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Global sensitivity analysis of the maximum live load and its applications 最大活载荷的全局敏感性分析及其应用
IF 5.8 1区 工程技术 Q1 ENGINEERING, CIVIL Pub Date : 2024-04-26 DOI: 10.1016/j.strusafe.2024.102476
Chi Xu , Jun Chen , Jie Li

The design live loads are determined by the probability distribution of the maximum live load, which is influenced by the amplitudes and time intervals of various sustained and extraordinary loads. If the relative impact of different input variables on the maximum can be clarified, more targeted load surveys and modeling can be achieved. However, there is currently no global sensitivity analysis that simultaneously considers all input variables. In this study, the probability density function of the maximum live load is determined using the load coincidence principle and probability density evolution method. The relative entropy is employed as a measure for conducting a global sensitivity analysis across five common building occupancy types. The results indicate a significant imbalance in the impact of different input variables. The load amplitudes have a much greater effect than the time intervals. Among various load amplitudes, those related to the extraordinary loads often have the most significant impact. Regarding the time intervals, the occurrence intervals corresponding to the extraordinary loads caused by furniture stacking and normal crowding consistently have the least influence. For the time intervals with minimal impact, it is suggested to treat them as deterministic values in the live load modeling. This treatment has a negligible impact (not exceeding 10%) on the mean and upper fractile of the maximum, which are generally used for design in load codes.

设计活荷载由最大活荷载的概率分布决定,而最大活荷载又受到各种持续荷载和超常荷载的振幅和时间间隔的影响。如果能明确不同输入变量对最大荷载的相对影响,就能进行更有针对性的荷载测量和建模。然而,目前还没有同时考虑所有输入变量的全局敏感性分析。本研究利用荷载重合原理和概率密度演化方法确定了最大活荷载的概率密度函数。在对五种常见建筑占用类型进行全局敏感性分析时,采用了相对熵作为衡量标准。结果表明,不同输入变量的影响严重失衡。负载振幅的影响远远大于时间间隔的影响。在各种负载振幅中,与特殊负载相关的振幅往往影响最大。就时间间隔而言,与家具堆放和正常拥挤造成的超常荷载相对应的时间间隔始终影响最小。对于影响最小的时间间隔,建议在活荷载建模中将其视为确定值。这种处理方法对最大值的平均值和上分位数的影响可以忽略不计(不超过 10%),而这些值通常用于荷载规范的设计。
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引用次数: 0
Reliability analysis of complex systems using subset simulations with Hamiltonian Neural Networks 利用汉密尔顿神经网络子集模拟进行复杂系统可靠性分析
IF 5.8 1区 工程技术 Q1 ENGINEERING, CIVIL Pub Date : 2024-04-25 DOI: 10.1016/j.strusafe.2024.102475
Denny Thaler , Somayajulu L.N. Dhulipala , Franz Bamer , Bernd Markert , Michael D. Shields

We present a new Subset Simulation approach using Hamiltonian neural network-based Monte Carlo sampling for reliability analysis. The proposed strategy combines the superior sampling of the Hamiltonian Monte Carlo method with computationally efficient gradient evaluations using Hamiltonian neural networks. This combination is especially advantageous because the neural network architecture conserves the Hamiltonian, which defines the acceptance criteria of the Hamiltonian Monte Carlo sampler. Hence, this strategy achieves high acceptance rates at low computational cost. Our approach estimates small failure probabilities using Subset Simulations. However, in low-probability sample regions, the gradient evaluation is particularly challenging. The remarkable accuracy of the proposed strategy is demonstrated on different reliability problems, and its efficiency is compared to the traditional Hamiltonian Monte Carlo method. We note that this approach can reach its limitations for gradient estimations in low-probability regions of complex and high-dimensional distributions. Thus, we propose techniques to improve gradient prediction in these particular situations and enable accurate estimations of the probability of failure. The highlight of this study is the reliability analysis of a system whose parameter distributions must be inferred with Bayesian inference problems. In such a case, the Hamiltonian Monte Carlo method requires a full model evaluation for each gradient evaluation and, therefore, comes at a very high cost. However, using Hamiltonian neural networks in this framework replaces the expensive model evaluation, resulting in tremendous improvements in computational efficiency.

