Probabilistic Engineering Mechanics最新文献_第4页

Translation models and extremes

IF 3 3区工程技术

Probabilistic Engineering Mechanics Pub Date : 2025-01-01 DOI: 10.1016/j.probengmech.2025.103738

M. Grigoriu

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A two stage Kriging approach for Bayesian optimal experimental design

IF 3 3区工程技术

Probabilistic Engineering Mechanics Pub Date : 2025-01-01 DOI: 10.1016/j.probengmech.2024.103724

Cibelle Dias de Carvalho Dantas Maia , Rafael Holdorf Lopez , André Jacomel Torii , Leandro Fleck Fadel Miguel

{"title":"A two stage Kriging approach for Bayesian optimal experimental design","authors":"Cibelle Dias de Carvalho Dantas Maia , Rafael Holdorf Lopez , André Jacomel Torii , Leandro Fleck Fadel Miguel","doi":"10.1016/j.probengmech.2024.103724","DOIUrl":"10.1016/j.probengmech.2024.103724","url":null,"abstract":"<div><div>This paper presents a two-stage Kriging framework designed to efficiently tackle Bayesian optimal experiment design (OED) problems. To enhance computational efficiency in evaluating the Shannon expected information gain (SEIG), we introduced a Kriging surrogate as a replacement for the original forward model (stage 1 Kriging). This surrogate is utilized within the Double Loop Monte Carlo method for SEIG estimation. We employed the Efficient Global Optimization (EGO) framework as the optimizer, which requires the construction of a Kriging surrogate of the SEIG (stage 2 Kriging). Within EGO, the expected improvement infill criterion was employed as the active learning metric. The underlying rationale of employing a two-stage Kriging approach is to alleviate the curse of dimensionality typically associated with Kriging surrogates. In this strategy, the first stage of Kriging is focused on surrogating the random parameter space, while the second stage is dedicated to modeling the design variable space. By adopting this two-stage approach, the need for constructing a global surrogate for the forward model in both spaces is circumvented. This segmentation allows for more efficient and accurate surrogate modeling, particularly in high-dimensional spaces, enhancing the overall computational performance of the optimization process. The method was applied to three OED problems. The results demonstrate that the proposed two-stage Kriging approach (EGO-KR) effectively addressed the analyzed problems, offering good precision and significant computational savings, particularly in the third and more complex example.</div></div>","PeriodicalId":54583,"journal":{"name":"Probabilistic Engineering Mechanics","volume":"79 ","pages":"Article 103724"},"PeriodicalIF":3.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143146061","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Optimizing site investigations for gassy soils: A Bi-objective approach using value of information and cost of boreholes

IF 3 3区工程技术

Probabilistic Engineering Mechanics Pub Date : 2025-01-01 DOI: 10.1016/j.probengmech.2024.103727

Shao-Lin Ding , Kai-Qi Li , Rui Tao

{"title":"Optimizing site investigations for gassy soils: A Bi-objective approach using value of information and cost of boreholes","authors":"Shao-Lin Ding , Kai-Qi Li , Rui Tao","doi":"10.1016/j.probengmech.2024.103727","DOIUrl":"10.1016/j.probengmech.2024.103727","url":null,"abstract":"<div><div>Gassy soils, containing flammable gases like methane (CH₄), are commonly found in the shallow layers of Quaternary deposits, posing significant challenges for underground construction. Effective site investigation, particularly the strategic placement of boreholes for gas pressure measurement, is critical for assessing engineering risks. However, the high costs of borehole drilling often limit the amount of available gas pressure data, leading to potential errors in risk assessments at unmeasured locations. Misclassifying hazardous conditions as safe can result in costly penalties. Currently, investigation strategies that balance cost reduction with risk mitigation rely largely on engineering judgment. This study presents a probabilistic optimization approach for planning site investigations in gassy soils, explicitly addressing the trade-off between investigation costs and misclassification penalties. These factors are quantified using Value of Information (VoI) and cost of boreholes (CoB). The optimal investigation strategy is determined through the knee point method, which identifies the best compromise between VoI and CoB. A case study on Hangzhou Metro Line 1 demonstrates the practicality and effectiveness of this approach, showing that the optimal strategy balances VoI maximization with CoB minimization. The knee point method effectively identifies this compromise, ensuring maximum marginal utility by balancing information value and investigation cost.</div></div>","PeriodicalId":54583,"journal":{"name":"Probabilistic Engineering Mechanics","volume":"79 ","pages":"Article 103727"},"PeriodicalIF":3.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143147060","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Efficient metamodeling and uncertainty propagation for rotor systems by sparse polynomial chaos expansion

