Structural Safety最新文献_第10页

In Memoriam Alfredo Hua-Sing Ang July 4, 1930 – October 14, 2024 1930年7月4日- 2024年10月14日

IF 5.7 1区工程技术 Q1 ENGINEERING, CIVIL

Structural Safety

Pub Date : 2025-03-12 DOI: 10.1016/j.strusafe.2025.102594

Armen Der Kiureghian, Bruce R. Ellingwood

引用次数: 0

Stochastic nonlinear dynamic analysis and system reliability evaluation of RC structures involving spatial variation under stochastic ground motions 随机地震动作用下空间变化RC结构随机非线性动力分析及系统可靠性评价

IF 5.7 1区工程技术 Q1 ENGINEERING, CIVIL

Structural Safety

Pub Date : 2025-02-26 DOI: 10.1016/j.strusafe.2025.102581

Xin Chen , Jie Li

Dynamic analysis and system reliability evaluation are crucial in the design of seismic-resilient reinforced concrete (RC) structures. Uncertainties in earthquake ground motions (EGM) and the spatial variation of heterogeneous concrete must be thoroughly considered. However, implementing these analyses poses significant challenges due to the inherent complexity and high computational costs associated with stochastic nonlinear dynamic analysis and the quantification of concrete’s spatial variation through random field theory. To address these issues, we propose a novel methodology for the stochastic dynamic analysis and system reliability evaluation of RC structures involving spatial variation under stochastic ground motions. In the methodology, a two-scale random field model developed within the framework of stochastic damage mechanics is adopted to capture the coupling effects of the nonlinearity and the spatial variation of concrete. Additionally, a physical-based stochastic ground motion model is utilized to represent the randomness of EGM. Furthermore, the probability density evolution method is employed to derive probabilistic information (statistical moments, and probability density function (PDF), etc.) of dynamic responses, and the system reliability is evaluated by the physical synthesis method. A well-designed five-story RC frame structure is analyzed to demonstrate the efficacy of the proposed methodology and to investigate the influence of concrete’s spatial variation and randomness of EGM on structural responses. The results indicate that the proposed methodology can effectively obtain the probabilistic information of stochastic responses and system reliability, and the concrete’s spatial variation has a non-negligible impact on the structural responses and system reliability.

在抗震钢筋混凝土结构设计中，动力分析和系统可靠性评估是至关重要的。地震动的不确定性和非均质混凝土的空间变化必须充分考虑。然而，由于随机非线性动力分析和通过随机场理论量化混凝土空间变化的固有复杂性和高计算成本，实施这些分析带来了重大挑战。为了解决这些问题，我们提出了一种新的方法，用于随机地震动下涉及空间变化的RC结构随机动力分析和系统可靠性评估。该方法采用随机损伤力学框架下建立的双尺度随机场模型来捕捉混凝土非线性与空间变化的耦合效应。此外，利用基于物理的随机地震动模型来表示EGM的随机性。在此基础上，采用概率密度演化法推导动力响应的概率信息（统计矩、概率密度函数等），并采用物理综合方法对系统可靠性进行评估。通过对一个精心设计的五层钢筋混凝土框架结构进行分析，证明了所提出方法的有效性，并研究了混凝土的空间变化和EGM的随机性对结构响应的影响。结果表明，该方法能有效地获取随机响应和系统可靠度的概率信息，混凝土的空间变异对结构响应和系统可靠度的影响不容忽视。

{"title":"Stochastic nonlinear dynamic analysis and system reliability evaluation of RC structures involving spatial variation under stochastic ground motions","authors":"Xin Chen , Jie Li","doi":"10.1016/j.strusafe.2025.102581","DOIUrl":"10.1016/j.strusafe.2025.102581","url":null,"abstract":"<div><div>Dynamic analysis and system reliability evaluation are crucial in the design of seismic-resilient reinforced concrete (RC) structures. Uncertainties in earthquake ground motions (EGM) and the spatial variation of heterogeneous concrete must be thoroughly considered. However, implementing these analyses poses significant challenges due to the inherent complexity and high computational costs associated with stochastic nonlinear dynamic analysis and the quantification of concrete’s spatial variation through random field theory. To address these issues, we propose a novel methodology for the stochastic dynamic analysis and system reliability evaluation of RC structures involving spatial variation under stochastic ground motions. In the methodology, a two-scale random field model developed within the framework of stochastic damage mechanics is adopted to capture the coupling effects of the nonlinearity and the spatial variation of concrete. Additionally, a physical-based stochastic ground motion model is utilized to represent the randomness of EGM. Furthermore, the probability density evolution method is employed to derive probabilistic information (statistical moments, and probability density function (PDF), etc.) of dynamic responses, and the system reliability is evaluated by the physical synthesis method. A well-designed five-story RC frame structure is analyzed to demonstrate the efficacy of the proposed methodology and to investigate the influence of concrete’s spatial variation and randomness of EGM on structural responses. The results indicate that the proposed methodology can effectively obtain the probabilistic information of stochastic responses and system reliability, and the concrete’s spatial variation has a non-negligible impact on the structural responses and system reliability.</div></div>","PeriodicalId":21978,"journal":{"name":"Structural Safety","volume":"114 ","pages":"Article 102581"},"PeriodicalIF":5.7,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143535099","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