我们提出了一种新的子集模拟方法,利用基于哈密尔顿神经网络的蒙特卡罗抽样进行可靠性分析。所提出的策略结合了哈密尔顿蒙特卡洛法的出色采样和使用哈密尔顿神经网络的高效梯度计算。这种结合尤其具有优势,因为神经网络结构保留了哈密顿,而哈密顿定义了哈密顿蒙特卡洛采样器的接受标准。因此,这种策略能以较低的计算成本实现较高的接受率。我们的方法使用子集模拟来估算小故障概率。然而,在低概率样本区域,梯度评估尤其具有挑战性。我们在不同的可靠性问题上证明了所提出策略的卓越准确性,并将其效率与传统的汉密尔顿蒙特卡罗方法进行了比较。我们注意到,这种方法在复杂和高维分布的低概率区域的梯度估计中会受到限制。因此,我们提出了在这些特殊情况下改进梯度预测的技术,从而实现对失效概率的准确估计。本研究的亮点是对参数分布必须通过贝叶斯推理问题来推断的系统进行可靠性分析。在这种情况下,哈密尔顿蒙特卡洛方法需要对每个梯度进行完整的模型评估,因此成本非常高。然而,在此框架中使用汉密尔顿神经网络可以取代昂贵的模型评估,从而极大地提高计算效率。
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引用次数: 0
Development of methods of structural reliability 开发结构可靠性方法
IF 5.7 1区 工程技术 Q1 ENGINEERING, CIVIL Pub Date : 2024-04-18 DOI: 10.1016/j.strusafe.2024.102474
Bruce Ellingwood , Marc Maes , F. Michael Bartlett , Andre T. Beck , Colin Caprani , Armen Der Kiureghian , Leonardo Dueñas-Osorio , Neryvaldo Galvão , Robert Gilbert , Jie Li , Jose Matos , Yasuhiro Mori , Iason Papaioannou , Roger Parades , Daniel Straub , Bruno Sudret
The growth of structural reliability theory and applications, along with a recognition of its role in guiding the structural engineering profession in addressing some of the most important issues in design of the built environment, represents one of the key engineering achievements during the past five decades. Structural reliability provides a unifying framework for managing uncertainties affecting performance of structures and a quantitative link between the practice of structural engineering and its social consequences. Such links perhaps are most obvious in probability-based codified design and performance evaluation but there are numerous other applications, which are summarized in this special issue. As the field has matured, researchers in reliability have worked with structural engineers to elevate both the practice of structural engineering and the quality of research to levels that otherwise would not have been possible. The Joint Committee on Structural Safety has played a central role in this historic development and it will inspire future opportunities for the reliability community to build upon past successes to improve structural engineering and construction practices. This paper surveys the key theoretical developments and milestones that enable these opportunities.
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引用次数: 0
Interpretation of probability in structural safety – A philosophical conundrum 结构安全中的概率解释--一个哲学难题
IF 5.7 1区 工程技术 Q1 ENGINEERING, CIVIL Pub Date : 2024-04-15 DOI: 10.1016/j.strusafe.2024.102473
Ton Vrouwenvelder , André Beck , Dirk Proske , Michael Faber , Jochen Köhler , Matthias Schubert , Daniel Straub , Max Teichgräber
The term probability is essential in the domain of structural safety and yet its interpretation is, even after more than 50 years of application, still a subject of discussion. For instance, the probability of failure of structures belonging to the same cohort for a specific period of time, is often understood in a pure frequentist way as an observable average number of failure events for that period and portfolio. By contrast, the Bayesian interpretation considers probability as a degree of belief and a reflection of the state of information to the best belief or knowledge of the decision maker. In the field of structural reliability, depending on the type of decision problem, probabilities are often referred to as nominal (or notional) measures of uncertainty to emphasize that these values are conditional on a model and available observations. Probabilistic methods then serve primarily to undertake the book-keeping required to assign probabilities to different possible outcomes or decisions in consistency with models, available observations and other relevant information. This paper discusses the background of these interpretations and the degree to which correspondence between reliability calculations and observations of failures can be expected and/or achieved. Recommendations corresponding to the JCSS line of thinking will be summarized in Section 8.
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引用次数: 0
A baseline approach for probabilistic blast risk analysis of building cladding under external explosions 外部爆炸下建筑物覆层爆炸风险概率分析的基线方法
IF 5.8 1区 工程技术 Q1 ENGINEERING, CIVIL Pub Date : 2024-04-10 DOI: 10.1016/j.strusafe.2024.102472
Orestis Ioannou, Georgios Rigoutsos, Dimitrios Vamvatsikos, Charis J. Gantes