IF 3 3区工程技术

Probabilistic Engineering Mechanics Pub Date : 2025-01-01 DOI: 10.1016/j.probengmech.2024.103723

Ben-Sheng Xu , Xiao-Min Yang , Ai-Cheng Zou , Chao-Ping Zang

{"title":"Efficient metamodeling and uncertainty propagation for rotor systems by sparse polynomial chaos expansion","authors":"Ben-Sheng Xu , Xiao-Min Yang , Ai-Cheng Zou , Chao-Ping Zang","doi":"10.1016/j.probengmech.2024.103723","DOIUrl":"10.1016/j.probengmech.2024.103723","url":null,"abstract":"<div><div>The modeling of rotor systems involves various parameters prone to uncertainties. These variations typically arise from the mathematical complexities of representing rotor system peculiarities and the limited understanding of material properties in specific applications. Analyzing uncertainties affecting rotor system performance is essential for effective design. A metamodeling approach for rotor systems under uncertain parameters is developed, employing sparse polynomial chaos expansion (sPCE) for uncertainty propagation. The sPCE method integrates basis functions adaptively using the Bayesian compressive sensing (BCS) method, enhancing convergence speed for accurate prediction of statistical moments. Probabilistic outcomes are compared with traditional Monte Carlo simulation (MCS) and Latin Hyper Sampling (LHS) methods. The comparative analysis shows that the proposed method achieves higher computational accuracy than the LHS method and exhibits a 40% improvement in computational efficiency compared to the traditional MCS method, thus providing valuable insights for the design and maintenance of rotor systems.</div></div>","PeriodicalId":54583,"journal":{"name":"Probabilistic Engineering Mechanics","volume":"79 ","pages":"Article 103723"},"PeriodicalIF":3.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143147061","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Nonlinear coupled asymmetric stochastic resonance for weak signal detection based on intelligent algorithm optimization 基于智能算法优化的用于微弱信号检测的非线性耦合非对称随机共振

IF 3 3区工程技术

Probabilistic Engineering Mechanics Pub Date : 2024-10-01 DOI: 10.1016/j.probengmech.2024.103697

Shaojuan Ma , Yuan Liu , Xiaoyan Ma , Yantong Liu

{"title":"Nonlinear coupled asymmetric stochastic resonance for weak signal detection based on intelligent algorithm optimization","authors":"Shaojuan Ma , Yuan Liu , Xiaoyan Ma , Yantong Liu","doi":"10.1016/j.probengmech.2024.103697","DOIUrl":"10.1016/j.probengmech.2024.103697","url":null,"abstract":"<div><div>Stochastic resonance has been extensively studied for detecting weak signals. To improve the diagnostic ability of weak signals, a novel nonlinear coupled asymmetric stochastic resonance (NCASR) system is investigated in this paper. Firstly, the NCASR system is established by coupling the asymmetric bistable system with the monostable system. Next, the expressions for the steady-state probability density (SPD) function, the mean first passage time (MFPT) and the signal-to-noise ratio (SNR) of the proposed system are derived based on the adiabatic approximation theory. Furthermore, the impact of system parameters on the SPD, the MFPT and the SNR is analyzed. Then, by simulation experiments, we verify the effectiveness of detecting weak signals for the NCASR system with Lévy noise. Finally, the NCASR system optimized by Adaptive Weighted Particle Swarm Optimization (AWPSO) algorithm is applied to detect the bearing fault signal. Compared with the optimized classical bistable stochastic resonance (CBSR) system, it is found that the detection performance of the NCASR system is superior to the CBSR system in detecting bearing fault signals.</div></div>","PeriodicalId":54583,"journal":{"name":"Probabilistic Engineering Mechanics","volume":"78 ","pages":"Article 103697"},"PeriodicalIF":3.0,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142663358","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Fractional-order filter approximations for efficient stochastic response determination of wind-excited linear structural systems 用于风激线性结构系统高效随机响应确定的分数阶滤波器近似值