A practical framework for determining target reliability indices for the assessment of existing structures based on risk-informed decision-making 基于风险知情决策的既有结构评估目标可靠度指标确定实用框架

IF 5.7 1区工程技术 Q1 ENGINEERING, CIVIL

Structural Safety

Pub Date : 2025-02-25 DOI: 10.1016/j.strusafe.2025.102583

Jianxu Su , Junping Zhang , Colin C. Caprani , Junyong Zhou

Target reliability levels define structural safety requirements. Most current studies on target reliability indices (

β_{t}

) have focused on reliability-based design for new structures. However, existing structures face significant safety challenges due to ongoing aging and financial constraints that limit maintenance and reinforcement efforts. Therefore, determining appropriate

β_{t}

for the assessment of existing structures is crucial to balance the tradeoff between safety and economy. This study develops a practical, risk-informed framework to streamline the determination of

β_{t}

for the reliability assessment of existing structures. It involves six critical steps including context definition, structural system modeling, failure statistics analysis, risk criteria establishment, and

β_{t}

selection. The framework’s practical application is carefully demonstrated through a case study centered on the reliability assessment of existing medium- and small-span (MS) bridges in China. A database was compiled for failure statistics of MS bridges, documenting 241 bridge collapse incidents in China spanning from 1983 to 2024. The statistical analysis of lethality ratios and fatalities from these failure events is incorporated into individual risk criteria, group risk criteria, cost optimization, and the marginal lifesaving cost principle. Using these criteria, alongside a refined as low as reasonably practicable (ALARP) principle, informed decisions are made on selecting

β_{t}

for reliability differentiation. Finally, three safety levels of

β_{t}

are recommended for the bridge system as well as individual components. The proposed methodology framework, as demonstrated in the case study on MS bridges in China, can be readily applicable to the determination of

β_{t}

for various other existing civil structures.

目标可靠性水平定义了结构安全要求。目前关于目标可靠度指标（βt）的研究大多集中在基于可靠度的新结构设计上。然而，由于持续老化和财政限制，现有结构面临着重大的安全挑战，限制了维护和加固工作。因此，确定适当的βt用于现有结构的评估是至关重要的，以平衡安全与经济之间的权衡。本研究开发了一个实用的、风险知情的框架，以简化现有结构可靠性评估βt的确定。它包括上下文定义、结构系统建模、失效统计分析、风险准则建立和βt选择六个关键步骤。通过以中国现有中小跨度桥梁可靠性评估为中心的案例研究，详细论证了该框架的实际应用。建立了MS桥梁失效统计数据库，记录了1983年至2024年中国发生的241起桥梁倒塌事件。这些失效事件的致死率和死亡人数的统计分析被纳入个人风险标准、群体风险标准、成本优化和边际救生成本原则。使用这些标准，再加上精炼的尽可能低的合理可行（ALARP）原则，在选择βt进行可靠性区分时做出明智的决定。最后，对桥梁体系和各个部件推荐了三个安全等级的βt。正如在中国MS桥梁案例研究中所展示的那样，所提出的方法框架可以很容易地适用于确定其他各种现有土木结构的βt。

