Structural Safety最新文献_第4页

Stochastic modelling of non-stationary and dependent weather extremes for structural reliability analysis in the changing climate 气候变化中结构可靠性分析的非平稳和相关极端天气随机建模

IF 5.7 1区工程技术

Structural Safety Pub Date : 2024-12-24 DOI: 10.1016/j.strusafe.2024.102569

Mahesh D. Pandey , Sophie Mercier

{"title":"Stochastic modelling of non-stationary and dependent weather extremes for structural reliability analysis in the changing climate","authors":"Mahesh D. Pandey , 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}

引用次数: 0

Hierarchical Bayesian models with subdomain clustering for parameter estimation of discrete Bayesian network 离散贝叶斯网络参数估计的子域聚类层次贝叶斯模型

IF 5.7 1区工程技术

Structural Safety Pub Date : 2024-12-20 DOI: 10.1016/j.strusafe.2024.102570

Changuk Mun , Jong-Wha Bai , Junho Song

{"title":"Hierarchical Bayesian models with subdomain clustering for parameter estimation of discrete Bayesian network","authors":"Changuk Mun , Jong-Wha Bai , 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}

引用次数: 0

Evaluating the importance of spatial variability of corrosion initiation parameters for the risk-based maintenance of reinforced concrete marine structures 海洋钢筋混凝土结构风险维修中起蚀参数空间变异性的重要性评价

IF 5.7 1区工程技术

Structural Safety Pub Date : 2024-12-12 DOI: 10.1016/j.strusafe.2024.102568

Romain Clerc , Charbel-Pierre El-Soueidy , Franck Schoefs

{"title":"Evaluating the importance of spatial variability of corrosion initiation parameters for the risk-based maintenance of reinforced concrete marine structures","authors":"Romain Clerc , Charbel-Pierre El-Soueidy , 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}

引用次数: 0

Assessment of risk reduction strategies for terrorist attacks on structures 评估减少建筑物恐怖袭击风险的策略

IF 5.7 1区工程技术

Structural Safety Pub Date : 2024-12-09 DOI: 10.1016/j.strusafe.2023.102381

Mark G. Stewart , Sebastian Thöns , André T. Beck

引用次数: 0

A probability-based risk assessment of secondary fragments ejected from the reinforced concrete wall under close-in explosions 近距离爆炸下钢筋混凝土墙体抛射二次破片的概率风险评估

IF 5.7 1区工程技术

Structural Safety Pub Date : 2024-12-09 DOI: 10.1016/j.strusafe.2024.102565

Zitong Wang , Qilin Li , Wensu Chen , Hong Hao , Ling Li

{"title":"A probability-based risk assessment of secondary fragments ejected from the reinforced concrete wall under close-in explosions","authors":"Zitong Wang , Qilin Li , Wensu Chen , Hong Hao , 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}

引用次数: 0

An efficient quantum computing based structural reliability analysis method using quantum amplitude estimation 基于量子振幅估计的结构可靠性分析方法

IF 5.7 1区工程技术

Structural Safety Pub Date : 2024-12-07 DOI: 10.1016/j.strusafe.2024.102555

Jingran He

{"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}

引用次数: 0

Probabilistic prediction and early warning for bridge bearing displacement using sparse variational Gaussian process regression 基于稀疏变分高斯过程回归的桥梁支座位移概率预测与预警

IF 5.7 1区工程技术

Structural Safety Pub Date : 2024-12-06 DOI: 10.1016/j.strusafe.2024.102564

Yafei Ma, Bachao Zhang, Ke Huang, Lei Wang

{"title":"Probabilistic prediction and early warning for bridge bearing displacement using sparse variational Gaussian process regression","authors":"Yafei Ma, Bachao Zhang, Ke Huang, 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}