IF 3 3区工程技术

Probabilistic Engineering Mechanics Pub Date : 2024-10-01 DOI: 10.1016/j.probengmech.2024.103696

Luca Roncallo , Ilias Mavromatis , Ioannis A. Kougioumtzoglou , Federica Tubino

{"title":"Fractional-order filter approximations for efficient stochastic response determination of wind-excited linear structural systems","authors":"Luca Roncallo , Ilias Mavromatis , Ioannis A. Kougioumtzoglou , Federica Tubino","doi":"10.1016/j.probengmech.2024.103696","DOIUrl":"10.1016/j.probengmech.2024.103696","url":null,"abstract":"<div><div>A fractional-order filter approximation is developed for a wind turbulence stochastic excitation model. Specifically, the unknown filter parameters are determined by minimizing the error in the frequency domain between the original and the approximate power spectral densities. It is shown that compared to the limiting case of a standard integer-order filter, and for the same number of parameters to be optimized, the determined fractional-order filter with derivative elements of rational order yields enhanced accuracy. Further, the developed filter approximation enables the analytical calculation of stationary response moments of linear structural systems at practically zero computational cost. This is done by employing a complex modal analysis treatment of the filter state-variable equations, and by relying on Cauchy's residue theorem for evaluating analytically the related random vibration integrals. Comparisons with estimates based on Monte Carlo simulation data demonstrate a quite high degree of accuracy.</div></div>","PeriodicalId":54583,"journal":{"name":"Probabilistic Engineering Mechanics","volume":"78 ","pages":"Article 103696"},"PeriodicalIF":3.0,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142421000","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Seismic reliability analysis using Subset Simulation enhanced with an explorative adaptive conditional sampling algorithm 利用探索性自适应条件采样算法增强子集模拟进行地震可靠性分析

IF 3 3区工程技术

Probabilistic Engineering Mechanics Pub Date : 2024-10-01 DOI: 10.1016/j.probengmech.2024.103690

Juan G. Sepúlveda , Sebastian T. Glavind , Michael H. Faber

{"title":"Seismic reliability analysis using Subset Simulation enhanced with an explorative adaptive conditional sampling algorithm","authors":"Juan G. Sepúlveda , Sebastian T. Glavind , Michael H. Faber","doi":"10.1016/j.probengmech.2024.103690","DOIUrl":"10.1016/j.probengmech.2024.103690","url":null,"abstract":"<div><div>Reliability analysis of structures under earthquake loading represents a significant engineering challenge. This is due to the required and rather numerically involving non-linear dynamic analysis, the large computational burden when targeting small failure probabilities, and the synthetic earthquake model representation that may contain thousands of random variables. Subset Simulation is an efficient reliability analysis technique that can handle the challenge of a high-dimensional space with a reduced number of structural analysis calls compared to crude Monte Carlo Simulation. In this contribution, firstly, we investigate the conditions for which Subset Simulation performs efficiently. Thereafter we propose an enhancement to the existing Subset Simulation schemes that shows significant potentials for enhancing the strategy for the starting of the Markov Chain Monte Carlo simulations whenever a new level is reached in the Subset Simulation. Finally, the information gathered from the simulations is investigated to verify that Subset Simulation provides meaningful results from a physical point of view.</div></div>","PeriodicalId":54583,"journal":{"name":"Probabilistic Engineering Mechanics","volume":"78 ","pages":"Article 103690"},"PeriodicalIF":3.0,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142421001","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Efficient optimization-based method for simultaneous calibration of load and resistance factors considering multiple target reliability indices 基于优化的高效方法，可同时校准考虑多个目标可靠性指数的载荷系数和阻力系数