{"title":"A practical framework for determining target reliability indices for the assessment of existing structures based on risk-informed decision-making","authors":"Jianxu Su , Junping Zhang , Colin C. Caprani , Junyong Zhou","doi":"10.1016/j.strusafe.2025.102583","DOIUrl":"10.1016/j.strusafe.2025.102583","url":null,"abstract":"<div><div>Target reliability levels define structural safety requirements. Most current studies on target reliability indices (<span><math><mrow><msub><mi>β</mi><mi>t</mi></msub></mrow></math></span>) have focused on reliability-based design for new structures. However, existing structures face significant safety challenges due to ongoing aging and financial constraints that limit maintenance and reinforcement efforts. Therefore, determining appropriate <span><math><mrow><msub><mi>β</mi><mi>t</mi></msub></mrow></math></span> for the assessment of existing structures is crucial to balance the tradeoff between safety and economy. This study develops a practical, risk-informed framework to streamline the determination of <span><math><mrow><msub><mi>β</mi><mi>t</mi></msub></mrow></math></span> for the reliability assessment of existing structures. It involves six critical steps including context definition, structural system modeling, failure statistics analysis, risk criteria establishment, and <span><math><mrow><msub><mi>β</mi><mi>t</mi></msub></mrow></math></span> selection. The framework’s practical application is carefully demonstrated through a case study centered on the reliability assessment of existing medium- and small-span (MS) bridges in China. A database was compiled for failure statistics of MS bridges, documenting 241 bridge collapse incidents in China spanning from 1983 to 2024. The statistical analysis of lethality ratios and fatalities from these failure events is incorporated into individual risk criteria, group risk criteria, cost optimization, and the marginal lifesaving cost principle. Using these criteria, alongside a refined as low as reasonably practicable (ALARP) principle, informed decisions are made on selecting <span><math><mrow><msub><mi>β</mi><mi>t</mi></msub></mrow></math></span> for reliability differentiation. Finally, three safety levels of <span><math><mrow><msub><mi>β</mi><mi>t</mi></msub></mrow></math></span> are recommended for the bridge system as well as individual components. The proposed methodology framework, as demonstrated in the case study on MS bridges in China, can be readily applicable to the determination of <span><math><mrow><msub><mi>β</mi><mi>t</mi></msub></mrow></math></span> for various other existing civil structures.</div></div>","PeriodicalId":21978,"journal":{"name":"Structural Safety","volume":"114 ","pages":"Article 102583"},"PeriodicalIF":5.7,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143527432","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

Integrating risk perceptions in a value of information framework using cumulative prospect theory 运用累积前景理论在价值信息框架中整合风险感知

IF 5.7 1区工程技术 Q1 ENGINEERING, CIVIL

Structural Safety

Pub Date : 2025-02-22 DOI: 10.1016/j.strusafe.2025.102573

Zaid Y Mir Rangrez , Jayadipta Ghosh , Colin Caprani , Siddhartha Ghosh

Value of information (VoI) analysis provides a framework that can be used to decide on an optimal monitoring strategy, to carry out an efficient maintenance of civil infrastructure. Existing VoI frameworks adopt utility functions to characterize the risk appetite of an asset manager based on expected utility theory (EUT). However, these utility functions cannot predict the decision choices under uncertainty resulting from failure risk perceptions. Cumulative prospect theory (CPT) is a comprehensive model for characterizing an asset manager’s risk appetite and perception. CPT captures both, the preference for different action outcomes using a value function and corresponding risk perceptions exhibited by an asset manager using a probability weight function. The present study proposes a CPT-based VoI framework which integrates risk perceptions and appetite within the VoI analysis. The proposed framework is implemented to investigate the sensitivity of the resulting expected VoI and the monitoring decisions to risk perception profiles. It is observed that the VoI is sensitive to the risk perception profile of an asset manager. An in-depth analysis of the decision patterns reveal that the risk profile affects the choice of prior optimal action that in turn dictates which type of posterior actions contribute positively or negatively towards the cost savings when referenced to the cost of prior optimal action. Based on these finding, the paper recommends to calibrate an asset manager’s risk perception profile to predict the decisions that an asset manager perceives as optimal for a given failure risk, and to evaluate the expected VoI resulting from such decisions.

信息价值（VoI）分析提供了一个框架，可用于决定最佳监测策略，以便对民用基础设施进行有效维护。现有的VoI框架基于期望效用理论（EUT），采用效用函数来表征资产管理人的风险偏好。然而，这些效用函数不能预测由于失效风险感知而导致的不确定性下的决策选择。累积前景理论（CPT）是描述资产管理者风险偏好和感知的综合模型。CPT捕获两者，即使用价值函数对不同行动结果的偏好，以及资产经理使用概率权重函数所表现出的相应风险感知。本研究提出了一个基于cpt的VoI框架，该框架将风险感知和偏好整合到VoI分析中。实施提议的框架是为了调查由此产生的预期VoI和监测决策对风险感知概况的敏感性。可以观察到，VoI对资产管理公司的风险感知特征非常敏感。对决策模式的深入分析表明，风险状况会影响先前最优行为的选择，进而决定哪种类型的后验行为对先前最优行为的成本节约有积极或消极的贡献。基于这些发现，本文建议校准资产管理人的风险感知概况，以预测资产管理人对给定失败风险的最佳决策，并评估此类决策产生的预期VoI。