The state of the practice in blast-resistant applications against explosions is to design the structural components for a prescribed combination of explosive mass and location, namely the design basis threat. In this context, the blast source is represented by specific scenarios, mostly associated with expert judgement, rating systems or code provisions. While offering a useful basis for practical applications, the level of detail can be significantly enhanced within a probabilistic framework for risk assessment. In research practice, plenty simplified probabilistic approaches have been proposed on external explosions in order to perform risk assessment. A rigorous methodology for such an assessment is presented herein, using tools and techniques derived from seismic risk-assessment applications. Specifically, the mean annual frequency of different explosive mass hazards is represented via a recurrence law, while the potential locations are modelled as a 2D spatial distribution, accounting for the various layers of defense that exist around the structure, i.e., perimeter protection, landscape, public or private spaces, etc. The methodology is finally substantiated with the case study of a typical building subjected to potential blast loadings from external aggressors.

在防爆应用中,针对爆炸质量和爆炸位置(即设计基础威胁)的规定组合来设计结构部件,是防爆应用中的惯例。在这种情况下,爆炸源由特定场景表示,大多与专家判断、评级系统或规范条款有关。虽然为实际应用提供了一个有用的基础,但在风险评估的概率框架内,详细程度可以大大提高。在研究实践中,针对外部爆炸提出了大量简化的概率方法,以进行风险评估。本文利用地震风险评估应用中衍生的工具和技术,提出了进行此类评估的严格方法。具体来说,不同爆炸质量危害的年平均频率通过重现定律表示,而潜在位置则以二维空间分布建模,并考虑到结构周围存在的各种防御层,即周边保护、景观、公共或私人空间等。最后,通过对一栋典型建筑进行案例研究,证实了该方法可承受外部攻击者的潜在爆炸荷载。
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引用次数: 0
Adaptive sampling based estimation of small probability of failure using interpretable Self-Organising Map 利用可解释自组织图,基于自适应抽样估算小故障概率
IF 5.8 1区 工程技术 Q1 ENGINEERING, CIVIL Pub Date : 2024-04-03 DOI: 10.1016/j.strusafe.2024.102470
Deepanshu Yadav, Kannan Sekar, Palaniappan Ramu

Structural and multidisciplinary design under uncertainty for high reliability or equivalently small probability of failure is a challenging task owing to the high computational cost associated with generating the samples at the extreme (tail) of the underlying distribution. Among other approaches, statistics of extremes based techniques are usually suitable for small probability estimation. However, typically only 10% of the samples generated that correspond to the tail of the distribution are used for probability estimation. If apriori information about regions in the design space that corresponds to the tail is available, additional samples in the identified region permit better tail fit and hence better probability estimation. In the current work, we propose iSOM (interpretable Self-Organising Map) to identify region/s in the design space, that corresponds to the extremes. An initial sample is used to map (visualize) the limit state function and random/design variables using iSOM which permits the designer to identify the region(s) that corresponds to the tail of the response. Adaptive sampling is performed in the identified region of interest to obtain additional samples. Next, the cumulative distribution function (CDF) of the response using initial as well as adaptive samples is evaluated for probability estimation. The effectiveness of the proposed approach is evident from its successful implementation on benchmark examples, real-world engineering examples, and a multi-objective reliability-based design optimization (MORBDO) case. The proposed method showcases the capability of iSOM to perform adaptive sampling for limit-state functions characterized by non-linearity and multiple modes. iSOM-enabled sampling in conjunction with log-TPNT provides better estimates of small failure probabilities than log-TPNT alone. The results from the proposed approach is compared with results from state-of-the-art (SOTA) sampling and surrogate-based techniques. For a given number of limit state evaluations, the proposed approach estimates probabilities of the order 1e−4, with lesser variance, compared to other SOTA approaches. Hence, the proposed approach is likely to encourage further research into employing iSOM-assisted sampling for other reliability estimation methods as well.