引用次数: 0

Multi-point Bayesian active learning reliability analysis 多点贝叶斯主动学习信度分析

IF 5.7 1区工程技术

Structural Safety Pub Date : 2024-12-06 DOI: 10.1016/j.strusafe.2024.102557

Tong Zhou , Xujia Zhu , Tong Guo , You Dong , Michael Beer

{"title":"Multi-point Bayesian active learning reliability analysis","authors":"Tong Zhou , Xujia Zhu , Tong Guo , You Dong , Michael Beer","doi":"10.1016/j.strusafe.2024.102557","DOIUrl":"10.1016/j.strusafe.2024.102557","url":null,"abstract":"<div><div>This manuscript presents a novel Bayesian active learning reliability method integrating both Bayesian failure probability estimation and Bayesian decision-theoretic multi-point enrichment process. First, an epistemic uncertainty measure called integrated margin probability (IMP) is proposed as an upper bound for the mean absolute deviation of failure probability estimated by Kriging. Then, adhering to the Bayesian decision theory, a look-ahead learning function called multi-point stepwise margin reduction (MSMR) is defined to quantify the possible reduction of IMP brought by adding a batch of new samples in expectation. The cost-effective implementation of MSMR-based multi-point enrichment process is conducted by three key workarounds: (a) Thanks to analytical tractability of the inner integral, the MSMR reduces to a single integral. (b) The remaining single integral in the MSMR is numerically computed with the rational truncation of the quadrature set. (c) A heuristic treatment of maximizing the MSMR is devised to fastly select a batch of best next points per iteration, where the prescribed scheme or adaptive scheme is used to specify the batch size. The proposed method is tested on two benchmark examples and two dynamic reliability problems. The results indicate that the adaptive scheme in the MSMR gains a good balance between the computing resource consumption and the overall computational time. Then, the MSMR fairly outperforms those existing leaning functions and parallelization strategies in terms of the accuracy of failure probability estimate, the number of iterations, as well as the number of performance function evaluations, especially in complex dynamic reliability problems.</div></div>","PeriodicalId":21978,"journal":{"name":"Structural Safety","volume":"114 ","pages":"Article 102557"},"PeriodicalIF":5.7,"publicationDate":"2024-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143151098","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}

引用次数: 0

Disaster risk-informed optimization using buffered failure probability for regional-scale building retrofit strategy 区域尺度建筑改造策略中基于缓冲失效概率的灾害风险优化

IF 5.7 1区工程技术

Structural Safety Pub Date : 2024-12-05 DOI: 10.1016/j.strusafe.2024.102556

Uichan Seok , Ji-Eun Byun , Junho Song

{"title":"Disaster risk-informed optimization using buffered failure probability for regional-scale building retrofit strategy","authors":"Uichan Seok , Ji-Eun Byun , Junho Song","doi":"10.1016/j.strusafe.2024.102556","DOIUrl":"10.1016/j.strusafe.2024.102556","url":null,"abstract":"<div><div>Regional retrofit planning of buildings is critical to address the increasing threat of natural disasters exacerbated by urban growth and climate change. To identify an optimal plan, this paper introduces a novel optimization framework. By integrating performance-based engineering (PBE) and reliability-based optimization (RBO), we propose buffered optimization and reliability method based mixed integer linear programming (BORM-MILP). The proposed formulation can identify optimal solutions using general optimization solvers, while handling a large number of PBE samples and buildings. Furthermore, the formulation introduces a modified active-set strategy tailored to regional-scale building retrofit optimization problems, further reducing computational memory. The proposed optimization framework is validated by a benchmark example of Seaside, Oregon. The optimization results are presented along in a map, offering visual support for decision-making processes. The application results are further investigated to analyze computational efficiency of the proposed active-set strategy, study convergence to the normal distribution, and identify a dominant factor for the building retrofit selection.</div></div>","PeriodicalId":21978,"journal":{"name":"Structural Safety","volume":"114 ","pages":"Article 102556"},"PeriodicalIF":5.7,"publicationDate":"2024-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143151099","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}

引用次数: 0

Reliability analysis for data-driven noisy models using active learning 基于主动学习的数据驱动噪声模型可靠性分析

IF 5.7 1区工程技术

Structural Safety Pub Date : 2024-11-26 DOI: 10.1016/j.strusafe.2024.102543

Anderson V. Pires, Maliki Moustapha, Stefano Marelli, Bruno Sudret

{"title":"Reliability analysis for data-driven noisy models using active learning","authors":"Anderson V. Pires, Maliki Moustapha, Stefano Marelli, Bruno Sudret","doi":"10.1016/j.strusafe.2024.102543","DOIUrl":"10.1016/j.strusafe.2024.102543","url":null,"abstract":"<div><div>Reliability analysis aims at estimating the failure probability of an engineering system. It often requires multiple runs of a limit-state function, which usually relies on computationally intensive simulations. Traditionally, these simulations have been considered deterministic, <em>i.e.</em> running them multiple times for a given set of input parameters always produces the same output. However, this assumption does not always hold, as many studies in the literature report non-deterministic computational simulations (also known as noisy models). In such cases, running the simulations multiple times with the same input will result in different outputs. Similarly, data-driven models that rely on real-world data may also be affected by noise. This characteristic poses a challenge when performing reliability analysis, as many classical methods, such as FORM and SORM, are tailored to deterministic models. To bridge this gap, this paper provides a novel methodology to perform reliability analysis on models contaminated by noise. In such cases, noise introduces latent uncertainty into the reliability estimator, leading to an incorrect estimation of the real underlying reliability index, even when using Monte Carlo simulation. To overcome this challenge, we propose the use of denoising regression-based surrogate models within an active learning reliability analysis framework. Specifically, we combine Gaussian process regression with a noise-aware learning function to efficiently estimate the probability of failure of the underlying noise-free model. We showcase the effectiveness of this methodology on standard benchmark functions and a finite element model of a realistic structural frame.</div></div>","PeriodicalId":21978,"journal":{"name":"Structural Safety","volume":"112 ","pages":"Article 102543"},"PeriodicalIF":5.7,"publicationDate":"2024-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142748044","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}

引用次数: 0