{"title":"Integrating risk perceptions in a value of information framework using cumulative prospect theory","authors":"Zaid Y Mir Rangrez , Jayadipta Ghosh , Colin Caprani , Siddhartha Ghosh","doi":"10.1016/j.strusafe.2025.102573","DOIUrl":"10.1016/j.strusafe.2025.102573","url":null,"abstract":"<div><div>Value of information (VoI) analysis provides a framework that can be used to decide on an optimal monitoring strategy, to carry out an efficient maintenance of civil infrastructure. Existing VoI frameworks adopt utility functions to characterize the risk appetite of an asset manager based on expected utility theory (EUT). However, these utility functions cannot predict the decision choices under uncertainty resulting from failure risk perceptions. Cumulative prospect theory (CPT) is a comprehensive model for characterizing an asset manager’s risk appetite and perception. CPT captures both, the preference for different action outcomes using a value function and corresponding risk perceptions exhibited by an asset manager using a probability weight function. The present study proposes a CPT-based VoI framework which integrates risk perceptions and appetite within the VoI analysis. The proposed framework is implemented to investigate the sensitivity of the resulting expected VoI and the monitoring decisions to risk perception profiles. It is observed that the VoI is sensitive to the risk perception profile of an asset manager. An in-depth analysis of the decision patterns reveal that the risk profile affects the choice of prior optimal action that in turn dictates which type of posterior actions contribute positively or negatively towards the cost savings when referenced to the cost of prior optimal action. Based on these finding, the paper recommends to calibrate an asset manager’s risk perception profile to predict the decisions that an asset manager perceives as optimal for a given failure risk, and to evaluate the expected VoI resulting from such decisions.</div></div>","PeriodicalId":21978,"journal":{"name":"Structural Safety","volume":"115 ","pages":"Article 102573"},"PeriodicalIF":5.7,"publicationDate":"2025-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143576880","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

In Memoriam of Ove Dalager Ditlevsen 纪念爱·达拉格·迪特莱夫森

IF 5.7 1区工程技术 Q1 ENGINEERING, CIVIL

Structural Safety

Pub Date : 2025-02-21 DOI: 10.1016/j.strusafe.2025.102585

Armen Der Kiureghian

引用次数: 0

Response probability distribution estimation of expensive computer simulators: A Bayesian active learning perspective using Gaussian process regression 昂贵计算机模拟器的响应概率分布估计：使用高斯过程回归的贝叶斯主动学习视角

IF 5.7 1区工程技术 Q1 ENGINEERING, CIVIL

Structural Safety

Pub Date : 2025-02-19 DOI: 10.1016/j.strusafe.2025.102579

Chao Dang , Marcos A. Valdebenito , Nataly A. Manque , Jun Xu , Matthias G.R. Faes

Estimation of the response probability distributions of computer simulators subject to input random variables is a crucial task in many fields. However, achieving this task with guaranteed accuracy remains an open computational challenge, especially for expensive-to-evaluate computer simulators. In this work, a Bayesian active learning perspective is presented to address the challenge, which is based on the use of the Gaussian process (GP) regression. First, estimation of the response probability distributions is conceptually interpreted as a Bayesian inference problem, as opposed to frequentist inference. This interpretation provides several important benefits: (1) it quantifies and propagates discretization error probabilistically; (2) it incorporates prior knowledge of the computer simulator, and (3) it enables the effective reduction of numerical uncertainty in the solution to a prescribed level. The conceptual Bayesian idea is then realized by using the GP regression, where we derive the posterior statistics of the response probability distributions in semi-analytical form and also provide a numerical solution scheme. Based on the practical Bayesian approach, a Bayesian active learning (BAL) method is further proposed for estimating the response probability distributions. In this context, the key contribution lies in the development of two crucial components for active learning, i.e., stopping criterion and learning function, by taking advantage of the posterior statistics. It is empirically demonstrated by five numerical examples that the proposed BAL method can efficiently estimate the response probability distributions with desired accuracy.