在不确定条件下进行结构设计和多学科设计,以获得高可靠性或等效的小故障概率,是一项极具挑战性的任务,因为在基本分布的极端(尾部)生成样本的计算成本很高。在其他方法中,基于极值统计的技术通常适用于小概率估计。然而,通常情况下,生成的样本中只有 10% 与分布尾部相对应,才会用于概率估计。如果设计空间中与尾部相对应的区域的先验信息可用,则识别区域中的额外样本可实现更好的尾部拟合,从而实现更好的概率估计。在当前的工作中,我们提出了 iSOM(可解释自组织图)来识别设计空间中与极端值相对应的区域。利用 iSOM,初始样本被用来映射(可视化)极限状态函数和随机/设计变量,从而使设计者能够识别与响应尾部相对应的区域。在确定的相关区域内进行自适应采样,以获得更多样本。接下来,使用初始样本和自适应样本对响应的累积分布函数(CDF)进行评估,以进行概率估计。通过在基准实例、实际工程实例和基于可靠性的多目标优化设计(MORBDO)案例中的成功实施,证明了所提方法的有效性。所提出的方法展示了 iSOM 对以非线性和多模式为特征的极限状态函数进行自适应采样的能力。建议方法的结果与最先进的(SOTA)采样和基于代用技术的结果进行了比较。对于给定数量的极限状态评估,与其他 SOTA 方法相比,拟议方法估计的概率为 1e-4 数量级,方差较小。因此,所提出的方法可能会鼓励进一步研究在其他可靠性估计方法中采用 iSOM 辅助抽样。
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引用次数: 0
IF 5.8 1区 工程技术 Q1 ENGINEERING, CIVIL Pub Date : 2024-04-03 DOI: 10.1016/j.strusafe.2024.102471
Mitsuyoshi Akiyama
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引用次数: 0
System risk modelling and decision-making – Reflections and common pitfalls 系统风险建模与决策--思考与常见误区
IF 5.7 1区 工程技术 Q1 ENGINEERING, CIVIL Pub Date : 2024-03-29 DOI: 10.1016/j.strusafe.2024.102469
Niels Peter Høj , Inger Birgitte Kroon , Jannie Sønderkær Nielsen , Matthias Schubert
Since its foundation, the Joint Committee on Structural Safety (JCSS) has been engaged in the discussion of methods for determining the reliability of components, calibration of standards, as well as risk modelling of systems. In publications, it is regularly explained which methods have which advantages. In the literature, the drawbacks and pitfalls that challenge rational decisions and help to develop and find more appropriate methods for practice are often not documented.
Such problems can lead to decisions, which are not rational from a decision-theoretic point of view, some of which are worse than a random decision. Especially events, with a very small probability of occurrence hardly give any feedback possibilities from reality and evidence-based analysis of decisions is not possible. Careful selection of methods and knowledge/information of the assumptions is crucial to rational decisions.
This paper will discuss some of the identified pitfalls based on the discussions in the JCSS. It will span from aspects in the uncertainty quantification, uncertainty propagation, consequence assessment as well as approaches that are found and used in practice for decision-making (e.g. probability interpretations, risk aversion, risk matrices and FN diagrams). This paper can be seen as a documentation of outtakes from the discussions which led to the joint understanding and approach of the JCSS. The paper does not claim to be complete concerning all the possible pitfalls in risk assessments and system identification. But it does provide important reflections and indicates where the eyes must be kept open. Further, the paper points to a way of rational decision-making accounting for the uncertainties in information.
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引用次数: 0
The JCSS – A major contributor to structural safety through half a century JCSS - 半个世纪以来对结构安全的主要贡献者
IF 5.7 1区 工程技术 Q1 ENGINEERING, CIVIL Pub Date : 2024-03-24 DOI: 10.1016/j.strusafe.2024.102468
Ton Vrouwenvelder , Michael Baker , Michael Havbro Faber
Society expects buildings and structures in the built environment to possess an adequate level of safety. This holds true throughout the whole life cycle of the structure, from the erection stage until the end of life and also during its eventual demolition. In order to achieve this objective, engineers need to have knowledge of structural materials, the use of the structure and the various interactions with the environment. Fundamentally, this knowledge is rarely available in a deterministic form and decisions have to be made taking into account the various sources of uncertainty that affect the structure’s behaviour. The need to answer the question of how to account for such uncertainties and their effects on structural safety was one of the main reasons for the creation of the Joint Committee on Structural Safety over half a century ago. This paper presents an historical account of how it started, the development of the philosophical background and objectives over the course of time, and its main achievements and key publications. The paper closes with a short reflection on what has been achieved and what is required in the future.
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引用次数: 0
An enhanced learning function for bootstrap polynomial chaos expansion-based enhanced active learning algorithm for reliability analysis of structure 基于引导多项式混沌扩展的增强型主动学习算法的学习函数,用于结构可靠性分析
IF 5.8 1区 工程技术 Q1 ENGINEERING, CIVIL Pub Date : 2024-03-20 DOI: 10.1016/j.strusafe.2024.102467
Avinandan Modak, Subrata Chakraborty

