Structural Safety最新文献_第2页

A probability-based risk assessment of secondary fragments ejected from the reinforced concrete wall under close-in explosions

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.

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引用次数: 0

An efficient quantum computing based structural reliability analysis method using quantum amplitude estimation

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.

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引用次数: 0

Probabilistic prediction and early warning for bridge bearing displacement using sparse variational Gaussian process regression

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.

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引用次数: 0

Multi-point Bayesian active learning reliability analysis

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.

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引用次数: 0

Disaster risk-informed optimization using buffered failure probability for regional-scale building retrofit strategy

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.

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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。

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引用次数: 0

An Adaptive Gaussian Mixture Model for structural reliability analysis using convolution search technique 利用卷积搜索技术进行结构可靠性分析的自适应高斯混合模型

IF 5.7 1区工程技术 Q1 ENGINEERING, CIVIL

Structural Safety

Pub Date : 2024-11-24 DOI: 10.1016/j.strusafe.2024.102548

Futai Zhang , Jun Xu , Zhiqiang Wan

Non-parametric probability density estimation has gained popularity due to its flexibility and ease of use without requiring prior assumptions about distribution types. Notable examples include Kernel Density Estimation, Gaussian Mixture Model (GMM), the Mellin transform, and the Generalized Distribution Reconstruction (GDR) method, etc. However, these methods can encounter issues such as tail oscillation and sensitivity to initial guesses, particularly in the context of structural reliability analysis. To improve accuracy, this paper proposes an Adaptive Gaussian Mixture Model method. This method uses the inverse Fourier relationship between the Characteristic Function (CF) and the Probability Density Function (PDF), combined with a convolution search technique for parameter estimation. First, a more accurate expression for the CF is introduced, where the undetermined parameters are specified based on the numerically estimated CF curve. Then, a convolution search domain is developed to determine these parameters, including weight coefficients, mean domain, and standard deviation domain. Compared to the conventional methods for parameter estimation, the proposed convolution search technique can effectively avoid the problems of overfitting and initial parameter sensitivity. Using these parameters, the PDF is reconstructed and evolves into an Adaptive Gaussian Mixture Model. Numerical investigations are conducted to validate the efficacy of the proposed method, with comparisons made to the Mellin transform, GDR, Classic GMM, and other parametric methods.

非参数概率密度估计因其灵活性和易用性，无需事先假设分布类型而广受欢迎。著名的例子包括核密度估计、高斯混杂模型（GMM）、梅林变换和广义分布重构（GDR）方法等。然而，这些方法可能会遇到尾部振荡和对初始猜测敏感等问题，特别是在结构可靠性分析中。为了提高准确性，本文提出了一种自适应高斯混合模型方法。该方法利用特征函数（CF）和概率密度函数（PDF）之间的反傅里叶关系，结合卷积搜索技术进行参数估计。首先，引入更精确的 CF 表达式，根据数值估计的 CF 曲线指定未确定的参数。然后，开发了一个卷积搜索域来确定这些参数，包括权系数、均值域和标准偏差域。与传统的参数估计方法相比，所提出的卷积搜索技术能有效避免过拟合和初始参数敏感性的问题。利用这些参数，PDF 将被重建并演化为自适应高斯混合模型。通过与梅林变换、GDR、经典 GMM 及其他参数方法的比较，我们进行了数值研究以验证所提方法的有效性。

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引用次数: 0

The generalized first-passage probability considering temporal correlation and its application in dynamic reliability analysis 考虑时间相关性的广义首通概率及其在动态可靠度分析中的应用

IF 5.7 1区工程技术 Q1 ENGINEERING, CIVIL

Structural Safety

Pub Date : 2024-11-22 DOI: 10.1016/j.strusafe.2024.102547

Xian-Lin Yang , Ming-Ming Jia , Da-Gang Lu

In the traditional up-crossing rate approaches, the absence of consideration for correlation among crossing events often results in significant inaccuracies, particularly in scenarios involving stochastic processes with high autocorrelation and low thresholds. To fundamentally address these issues and limitations, the probability density function of the first passage time represented by the high-dimensional joint probability density function was investigated, and the equiprobable joint Gaussian (E-PHIn) method is proposed to prevent the redundant counting of the same crossing event. The innovation of the developed method is that it accounts for the correlation among different time instances of the stochastic process and allows for direct integration to derive the first-passage probabilities. When dealing with stochastic processes with unknown marginal distributions, the method of moments was introduced, complementing the E-PHIn method. Meanwhile, corresponding dimensionality reduction strategies are offered to improve computational efficiency. Through theoretical analysis and case studies, the results indicate that the conditional up-crossing rate represents the probability density function of the first-passage time. The E-PHIn method effectively addresses the first-passage problem for stochastic processes with either known or unknown marginal probability density functions. It fills the gap in traditional up-crossing rate approaches within the domain of nonlinear dynamic reliability. For the example structures, the E-PHIn method demonstrates higher accuracy compared to traditional point-based PDEM. Compared to MCS, the E-PHIn method significantly improves analytical efficiency while maintaining high precision for low-probability failure events.