在输入随机变量的情况下，计算机模拟器响应概率分布的估计是许多领域的一项重要任务。然而，在保证精度的情况下实现这一任务仍然是一个开放的计算挑战，特别是对于昂贵的评估计算机模拟器。在这项工作中，提出了一个基于高斯过程（GP）回归的贝叶斯主动学习视角来解决这一挑战。首先，响应概率分布的估计在概念上被解释为贝叶斯推理问题，而不是频率推理问题。这种解释提供了几个重要的好处：(1)它量化和传播离散误差的概率；(2)它结合了计算机模拟器的先验知识，(3)它能够有效地将解决方案中的数值不确定性降低到规定的水平。然后利用GP回归实现了概念贝叶斯思想，在GP回归中，我们以半解析形式导出了响应概率分布的后验统计量，并提供了数值解方案。在实际贝叶斯方法的基础上，进一步提出了一种估计响应概率分布的贝叶斯主动学习（BAL）方法。在这种情况下，关键的贡献在于主动学习的两个关键组成部分的发展，即停止准则和学习函数，利用后验统计的优势。通过5个数值算例的经验证明，所提出的BAL方法能够有效地估计出响应概率分布，并具有理想的精度。

{"title":"Response probability distribution estimation of expensive computer simulators: A Bayesian active learning perspective using Gaussian process regression","authors":"Chao Dang , Marcos A. Valdebenito , Nataly A. Manque , Jun Xu , Matthias G.R. Faes","doi":"10.1016/j.strusafe.2025.102579","DOIUrl":"10.1016/j.strusafe.2025.102579","url":null,"abstract":"<div><div>Estimation of the response probability distributions of computer simulators subject to input random variables is a crucial task in many fields. However, achieving this task with guaranteed accuracy remains an open computational challenge, especially for expensive-to-evaluate computer simulators. In this work, a Bayesian active learning perspective is presented to address the challenge, which is based on the use of the Gaussian process (GP) regression. First, estimation of the response probability distributions is conceptually interpreted as a Bayesian inference problem, as opposed to frequentist inference. This interpretation provides several important benefits: (1) it quantifies and propagates discretization error probabilistically; (2) it incorporates prior knowledge of the computer simulator, and (3) it enables the effective reduction of numerical uncertainty in the solution to a prescribed level. The conceptual Bayesian idea is then realized by using the GP regression, where we derive the posterior statistics of the response probability distributions in semi-analytical form and also provide a numerical solution scheme. Based on the practical Bayesian approach, a Bayesian active learning (BAL) method is further proposed for estimating the response probability distributions. In this context, the key contribution lies in the development of two crucial components for active learning, i.e., stopping criterion and learning function, by taking advantage of the posterior statistics. It is empirically demonstrated by five numerical examples that the proposed BAL method can efficiently estimate the response probability distributions with desired accuracy.</div></div>","PeriodicalId":21978,"journal":{"name":"Structural Safety","volume":"114 ","pages":"Article 102579"},"PeriodicalIF":5.7,"publicationDate":"2025-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143464183","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A novel simulation method for the multivariate non-stationary non-Gaussian wind speed based on KL expansion and translation process theory 基于KL展开和平移过程理论的多变量非平稳非高斯风速模拟新方法

IF 5.7 1区工程技术 Q1 ENGINEERING, CIVIL

Structural Safety

Pub Date : 2025-02-18 DOI: 10.1016/j.strusafe.2025.102584

Fengbo Wu , Yu Wu , Ning Zhao

Accurate simulation of multivariate non-stationary non-Gaussian wind speed is the premise of evaluating the response of nonlinear structures. The methods based on Karhunen-Loève (KL) expansion and translation process method are extensively applied to predict non-stationary non-Gaussian simulation because it is easy for use and has relatively satisfactory simulation efficiency. However, these methods perform poorly in simulating the non-stationary strongly non-Gaussian process, especially the wind speed processes with highly skewed or bimodal distributions. This study comprehensively utilizes the KL expansion, the maximum entropy methods (MEM), and piecewise Hermite polynomial model (PHPM) to formulate a novel approach for simulating multivariate non-stationary non-Gaussian wind speed. In this method, the KL expansion is firstly used to generate the non-stationary Gaussian process. Then, a new strategy, the MEM is used to approximate the probability density function (PDF) of the target process which is then used to establish PHPM, is proposed to achieve the accurate and efficient simulation of non-stationary non-Gaussian process. The numerical results show that the proposed method has better simulation accuracy than traditional KL-based methods for non-stationary strongly non-Gaussian wind speed processes, especially the processes with highly skewed or bimodal distributions. Note that the proposed method can also be applied to simulate other non-Gaussian non-stationary excitations such as the wind pressure processes influenced by complex effects such as interference effect.