Sparse polynomial chaos expansion (PCE) combined with the bootstrap resampling method is a viable alternative to obtain an active learning algorithm for reliability analysis. The existing learning functions in PCE-based active learning algorithms do not consider the joint probability density function (PDF) information. The present study explores a sparse PCE-based active learning algorithm based on a newly proposed learning function that maintains a balance between the misclassification probability and the joint PDF information of sample points. In doing so, the coefficients of the sparse PCE are estimated using a Bayesian compressive sensing regressor, as it is noted to be one of the best-performing regression solvers for PCE, irrespective of sampling schemes. The proposed learning function considers the weight of the joint PDF with the local accuracy measure of bootstrap PCE (bPCE) to add new samples iteratively in the existing training set. The convergence is achieved when the ten consecutive failure estimates are within a negligible discrepancy and also checks the confidence bounds of the bPCE estimates. The effectiveness of the proposed approach is demonstrated using two structural engineering examples and one well-known analytical test function and is found to be quite efficient and accurate in estimating reliability.

稀疏多项式混沌扩展(PCE)与自举重采样法相结合,是获得可靠性分析主动学习算法的一种可行选择。现有基于 PCE 的主动学习算法中的学习函数并未考虑联合概率密度函数 (PDF) 信息。本研究探讨了一种基于稀疏 PCE 的主动学习算法,该算法基于新提出的学习函数,能在误分类概率和样本点的联合 PDF 信息之间保持平衡。在此过程中,稀疏 PCE 的系数使用贝叶斯压缩感知回归器进行估计,因为该回归器是 PCE 性能最佳的回归求解器之一,与采样方案无关。所提出的学习函数考虑了联合 PDF 的权重和自举 PCE(bPCE)的局部精确度,在现有训练集中迭代添加新样本。当连续十次故障估计值的偏差都在可忽略不计的范围内时,就实现了收敛,同时也检验了 bPCE 估计值的置信区间。利用两个结构工程实例和一个著名的分析测试函数证明了所提方法的有效性,并发现该方法在估计可靠性方面相当高效和准确。
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引用次数: 0
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Structural Safety
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