Structural Safety最新文献_第3页

Assessment of risk reduction strategies for terrorist attacks on structures

IF 5.7 1区工程技术

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

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

引用次数: 0

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

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

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

{"title":"A probability-based risk assessment of secondary fragments ejected from the reinforced concrete wall under close-in explosions","authors":"Zitong Wang , Qilin Li , Wensu Chen , Hong Hao , Ling Li","doi":"10.1016/j.strusafe.2024.102565","DOIUrl":"10.1016/j.strusafe.2024.102565","url":null,"abstract":"<div><div>Improvised explosive device (IED) poses a significant threat due to its simplicity of fabrication and deployment. For reinforced concrete (RC) walls, the close-in IED explosions could cause severe structural damage, and the resultant high-velocity secondary fragments endanger people and facilities in the surrounding area. Existing safety standards regarding safety distance are not applicable for close-in IED explosions. This study proposes a probability-based risk assessment method to estimate human casualty risks from secondary fragment ejection caused by close-in IED explosions. This method leverages data from a machine-learning-based Fragment Graph Network (FGN) developed in the authors’ previous research, simulating secondary fragments more efficiently than traditional methods. By analysing fragment distribution data and applying logistic regression analysis, safety distances to avoid human casualties corresponding to various safety probability thresholds are determined. Consequently, the proposed systematic risk assessment method for secondary fragments enables precise determination of safety distances to mitigate potential injuries in close-in IED blast scenarios. Empirical formulae are developed for fast estimation of safety distances required for different blast scenarios and wall configurations.</div></div>","PeriodicalId":21978,"journal":{"name":"Structural Safety","volume":"114 ","pages":"Article 102565"},"PeriodicalIF":5.7,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143151100","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

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

IF 5.7 1区工程技术

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

Jingran He

{"title":"An efficient quantum computing based structural reliability analysis method using quantum amplitude estimation","authors":"Jingran He","doi":"10.1016/j.strusafe.2024.102555","DOIUrl":"10.1016/j.strusafe.2024.102555","url":null,"abstract":"<div><div>Efficient structural reliability analysis methods are of great concern in civil engineering. Although excellent works have be dedicated in the past years for improving the computation efficiency in classical computer, the development of quantum computer has shown new potential to further extend the boundary of computation efficiency. In this paper, an efficient quantum computing based structural reliability assessment method is proposed. Compared with the Monte Carlo method in classical computer, the major advantage of quantum amplitude estimation method is that the computation cost is reduced from <span><math><mrow><mi>O</mi><mfenced><mrow><mi>N</mi></mrow></mfenced></mrow></math></span> to <span><math><mrow><mi>O</mi><mfenced><mrow><msqrt><mi>N</mi></msqrt></mrow></mfenced></mrow></math></span> for the failure probability being <span><math><mrow><mi>O</mi><mfenced><mrow><mrow><mn>1</mn><mo>/</mo><mi>N</mi></mrow></mrow></mfenced></mrow></math></span>. The present study formulated the reliability problems by means of quantum computing using quantum amplitude estimation. And a simple numerical application example is given to verify the proposed method with comparison to Monte Carlo method.</div></div>","PeriodicalId":21978,"journal":{"name":"Structural Safety","volume":"114 ","pages":"Article 102555"},"PeriodicalIF":5.7,"publicationDate":"2024-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143151078","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