准确模拟多变量非平稳非高斯风速是评价非线性结构响应的前提。基于karhunen - lo (KL)展开法和平移过程法的非平稳非高斯模拟预测方法因其易于使用和具有比较满意的模拟效率而被广泛应用于非平稳非高斯模拟预测。然而，这些方法在模拟非平稳的强非高斯过程中表现不佳，特别是在高度偏态或双峰分布的风速过程中。综合运用KL展开、最大熵方法（MEM）和分段Hermite多项式模型（PHPM），提出了一种模拟多变量非平稳非高斯风速的新方法。该方法首先利用KL展开生成非平稳高斯过程。然后，提出了一种新的策略，即利用MEM近似目标过程的概率密度函数（PDF），然后利用该函数建立PHPM，以实现对非平稳非高斯过程的精确和高效模拟。数值结果表明，对于非平稳强非高斯风速过程，特别是具有高度偏态或双峰分布的风速过程，所提出的方法比传统的基于kl的方法具有更好的模拟精度。注意，所提出的方法也可以应用于模拟其他非高斯非平稳激励，如受干扰效应等复杂效应影响的风压过程。

{"title":"A novel simulation method for the multivariate non-stationary non-Gaussian wind speed based on KL expansion and translation process theory","authors":"Fengbo Wu , Yu Wu , Ning Zhao","doi":"10.1016/j.strusafe.2025.102584","DOIUrl":"10.1016/j.strusafe.2025.102584","url":null,"abstract":"<div><div>Accurate simulation of multivariate non-stationary non-Gaussian wind speed is the premise of evaluating the response of nonlinear structures. The methods based on Karhunen-Loève (KL) expansion and translation process method are extensively applied to predict non-stationary non-Gaussian simulation because it is easy for use and has relatively satisfactory simulation efficiency. However, these methods perform poorly in simulating the non-stationary strongly non-Gaussian process, especially the wind speed processes with highly skewed or bimodal distributions. This study comprehensively utilizes the KL expansion, the maximum entropy methods (MEM), and piecewise Hermite polynomial model (PHPM) to formulate a novel approach for simulating multivariate non-stationary non-Gaussian wind speed. In this method, the KL expansion is firstly used to generate the non-stationary Gaussian process. Then, a new strategy, the MEM is used to approximate the probability density function (PDF) of the target process which is then used to establish PHPM, is proposed to achieve the accurate and efficient simulation of non-stationary non-Gaussian process. The numerical results show that the proposed method has better simulation accuracy than traditional KL-based methods for non-stationary strongly non-Gaussian wind speed processes, especially the processes with highly skewed or bimodal distributions. Note that the proposed method can also be applied to simulate other non-Gaussian non-stationary excitations such as the wind pressure processes influenced by complex effects such as interference effect.</div></div>","PeriodicalId":21978,"journal":{"name":"Structural Safety","volume":"114 ","pages":"Article 102584"},"PeriodicalIF":5.7,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143464184","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Advanced terrain-adaptive tropical cyclone wind field modeling using deep learning for infrastructure resilience planning 基于深度学习的高级地形自适应热带气旋风场建模，用于基础设施弹性规划

IF 5.7 1区工程技术 Q1 ENGINEERING, CIVIL

Structural Safety

Pub Date : 2025-02-18 DOI: 10.1016/j.strusafe.2025.102580

Yilin Shi , Naiyu Wang , Bruce R. Ellingwood

Tropical cyclones pose significant threats to the resilience of coastal communities, underscoring the need for reliable wind field models to support robust hazard analyses. Parametric wind models (PWMs), despite their computational efficiency, often fall short in capturing intricate wind-terrain interactions, leading to inaccurate resilience evaluations for spatially-distributed civil infrastructure systems situated in complex terrains. This study introduces an innovative approach that integrates the strengths of numerical wind models to handle intricate terrain features into PWMs through a deep learning-based Convolutional Neural Network for Terrain Modification (CNN-TM). The CNN-TM model, trained over 3 million km² of numerically simulated high-resolution wind fields, enhances terrain representation in PWMs by generating 450 m-resolution terrain-modified wind fields for both wind speed and direction. The accuracy and efficiency of this integration are validated across multiple scales: grid (∼0.2 km²), patch (∼506 km²), and region (∼34,000 km²). Applications during Typhoon Hagupit (2020) in Zhejiang Province, China, demonstrate its practical effectiveness across a 105,000 km² area. By leveraging deep learning to synergize numerical and parametric models, the CNN-TM model addresses limitations of traditional PWMs and provides a robust tool for resilience-oriented decision-making for infrastructure systems in coastal regions characterized by complex terrains.

