Structural Safety最新文献_第8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 Q1 ENGINEERING, CIVIL

Structural Safety

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

Mahesh D. Pandey , Sophie Mercier

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.

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.

近年来，由于气候变化的影响，极端天气事件日益严重，基础设施系统的安全性受到了挑战。正如在高排放情景下对未来气候条件的模拟所显示的那样，预计这一趋势将继续下去。本文提出了一个一般的随机过程，称为Yule过程的线性扩展（LEYP），来模拟极端事件的非平稳频率和强度。LEYP模型克服了经典泊松过程的一个主要限制，它包含了极端事件之间的统计依赖性。本文提出了一种非平稳结构可靠性分析的概率框架，该框架包括回归期的新结果、下一次事件的等待时间、相关系数和给定时间间隔内最大荷载的分布。文中给出的例子表明，即使是适度的依赖也能显著减小事件之间的间隔，并随时间增加故障的概率。此外，本文还举例说明了由加拿大地球系统模式（CanESM5）模拟的未来降水数据的非平稳模式。本研究的结果有望对修订现行的“固定”设计规范和确保结构在不断变化的气候下的安全有用。

{"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 Q1 ENGINEERING, CIVIL

Structural Safety

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

Changuk Mun , Jong-Wha Bai , Junho Song

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.

贝叶斯网络是多随机变量概率建模和推理的有力工具。虽然离散BN的条件概率表（cpt）提供了统一的表示，便于通过有效的算法进行封闭形式的推理，但它们在参数估计方面提出了挑战，特别是由于连续父变量的离散化导致的数据稀疏性。为了解决这些挑战，本文提出了一种新的BN建模方法，这是首次尝试应用分层贝叶斯建模来量化具有离散多父变量的子变量的CPT。此外，考虑到许多子域的离散化结果显示出很强的相关性，在有监督和无监督学习方案中都引入了子域聚类的概念。通过将所提出的程序应用于描述主余震和余震序列下结构反应的BN模型，证明了所提出的程序。在该模型中，目标结构的动力响应是在六维地震动特征的离散域中用CPT模型来模拟的。建立了层次贝叶斯正态模型来量化子域中的条件概率参数，并利用峰值地面加速度信息对子域进行分类。该方法有利于CPT参数的鲁棒估计，特别是在数据点较少的子域中。通过将所提出方法的CPT推理结果与不考虑子域之间相关性的替代方法的推理结果进行比较，彻底验证了这一点。此外，使用各种无监督学习方案对父变量的不同子集进行验证，以证明分层贝叶斯方法的子域聚类的一般有效性。

{"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 Q1 ENGINEERING, CIVIL

Structural Safety

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

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

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.

在基于风险的钢筋混凝土（RC）海洋结构维修（RBM）中，建立起蚀参数的空间变异性模型是确保耐久性的关键。然而，尽管测量成本可能会增加，但对这种空间变异性进行准确表征的必要性尚未得到充分研究。本研究通过关注氯化物腐蚀引发的耐久性极限状态（DLS）失效概率来解决这一问题。采用鲁棒灵敏度分析（SA）方法，结合全局定量实时（AAT）方法，对码头梁进行了实例分析。目的是确定空间可变的退化参数，其波动尺度对失效概率的影响至少与其他统计超参数（HP）相同。结果表明，关键参数——扩散参数的相关系数和总氯离子表观扩散系数的平均值和标准差——对失效概率有显著影响，分别是第一、第二和第三个最敏感的HP。在波动尺度中，只有氯离子扩散系数会影响失效概率，其他波动尺度的敏感性均不高于第5位。研究结果表明，对于RBM来说，一个广泛的、预定义的波动尺度范围（元素尺寸的4%-20%）就足够了，从而最大限度地减少了随着时间的推移而进行昂贵更新的需要。研究还表明，结合老化和扩散参数相关性可以显著改变失效时间和失效概率，在某些情况下，它们分别增加了33%和40个百分点。

{"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 Q1 ENGINEERING, CIVIL

Structural Safety

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

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

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.

几十年来，对基础设施的袭击一直是恐怖主义的一个共同特征。选择的武器通常是车载简易爆炸装置（VBIED）或人载或其他类型的简易爆炸装置。一次成功的袭击在人员伤亡、物理损失以及其他直接和间接成本（包括社会成本）方面的后果可能是灾难性的。保护措施和其他减少风险措施可以改善威胁的可能性、脆弱性或后果。我们需要一种理性的方法来决定如何最好地保护基础设施，哪些不应该保护。因此，本文描述了一种保护基础设施免受爆炸攻击的概率风险评估方法。这包括对恐怖主义威胁和危害的描述，脆弱性评估，包括渐进或不成比例的崩溃，以及后果评估。决策分析的示例考虑了桥梁和建筑物渐进倒塌的最佳风险降低和设计策略。

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 Q1 ENGINEERING, CIVIL

Structural Safety

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

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

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.

简易爆炸装置（IED）由于其制造和部署简单，构成了重大威胁。对于钢筋混凝土墙体，近距离简易爆炸装置爆炸会造成严重的结构破坏，产生的高速二次破片会危及周边地区的人员和设施。现行有关安全距离的安全标准不适用于近距离简易爆炸装置爆炸。提出了一种基于概率的简易爆炸装置爆炸二次破片弹射风险评估方法。该方法利用了作者之前研究中开发的基于机器学习的碎片图网络（FGN）的数据，比传统方法更有效地模拟次要碎片。通过对碎片分布数据的分析，运用logistic回归分析，确定了不同安全概率阈值对应的避免人员伤亡的安全距离。因此，提出的二次破片系统风险评估方法能够精确确定安全距离，以减轻近距离简易爆炸装置爆炸场景中的潜在伤害。开发了经验公式，用于快速估计不同爆炸场景和墙壁配置所需的安全距离。

{"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 Q1 ENGINEERING, CIVIL

Structural Safety

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

Jingran He

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

O (N)

to

O (\sqrt{N})

for the failure probability being

O (1 / N)

. 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.