在传统的上交叉率方法中，没有考虑交叉事件之间的相关性往往会导致显著的不准确性，特别是在涉及高自相关性和低阈值的随机过程的情况下。为了从根本上解决这些问题和局限性，研究了由高维联合概率密度函数表示的首次通过时间的概率密度函数，并提出了等概率联合高斯（E-PHIn）方法来防止同一交叉事件的重复计数。该方法的创新之处在于，它考虑了随机过程的不同时间实例之间的相关性，并允许直接积分来推导首次通过的概率。在处理边缘分布未知的随机过程时，引入矩量法作为E-PHIn方法的补充。同时，提出了相应的降维策略以提高计算效率。理论分析和实例分析表明，条件上交率是首次通过时间的概率密度函数。E-PHIn方法有效地解决了具有已知或未知边际概率密度函数的随机过程的首次通过问题。它填补了非线性动态可靠度领域中传统上交率方法的空白。对于实例结构，与传统的基于点的PDEM相比，E-PHIn方法具有更高的精度。与MCS相比，E-PHIn方法显著提高了分析效率，同时在低概率故障事件中保持了高精度。

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引用次数: 0

A novel deterministic sampling approach for the reliability analysis of high-dimensional structures 用于高维结构可靠性分析的新型确定性抽样方法

IF 5.7 1区工程技术 Q1 ENGINEERING, CIVIL

Structural Safety

Pub Date : 2024-11-20 DOI: 10.1016/j.strusafe.2024.102545

Yang Zhang , Jun Xu , Enrico Zio

Overcoming the “curse of dimensionality” in high-dimensional reliability analysis is still an enduring challenge. This paper proposes an innovative deterministic sampling method designed to overcome this challenge. The approach starts with a two-dimensional uniform point set, generated using the good lattice point method. This set is then refined through the cutting method to produce a specific number of points. A novel generating vector is computed based on this method, enabling the generation of the targeted high-dimensional point set through a strategic dimension-by-dimension mapping. Notably, this method eliminates the need for complex congruence computation and primitive root optimization, enhancing its efficiency for high-dimensional sampling. The resulting point set is deterministic and uniform, greatly reducing variability in reliability analysis. Then, the proposed approach is integrated into the fractional exponential moment-based maximum entropy method with the Box–Cox transform. This integration efficiently recovers the probability distribution for the limit state function (LSF) with high-dimensional inputs, enabling precise assessment of the failure probability. The efficacy of the proposed method is demonstrated through three high-dimensional numerical examples, involving both explicit and implicit LSFs, highlighting its applicability for high-dimensional reliability analysis of structures.

克服高维度可靠性分析中的 "维度诅咒 "仍然是一项持久的挑战。本文提出了一种创新的确定性抽样方法，旨在克服这一难题。该方法以二维均匀点集为起点，该点集是利用良好网格点法生成的。然后通过切割法对该集合进行细化，以产生特定数量的点。在此基础上计算出一个新颖的生成向量，通过策略性的逐维映射生成目标高维点集。值得注意的是，这种方法无需复杂的全等计算和原始根优化，提高了高维采样的效率。所得到的点集具有确定性和均匀性，大大降低了可靠性分析中的变异性。然后，利用 Box-Cox 变换将所提出的方法集成到基于分数指数矩的最大熵方法中。这种集成能有效地恢复具有高维输入的极限状态函数（LSF）的概率分布，从而实现对故障概率的精确评估。通过三个涉及显式和隐式 LSF 的高维数值示例，证明了所提方法的有效性，突出了其在结构高维可靠性分析中的适用性。

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引用次数: 0

A stratified beta-sphere sampling method combined with important sampling and active learning for rare event analysis 用于罕见事件分析的分层贝塔球取样法与重要取样和主动学习相结合

IF 5.7 1区工程技术 Q1 ENGINEERING, CIVIL

Structural Safety

Pub Date : 2024-11-20 DOI: 10.1016/j.strusafe.2024.102546

Fangqi Hong , Jingwen Song , Pengfei Wei , Ziteng Huang , Michael Beer

Accurate and efficient estimation of small failure probability subjected to high-dimensional and multiple failure domains is still a challenging task in structural reliability engineering. In this paper, we propose a stratified beta-spheres sampling method (SBSS) to tackle this task. Initially, the whole support space of random input variables is divided into a series of subdomains by using multiple specified beta-spheres, which is a hypersphere centered in the origin in standard normal space, then, the corresponding samples truncated by beta-spheres are generated explicitly and efficiently. Based on the truncated samples, the real failure probability can be estimated by the sum of failure probabilities of these subdomains. Next, we discuss and demonstrate the unbiasedness of the estimation of failure probability. The proposed method stands out for inheriting the advantages of Monte Carlo simulation (MCS) for highly nonlinear, high-dimensional problems, and problems with multiple failure domains, while overcoming the disadvantages of MCS for rare event. Furthermore, the SBSS method equipped with importance sampling technique (SBSS-IS) is also proposed to improve the robustness of estimation. Additionally, we combine the proposed SBSS and SBSS-IS methods with GPR model and active learning strategy so as to further substantially reduce the computational cost under the desired requirement of estimated accuracy. Finally, the superiorities of the proposed methods are demonstrated by six examples with different problem settings.

在结构可靠性工程中，准确有效地估计高维和多失效域的小失效概率仍然是一项具有挑战性的任务。本文提出了一种分层 beta 球体抽样方法（SBSS）来解决这一问题。首先，使用多个指定的贝塔球将随机输入变量的整个支持空间划分为一系列子域。根据截断样本，可以通过这些子域的失效概率之和估算出真正的失效概率。接下来，我们讨论并证明了失效概率估计的无偏性。所提出的方法继承了蒙特卡洛模拟（MCS）在处理高度非线性、高维问题和多失效域问题时的优点，同时克服了蒙特卡洛模拟在处理罕见事件时的缺点。此外，我们还提出了配备重要性抽样技术的 SBSS 方法（SBSS-IS），以提高估计的鲁棒性。此外，我们还将所提出的 SBSS 和 SBSS-IS 方法与 GPR 模型和主动学习策略相结合，从而在保证估计精度的前提下进一步大幅降低计算成本。最后，我们通过六个不同问题设置的实例证明了所提方法的优越性。

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引用次数: 0