热带气旋对沿海社区的恢复能力构成重大威胁，强调需要可靠的风场模型来支持强有力的危害分析。参数风模型（PWMs）尽管计算效率很高，但在捕捉复杂的风-地形相互作用方面往往不足，导致对位于复杂地形的空间分布式民用基础设施系统的弹性评估不准确。本研究介绍了一种创新的方法，通过基于深度学习的卷积神经网络地形改造（CNN-TM），将数值风模型处理复杂地形特征的优势整合到pwm中。CNN-TM模型训练了超过300万平方公里的数值模拟高分辨率风场，通过生成450米分辨率的风速和风向地形修正风场，增强了PWMs中的地形表征。这种整合的准确性和效率在多个尺度上得到了验证：网格（~ 0.2 km2）、斑块（~ 506 km2）和区域（~ 34,000 km2）。在中国浙江省台风黑格比（2020）期间的应用，在105,000平方公里的面积上证明了其实际有效性。通过利用深度学习来协同数值模型和参数模型，CNN-TM模型解决了传统PWMs的局限性，并为具有复杂地形特征的沿海地区基础设施系统的弹性导向决策提供了强大的工具。

{"title":"Advanced terrain-adaptive tropical cyclone wind field modeling using deep learning for infrastructure resilience planning","authors":"Yilin Shi , Naiyu Wang , Bruce R. Ellingwood","doi":"10.1016/j.strusafe.2025.102580","DOIUrl":"10.1016/j.strusafe.2025.102580","url":null,"abstract":"<div><div>Tropical cyclones pose significant threats to the resilience of coastal communities, underscoring the need for reliable wind field models to support robust hazard analyses. Parametric wind models (PWMs), despite their computational efficiency, often fall short in capturing intricate wind-terrain interactions, leading to inaccurate resilience evaluations for spatially-distributed civil infrastructure systems situated in complex terrains. This study introduces an innovative approach that integrates the strengths of numerical wind models to handle intricate terrain features into PWMs through a deep learning-based Convolutional Neural Network for Terrain Modification (CNN-TM). The CNN-TM model, trained over 3 million km<sup>2</sup> of numerically simulated high-resolution wind fields, enhances terrain representation in PWMs by generating 450 m-resolution terrain-modified wind fields for both wind speed and direction. The accuracy and efficiency of this integration are validated across multiple scales: grid (∼0.2 km<sup>2</sup>), patch (∼506 km<sup>2</sup>), and region (∼34,000 km<sup>2</sup>). Applications during Typhoon Hagupit (2020) in Zhejiang Province, China, demonstrate its practical effectiveness across a 105,000 km<sup>2</sup> area. By leveraging deep learning to synergize numerical and parametric models, the CNN-TM model addresses limitations of traditional PWMs and provides a robust tool for resilience-oriented decision-making for infrastructure systems in coastal regions characterized by complex terrains.</div></div>","PeriodicalId":21978,"journal":{"name":"Structural Safety","volume":"114 ","pages":"Article 102580"},"PeriodicalIF":5.7,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143480618","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enhanced sequential directional importance sampling for structural reliability analysis 结构可靠性分析的增强顺序定向重要抽样

IF 5.7 1区工程技术 Q1 ENGINEERING, CIVIL

Structural Safety

Pub Date : 2025-02-06 DOI: 10.1016/j.strusafe.2025.102574

Kai Cheng, Iason Papaioannou, Daniel Straub

Sequential directional importance sampling (SDIS) Kai Cheng et al. (2023) is an efficient adaptive simulation method for estimating failure probabilities. It expresses the failure probability as the product of a group of integrals that are easy to estimate, wherein the first one is estimated with Monte Carlo simulation (MCS), and all the subsequent ones are estimated with directional importance sampling. In this work, we propose an enhanced SDIS method for structural reliability analysis. We discuss the efficiency of MCS for estimating the first integral in standard SDIS and propose using Subset Simulation as an alternative method. Additionally, we propose a Kriging-based active learning algorithm tailored to identify multiple roots in certain important directions within a specificed search interval. The performance of the enhanced SDIS is demonstrated through various complex benchmark problems. The results show that the enhanced SDIS is a versatile reliability analysis method that can efficiently and robustly solve challenging reliability problems.