高效的结构可靠度分析方法一直是土木工程领域关注的问题。尽管过去几年在提高经典计算机的计算效率方面已经做出了出色的工作，但量子计算机的发展已经显示出进一步扩展计算效率边界的新潜力。提出了一种基于量子计算的结构可靠性评估方法。与经典计算机中的蒙特卡罗方法相比，量子振幅估计方法的主要优点是在故障概率为0 /N时，计算量从ON减少到ON。本研究采用量子计算的方法，利用量子振幅估计来表述可靠性问题。最后给出了一个简单的数值应用实例，并与蒙特卡罗方法进行了比较。

{"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 Q1 ENGINEERING, CIVIL

Structural Safety

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

Yafei Ma, Bachao Zhang, Ke Huang, Lei Wang

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.

研究温度变化与桥梁支座位移之间的关系对于保证结构的完整性和安全性至关重要。然而，目前的温度-位移回归（TDR）模型未能考虑监测数据的固有不确定性和模型误差。本文提出了一种基于稀疏变分高斯过程回归（SVGPR）模型的桥梁支座位移概率预测预警框架。分析了不同时间尺度下温度与轴承位移的时变关系。基于完全独立训练条件（FITC）构建SVGP-TDR模型，并结合变分学习和梯度下降法对诱导点和超参数进行同步优化。提出了一种基于模型估计误差和Shewhart控制图理论的轴承性能预警方法，并给出了实现步骤。利用某既有悬索桥的长期监测数据验证了该方法的有效性。结果表明，SVGP-TDR模型能较好地预测温度引起轴承位移的概率分布。不仅可以考虑监测数据的不确定性，还可以量化模型误差和预测不确定性。所提出的预警方法在评估桥梁支座的使用性能方面取得了满意的效果。

{"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 Q1 ENGINEERING, CIVIL

Structural Safety

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

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

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.

本文提出了一种结合贝叶斯故障概率估计和贝叶斯决策理论多点富集过程的贝叶斯主动学习可靠性方法。首先，提出了一种称为积分边际概率（IMP）的认知不确定性测度作为Kriging估计的失效概率平均绝对偏差的上界。然后，根据贝叶斯决策理论，定义了一种称为多点逐步边际缩减（multi-point stepwise margin reduction， MSMR）的前瞻学习函数，量化在期望中加入一批新样本可能带来的IMP缩减量。基于MSMR的多点富集过程的经济有效实施是通过三个关键的解决方案进行的：(a)由于内部积分的分析可追溯性，MSMR减少到单个积分。(b)用正交集的有理截断对MSMR中剩余的单积分进行数值计算。(c)设计了最大化MSMR的启发式处理，以便每次迭代快速选择一批最佳下一个点，其中使用规定的方案或自适应方案来指定批大小。通过两个基准算例和两个动态可靠性问题对该方法进行了验证。结果表明，该自适应方案在计算资源消耗和总体计算时间之间取得了良好的平衡。在故障概率估计精度、迭代次数、性能函数评估次数等方面，MSMR算法明显优于现有的学习函数和并行化策略，特别是在复杂的动态可靠性问题中。

{"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 Q1 ENGINEERING, CIVIL

Structural Safety

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

Uichan Seok , Ji-Eun Byun , Junho Song

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.

区域建筑改造规划对于应对日益严重的自然灾害威胁至关重要，这些自然灾害因城市发展和气候变化而加剧。为了确定最优方案，本文引入了一种新的优化框架。将基于性能的工程（PBE）和基于可靠性的优化（RBO）相结合，提出了基于混合整数线性规划（BORM-MILP）的缓冲优化和可靠性方法。在处理大量PBE样本和建筑物时，所提出的公式可以使用一般优化求解器识别最优解。此外，该公式引入了针对区域尺度建筑改造优化问题的改进活动集策略，进一步减少了计算内存。本文提出的优化框架通过俄勒冈州Seaside的基准实例进行了验证。优化结果呈现在地图上，为决策过程提供视觉支持。应用结果进一步分析了所提主动集策略的计算效率，研究了其向正态分布的收敛性，并确定了建筑改造选择的主导因素。

{"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 Q1 ENGINEERING, CIVIL

Structural Safety

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

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

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, i.e. 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.

可靠性分析的目的是估计工程系统的失效概率。它通常需要多次运行一个极限状态函数，这通常依赖于计算密集型的模拟。传统上，这些模拟被认为是确定性的，即对于给定的一组输入参数多次运行它们总是产生相同的输出。然而，这一假设并不总是成立，因为文献中的许多研究报告了非确定性计算模拟（也称为噪声模型）。在这种情况下，使用相同的输入多次运行模拟将导致不同的输出。同样，依赖于真实世界数据的数据驱动模型也可能受到噪声的影响。这一特性在执行可靠性分析时提出了挑战，因为许多经典方法（如FORM和SORM）都是针对确定性模型量身定制的。为了弥补这一差距，本文提供了一种新的方法来对受噪声污染的模型进行可靠性分析。在这种情况下，噪声将潜在的不确定性引入可靠性估计器，导致对真实潜在可靠性指标的不正确估计，即使使用蒙特卡罗模拟也是如此。为了克服这一挑战，我们建议在主动学习可靠性分析框架中使用基于去噪回归的代理模型。具体来说，我们将高斯过程回归与噪声感知学习函数相结合，以有效地估计底层无噪声模型的失效概率。我们展示了这种方法在标准基准函数和现实结构框架的有限元模型上的有效性。

{"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