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

IF 5.7 1区工程技术

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

Yafei Ma, Bachao Zhang, Ke Huang, Lei Wang

{"title":"Probabilistic prediction and early warning for bridge bearing displacement using sparse variational Gaussian process regression","authors":"Yafei Ma, Bachao Zhang, Ke Huang, Lei Wang","doi":"10.1016/j.strusafe.2024.102564","DOIUrl":"10.1016/j.strusafe.2024.102564","url":null,"abstract":"<div><div>Investigating the relationship between temperature variations and bridge bearing displacement is crucial for ensuring structural integrity and safety. However, the current temperature-displacement regression (TDR) model fails to account for inherent uncertainties in monitoring data and model errors. This paper proposes a probabilistic prediction and early warning framework for displacement of bridge bearing using the sparse variational Gaussian process regression (SVGPR) model. The time-varying relationships between temperature and bearing displacement at different time scales are analyzed. The SVGP-TDR model is constructed based on the fully independent training condition (FITC), and the induced points and hyperparameters are optimized simultaneously by combining variational learning and gradient descent method. An early warning method for bearing performance is proposed based on the model estimation error and Shewhart control chart theory, along with the implementation procedure provided. The effectiveness of the proposed method is verified using long-term monitoring data from an existing suspension bridge. The results show that the SVGP-TDR model can predict probability distribution of bearing displacement caused by temperature. Moreover, it can not only consider the uncertainty in the monitoring data, but also quantify the model error and prediction uncertainty. The proposed early warning method performs satisfactorily in assessing the service performance of bridge bearing.</div></div>","PeriodicalId":21978,"journal":{"name":"Structural Safety","volume":"114 ","pages":"Article 102564"},"PeriodicalIF":5.7,"publicationDate":"2024-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143150398","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multi-point Bayesian active learning reliability analysis

IF 5.7 1区工程技术

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

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

{"title":"Multi-point Bayesian active learning reliability analysis","authors":"Tong Zhou , Xujia Zhu , Tong Guo , You Dong , Michael Beer","doi":"10.1016/j.strusafe.2024.102557","DOIUrl":"10.1016/j.strusafe.2024.102557","url":null,"abstract":"<div><div>This manuscript presents a novel Bayesian active learning reliability method integrating both Bayesian failure probability estimation and Bayesian decision-theoretic multi-point enrichment process. First, an epistemic uncertainty measure called integrated margin probability (IMP) is proposed as an upper bound for the mean absolute deviation of failure probability estimated by Kriging. Then, adhering to the Bayesian decision theory, a look-ahead learning function called multi-point stepwise margin reduction (MSMR) is defined to quantify the possible reduction of IMP brought by adding a batch of new samples in expectation. The cost-effective implementation of MSMR-based multi-point enrichment process is conducted by three key workarounds: (a) Thanks to analytical tractability of the inner integral, the MSMR reduces to a single integral. (b) The remaining single integral in the MSMR is numerically computed with the rational truncation of the quadrature set. (c) A heuristic treatment of maximizing the MSMR is devised to fastly select a batch of best next points per iteration, where the prescribed scheme or adaptive scheme is used to specify the batch size. The proposed method is tested on two benchmark examples and two dynamic reliability problems. The results indicate that the adaptive scheme in the MSMR gains a good balance between the computing resource consumption and the overall computational time. Then, the MSMR fairly outperforms those existing leaning functions and parallelization strategies in terms of the accuracy of failure probability estimate, the number of iterations, as well as the number of performance function evaluations, especially in complex dynamic reliability problems.</div></div>","PeriodicalId":21978,"journal":{"name":"Structural Safety","volume":"114 ","pages":"Article 102557"},"PeriodicalIF":5.7,"publicationDate":"2024-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143151098","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

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

IF 5.7 1区工程技术

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

Uichan Seok , Ji-Eun Byun , Junho Song

{"title":"Disaster risk-informed optimization using buffered failure probability for regional-scale building retrofit strategy","authors":"Uichan Seok , Ji-Eun Byun , Junho Song","doi":"10.1016/j.strusafe.2024.102556","DOIUrl":"10.1016/j.strusafe.2024.102556","url":null,"abstract":"<div><div>Regional retrofit planning of buildings is critical to address the increasing threat of natural disasters exacerbated by urban growth and climate change. To identify an optimal plan, this paper introduces a novel optimization framework. By integrating performance-based engineering (PBE) and reliability-based optimization (RBO), we propose buffered optimization and reliability method based mixed integer linear programming (BORM-MILP). The proposed formulation can identify optimal solutions using general optimization solvers, while handling a large number of PBE samples and buildings. Furthermore, the formulation introduces a modified active-set strategy tailored to regional-scale building retrofit optimization problems, further reducing computational memory. The proposed optimization framework is validated by a benchmark example of Seaside, Oregon. The optimization results are presented along in a map, offering visual support for decision-making processes. The application results are further investigated to analyze computational efficiency of the proposed active-set strategy, study convergence to the normal distribution, and identify a dominant factor for the building retrofit selection.</div></div>","PeriodicalId":21978,"journal":{"name":"Structural Safety","volume":"114 ","pages":"Article 102556"},"PeriodicalIF":5.7,"publicationDate":"2024-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143151099","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Reliability analysis for data-driven noisy models using active learning 基于主动学习的数据驱动噪声模型可靠性分析

