Structural Safety最新文献_第8页

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 : 2025-05-01 Epub 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。

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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 : 2025-05-01 Epub 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。

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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 : 2025-05-01 Epub 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。

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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 : 2025-05-01 Epub 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。

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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 : 2025-05-01 Epub 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":"2025-05-01","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

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 : 2025-05-01 Epub 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。

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引用次数: 0

Sequential and adaptive probabilistic integration for structural reliability analysis 结构可靠性分析的顺序自适应概率集成

IF 5.7 1区工程技术 Q1 ENGINEERING, CIVIL

Structural Safety

Pub Date : 2025-05-01 Epub Date: 2025-02-05 DOI: 10.1016/j.strusafe.2025.102577

Masaru Kitahara , Pengfei Wei

We propose the application of sequential and adaptive probabilistic integration (SAPI) to the estimation of the probability of failure in structural reliability. SAPI was originally developed to explore the posterior distribution and estimate its normalising constant in Bayesian model updating. The principle is to perform probabilistic integration on a sequence of distributions, moving from the prior to the posterior, to learn the normalising constant of each distribution. In structural reliability, SAPI can be used to sample an approximation of the optimal importance sampling (IS) density, and we present a particular choice of the intermediate distributions. The derived SAPI estimator is thus an IS estimator of the thought probability. The numerical uncertainty is propagated using random process sampling, and the induced posterior statistics are used to design a Bayesian active learning strategy. Four numerical examples demonstrate that SAPI outperforms other state-of-the-art active learning reliability methods using sequential Monte Carlo samplers.

提出了序列自适应概率积分法（SAPI）在结构可靠性失效概率估计中的应用。SAPI最初是为了在贝叶斯模型更新中探索后验分布并估计其归一化常数而开发的。其原理是对一系列分布进行概率积分，从先验到后验，以学习每个分布的归一化常数。在结构可靠性中，SAPI可用于抽样最优重要抽样（IS）密度的近似值，并给出了中间分布的特定选择。因此，导出的SAPI估计量是思想概率的is估计量。采用随机过程抽样方法传播数值不确定性，并利用诱导后验统计量设计贝叶斯主动学习策略。四个数值示例表明，SAPI优于使用顺序蒙特卡罗采样器的其他最先进的主动学习可靠性方法。

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 : 2025-05-01 Epub 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。

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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 : 2025-05-01 Epub 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。

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引用次数: 0

The JCSS probabilistic model Code, future developments JCSS概率模型代码，未来发展

IF 5.7 1区工程技术 Q1 ENGINEERING, CIVIL

Structural Safety

Pub Date : 2025-03-01 Epub Date: 2024-10-06 DOI: 10.1016/j.strusafe.2024.102540

R.D.J.M. Steenbergen , A.C.W.M. Vrouwenvelder

To assess and verify the reliability of structures, reliability based building codes allow for the application of full-probabilistic methods and semi-probabilistic methods (i.e. the partial factor method). In principle, both methods should be equivalent and lead to (approximately) the same reliability level. Therefore partial factors should be as much as possible determined based on a probabilistic background and calibration exercises. On the other hand, as the probabilistic design method may be considered as more rational and consistent than the partial factor design, there is a tendency to use probabilistic methods directly in the assessment of special of important new structures and also in the assessment of existing structures. In both the calibration exercise and in the full probabilistic assessment of structures, we face the problem that many assumptions have to be made. In particular in regard to the statistical modelling of random variables and in regard to accepted approximative methods of calculation. This often brings the engineer to a challenging position. In the past years the JCSS probabilistic model code (PMC) has served as an often-consulted operational code for this purpose. In the present paper, the JCSS PMC and its future developments are presented and discussed.

为了评估和验证结构的可靠性，基于可靠性的建筑规范允许应用全概率方法和半概率方法（即部分因子方法）。原则上，这两种方法应该是等效的，并且产生（近似）相同的可靠性水平。因此，部分因素应尽可能根据概率背景和校准练习来确定。另一方面，由于概率设计方法比部分因子设计方法更为合理和一致，因此在特殊的重要新结构的评估和既有结构的评估中，有直接使用概率方法的趋势。在校正工作和对结构进行全面概率评估时，我们面临的问题是必须作出许多假设。特别是关于随机变量的统计建模和关于可接受的近似计算方法。这通常会把工程师带到一个具有挑战性的位置。在过去的几年中，JCSS概率模型代码（PMC）一直是为此目的经常参考的操作代码。本文对JCSS PMC及其未来发展进行了介绍和讨论。

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引用次数: 0