程凯等（2023）是一种有效的故障概率估计自适应仿真方法。它将失效概率表示为一组易于估计的积分的乘积，其中第一个积分用蒙特卡罗模拟（MCS）估计，随后的所有积分都用方向重要抽样估计。在这项工作中，我们提出了一种改进的SDIS方法进行结构可靠性分析。我们讨论了MCS在标准SDIS中估计第一个积分的效率，并提出使用子集模拟作为一种替代方法。此外，我们提出了一种基于kriging的主动学习算法，该算法可以在特定的搜索间隔内识别某些重要方向上的多个根。通过各种复杂的基准测试问题验证了增强的SDIS的性能。结果表明，改进的SDIS是一种通用的可靠性分析方法，能够有效、稳健地解决具有挑战性的可靠性问题。

引用次数: 0

Sensitivity of ship hull reliability considering geometric imperfections and residual stresses 考虑几何缺陷和残余应力的船体可靠性敏感性

IF 5.7 1区工程技术 Q1 ENGINEERING, CIVIL

Structural Safety

Pub Date : 2025-02-05 DOI: 10.1016/j.strusafe.2025.102575

Aws Idris, Mohamed Soliman

Initial geometric imperfections and welding-induced residual stresses are inevitable consequences of ship fabrication and manufacturing processes. This paper quantifies the effect of these imperfections, as well as other input parameters, on the reliability of ship hull girders. The paper introduces a comprehensive variance-based sensitivity analysis approach, assisted by artificial neural networks, to characterize the key input parameters influencing the failure probability under different operational conditions. A total of 16 input parameters related to load and capacity quantification are considered in the simulation. The ultimate strength of the hull girder is quantified using a high-fidelity nonlinear finite element model that accounts for initial geometric imperfections and residual stresses. The vertical bending moments acting on the ship during its service life are quantified probabilistically. The results indicate that although it is essential to account for initial geometric imperfections to properly establish the ultimate hull capacity, the uncertainty in their magnitude has low effect on the reliability of the investigated hull. Accordingly, their magnitude can be considered deterministically in the probabilistic simulations. It was also found that the influence of various input parameters on the variability of the ship reliability depends on the considered operational condition.

初始几何缺陷和焊接残余应力是船舶制造过程中不可避免的结果。本文量化了这些缺陷以及其他输入参数对船体大梁可靠性的影响。本文介绍了一种基于方差的综合灵敏度分析方法，并辅以人工神经网络对不同工况下影响失效概率的关键输入参数进行表征。仿真中共考虑了16个与负荷和容量量化相关的输入参数。采用考虑初始几何缺陷和残余应力的高保真非线性有限元模型对船体梁的极限强度进行了量化。对舰船在使用寿命期间所受的垂直弯矩进行了概率量化。结果表明，虽然考虑初始几何缺陷是建立船体极限承载力的必要条件，但其大小的不确定性对船体可靠性的影响很小。因此，在概率模拟中，它们的大小可以被认为是确定性的。研究还发现，各种输入参数对舰船可靠性变异性的影响取决于所考虑的运行状态。

{"title":"Sensitivity of ship hull reliability considering geometric imperfections and residual stresses","authors":"Aws Idris, Mohamed Soliman","doi":"10.1016/j.strusafe.2025.102575","DOIUrl":"10.1016/j.strusafe.2025.102575","url":null,"abstract":"<div><div>Initial geometric imperfections and welding-induced residual stresses are inevitable consequences of ship fabrication and manufacturing processes. This paper quantifies the effect of these imperfections, as well as other input parameters, on the reliability of ship hull girders. The paper introduces a comprehensive variance-based sensitivity analysis approach, assisted by artificial neural networks, to characterize the key input parameters influencing the failure probability under different operational conditions. A total of 16 input parameters related to load and capacity quantification are considered in the simulation. The ultimate strength of the hull girder is quantified using a high-fidelity nonlinear finite element model that accounts for initial geometric imperfections and residual stresses. The vertical bending moments acting on the ship during its service life are quantified probabilistically. The results indicate that although it is essential to account for initial geometric imperfections to properly establish the ultimate hull capacity, the uncertainty in their magnitude has low effect on the reliability of the investigated hull. Accordingly, their magnitude can be considered deterministically in the probabilistic simulations. It was also found that the influence of various input parameters on the variability of the ship reliability depends on the considered operational condition.</div></div>","PeriodicalId":21978,"journal":{"name":"Structural Safety","volume":"114 ","pages":"Article 102575"},"PeriodicalIF":5.7,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143418857","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0