Structural Safety最新文献

{"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}

{"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}

{"title":"Assessment of risk reduction strategies for terrorist attacks on structures","authors":"Mark G. Stewart ,&nbsp;Sebastian Thöns ,&nbsp;André T. Beck","doi":"10.1016/j.strusafe.2023.102381","DOIUrl":"10.1016/j.strusafe.2023.102381","url":null,"abstract":"<div><div>Attacks on infrastructure have been a common feature of terrorism over many decades. The weapon of choice is often a Vehicle-Borne Improvised Explosive Device (VBIED) or a person-borne or other type of IED. The consequences of a successful attack in terms of casualties, physical damage, and other direct and indirect costs including societal costs can be catastrophic. Protectives and other risk reduction measures can ameliorate the threat likelihood, vulnerability or consequences. There is a need for a rational approach to deciding how best to protect infrastructure, and what not to protect. Hence, this paper describes a probabilistic risk assessment for the protection of infrastructure from explosive attacks. This includes a description of terrorist threats and hazards, vulnerability assessment including progressive or disproportionate collapse, and consequences assessment. Illustrative examples of the decision analysis consider the optimal risk reduction and design strategies for bridges and the progressive collapse of buildings.</div></div>","PeriodicalId":21978,"journal":{"name":"Structural Safety","volume":"113 ","pages":"Article 102381"},"PeriodicalIF":5.7,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143138979","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 probability-based risk assessment of secondary fragments ejected from the reinforced concrete wall under close-in explosions","authors":"Zitong Wang ,&nbsp;Qilin Li ,&nbsp;Wensu Chen ,&nbsp;Hong Hao ,&nbsp;Ling Li","doi":"10.1016/j.strusafe.2024.102565","DOIUrl":"10.1016/j.strusafe.2024.102565","url":null,"abstract":"<div><div>Improvised explosive device (IED) poses a significant threat due to its simplicity of fabrication and deployment. For reinforced concrete (RC) walls, the close-in IED explosions could cause severe structural damage, and the resultant high-velocity secondary fragments endanger people and facilities in the surrounding area. Existing safety standards regarding safety distance are not applicable for close-in IED explosions. This study proposes a probability-based risk assessment method to estimate human casualty risks from secondary fragment ejection caused by close-in IED explosions. This method leverages data from a machine-learning-based Fragment Graph Network (FGN) developed in the authors’ previous research, simulating secondary fragments more efficiently than traditional methods. By analysing fragment distribution data and applying logistic regression analysis, safety distances to avoid human casualties corresponding to various safety probability thresholds are determined. Consequently, the proposed systematic risk assessment method for secondary fragments enables precise determination of safety distances to mitigate potential injuries in close-in IED blast scenarios. Empirical formulae are developed for fast estimation of safety distances required for different blast scenarios and wall configurations.</div></div>","PeriodicalId":21978,"journal":{"name":"Structural Safety","volume":"114 ","pages":"Article 102565"},"PeriodicalIF":5.7,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143151100","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":"An efficient quantum computing based structural reliability analysis method using quantum amplitude estimation","authors":"Jingran He","doi":"10.1016/j.strusafe.2024.102555","DOIUrl":"10.1016/j.strusafe.2024.102555","url":null,"abstract":"<div><div>Efficient structural reliability analysis methods are of great concern in civil engineering. Although excellent works have be dedicated in the past years for improving the computation efficiency in classical computer, the development of quantum computer has shown new potential to further extend the boundary of computation efficiency. In this paper, an efficient quantum computing based structural reliability assessment method is proposed. Compared with the Monte Carlo method in classical computer, the major advantage of quantum amplitude estimation method is that the computation cost is reduced from <span><math><mrow><mi>O</mi><mfenced><mrow><mi>N</mi></mrow></mfenced></mrow></math></span> to <span><math><mrow><mi>O</mi><mfenced><mrow><msqrt><mi>N</mi></msqrt></mrow></mfenced></mrow></math></span> for the failure probability being <span><math><mrow><mi>O</mi><mfenced><mrow><mrow><mn>1</mn><mo>/</mo><mi>N</mi></mrow></mrow></mfenced></mrow></math></span>. The present study formulated the reliability problems by means of quantum computing using quantum amplitude estimation. And a simple numerical application example is given to verify the proposed method with comparison to Monte Carlo method.</div></div>","PeriodicalId":21978,"journal":{"name":"Structural Safety","volume":"114 ","pages":"Article 102555"},"PeriodicalIF":5.7,"publicationDate":"2024-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143151078","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 prediction and early warning for bridge bearing displacement using sparse variational Gaussian process regression","authors":"Yafei Ma,&nbsp;Bachao Zhang,&nbsp;Ke Huang,&nbsp;Lei Wang","doi":"10.1016/j.strusafe.2024.102564","DOIUrl":"10.1016/j.strusafe.2024.102564","url":null,"abstract":"<div><div>Investigating the relationship between temperature variations and bridge bearing displacement is crucial for ensuring structural integrity and safety. However, the current temperature-displacement regression (TDR) model fails to account for inherent uncertainties in monitoring data and model errors. This paper proposes a probabilistic prediction and early warning framework for displacement of bridge bearing using the sparse variational Gaussian process regression (SVGPR) model. The time-varying relationships between temperature and bearing displacement at different time scales are analyzed. The SVGP-TDR model is constructed based on the fully independent training condition (FITC), and the induced points and hyperparameters are optimized simultaneously by combining variational learning and gradient descent method. An early warning method for bearing performance is proposed based on the model estimation error and Shewhart control chart theory, along with the implementation procedure provided. The effectiveness of the proposed method is verified using long-term monitoring data from an existing suspension bridge. The results show that the SVGP-TDR model can predict probability distribution of bearing displacement caused by temperature. Moreover, it can not only consider the uncertainty in the monitoring data, but also quantify the model error and prediction uncertainty. The proposed early warning method performs satisfactorily in assessing the service performance of bridge bearing.</div></div>","PeriodicalId":21978,"journal":{"name":"Structural Safety","volume":"114 ","pages":"Article 102564"},"PeriodicalIF":5.7,"publicationDate":"2024-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143150398","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}