IF 5.7 1区工程技术

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

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

{"title":"Reliability analysis for data-driven noisy models using active learning","authors":"Anderson V. Pires, Maliki Moustapha, Stefano Marelli, Bruno Sudret","doi":"10.1016/j.strusafe.2024.102543","DOIUrl":"10.1016/j.strusafe.2024.102543","url":null,"abstract":"<div><div>Reliability analysis aims at estimating the failure probability of an engineering system. It often requires multiple runs of a limit-state function, which usually relies on computationally intensive simulations. Traditionally, these simulations have been considered deterministic, <em>i.e.</em> running them multiple times for a given set of input parameters always produces the same output. However, this assumption does not always hold, as many studies in the literature report non-deterministic computational simulations (also known as noisy models). In such cases, running the simulations multiple times with the same input will result in different outputs. Similarly, data-driven models that rely on real-world data may also be affected by noise. This characteristic poses a challenge when performing reliability analysis, as many classical methods, such as FORM and SORM, are tailored to deterministic models. To bridge this gap, this paper provides a novel methodology to perform reliability analysis on models contaminated by noise. In such cases, noise introduces latent uncertainty into the reliability estimator, leading to an incorrect estimation of the real underlying reliability index, even when using Monte Carlo simulation. To overcome this challenge, we propose the use of denoising regression-based surrogate models within an active learning reliability analysis framework. Specifically, we combine Gaussian process regression with a noise-aware learning function to efficiently estimate the probability of failure of the underlying noise-free model. We showcase the effectiveness of this methodology on standard benchmark functions and a finite element model of a realistic structural frame.</div></div>","PeriodicalId":21978,"journal":{"name":"Structural Safety","volume":"112 ","pages":"Article 102543"},"PeriodicalIF":5.7,"publicationDate":"2024-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142748044","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

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

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

Futai Zhang , Jun Xu , Zhiqiang Wan

{"title":"An Adaptive Gaussian Mixture Model for structural reliability analysis using convolution search technique","authors":"Futai Zhang , Jun Xu , Zhiqiang Wan","doi":"10.1016/j.strusafe.2024.102548","DOIUrl":"10.1016/j.strusafe.2024.102548","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":21978,"journal":{"name":"Structural Safety","volume":"112 ","pages":"Article 102548"},"PeriodicalIF":5.7,"publicationDate":"2024-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142719985","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

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

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

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

{"title":"The generalized first-passage probability considering temporal correlation and its application in dynamic reliability analysis","authors":"Xian-Lin Yang , Ming-Ming Jia , Da-Gang Lu","doi":"10.1016/j.strusafe.2024.102547","DOIUrl":"10.1016/j.strusafe.2024.102547","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":21978,"journal":{"name":"Structural Safety","volume":"112 ","pages":"Article 102547"},"PeriodicalIF":5.7,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142748043","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 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

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

Yang Zhang , Jun Xu , Enrico Zio

{"title":"A novel deterministic sampling approach for the reliability analysis of high-dimensional structures","authors":"Yang Zhang , Jun Xu , Enrico Zio","doi":"10.1016/j.strusafe.2024.102545","DOIUrl":"10.1016/j.strusafe.2024.102545","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":21978,"journal":{"name":"Structural Safety","volume":"112 ","pages":"Article 102545"},"PeriodicalIF":5.7,"publicationDate":"2024-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142700580","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