion initiation parameters is crucial for ensuring durability. However, the necessity for an accurate characterization of this spatial variability has not yet been fully investigated, despite the potential increase in measurement costs. This study addresses this gap by focusing specifically on the failure probability at the Durability Limit-State (DLS) due to chloride-induced corrosion initiation. A robust Sensitivity Analysis (SA) methodology, combined with global quantitative All-At-Time (AAT) methods, is applied to a case study of a wharf beam. The objective is to identify the spatially variable degradation parameters whose fluctuation scales have at least the same impact on failure probability as other statistical hyperparameters (HP). The results highlight that key parameters – namely the correlation coefficient of diffusion parameters and the mean and standard deviation of total chloride apparent diffusivity – significantly impact failure probabilities, ranking as the first, second, and third most sensitive HP, respectively. Among fluctuation scales, only that of chloride diffusivity can affect failure probability, while others rank no higher than fifth in sensitivity. The findings demonstrate that a broad, pre-defined range for fluctuation scales (4%–20% of element dimensions) is sufficient for RBM, minimizing the need for costly updates over time. The study also reveals that incorporating aging and diffusion parameter correlations significantly changes both failure time and failure probabilities, increasing them up to 33% and 40 percentage points, respectively, in some scenarios.</div></div>","PeriodicalId":21978,"journal":{"name":"Structural Safety","volume":"114 ","pages":"Article 102568"},"PeriodicalIF":5.7,"publicationDate":"2024-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143151073","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}