IF 3 3区工程技术

Probabilistic Engineering Mechanics Pub Date : 2024-10-01 DOI: 10.1016/j.probengmech.2024.103695

Nhu Son Doan , Van Ha Mac , Huu-Ba Dinh

{"title":"Efficient optimization-based method for simultaneous calibration of load and resistance factors considering multiple target reliability indices","authors":"Nhu Son Doan , Van Ha Mac , Huu-Ba Dinh","doi":"10.1016/j.probengmech.2024.103695","DOIUrl":"10.1016/j.probengmech.2024.103695","url":null,"abstract":"<div><div>This study introduces an innovative optimization process for calibrating probabilistic load and resistance factors (LRFs) in limit state designs, effectively accommodating multiple target reliability indices. Given the impracticality of direct Monte Carlo simulations (MCS) for this task, a response surface method (RSM) is proposed to approximate load and resistance components separately rather than fitting conventional safety factors. This approach eliminates the need for additional implicit evaluations, thereby improving the efficiency of LRF calibration across multiple targets. The process is further enhanced by an adaptive boundary algorithm that updates search domains in real-time, streamlining the optimization. Validation through three examples—including one explicit and two implicit performance functions (a structural and a geotechnical example)—demonstrates that the method achieves accurate results with fewer iterations by dynamically narrowing search domains. Specifically, the accuracy of the proposed method is confirmed by comparing results with those from the literature for the explicit example and with basic MCS results applied to the initial implicit problems. Performance on the illustrative examples shows that the structural example achieves calibration for three targets within ten iterations. Additionally, this method eliminates the need for approximately ten thousand implicit evaluations when calculating limit state points for the geotechnical example.</div></div>","PeriodicalId":54583,"journal":{"name":"Probabilistic Engineering Mechanics","volume":"78 ","pages":"Article 103695"},"PeriodicalIF":3.0,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142421053","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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Nonprobabilistic time-dependent reliability analysis for uncertain structures under interval process loads 区间过程载荷下不确定结构的非概率时间相关可靠性分析

IF 3 3区工程技术

Probabilistic Engineering Mechanics Pub Date : 2024-10-01 DOI: 10.1016/j.probengmech.2024.103687

Jinglei Gong , Xiaojun Wang , Tangqi Lv , Junliu Yang , Linhui Zhou

{"title":"Nonprobabilistic time-dependent reliability analysis for uncertain structures under interval process loads","authors":"Jinglei Gong , Xiaojun Wang , Tangqi Lv , Junliu Yang , Linhui Zhou","doi":"10.1016/j.probengmech.2024.103687","DOIUrl":"10.1016/j.probengmech.2024.103687","url":null,"abstract":"<div><div>In this paper, a novel nonprobabilistic analysis framework is proposed to evaluate the time-dependent reliability of uncertain structures under time-varying loads. Firstly, a novel uncertainty propagation method is developed by combining interval process integration and surrogate-based interval analysis and the correlation coefficient between responses of adjacent time steps is further analyzed. Subsequently, the nonprobabilistic time-dependent reliability is analyzed base on the first-passage theory and the established interval model. Unlike existing nonprobabilistic methods that consider time-invariant external loads, the proposed method applies an interval process to describe time-varying external loads, thereby offering a broader range of applicability. Compared to existing nonprobabilistic methods that consider time-varying loads, the proposed method establishes a more refined nonprobabilistic time-dependent reliability model based on the first passage theory, achieving higher accuracy. The effectiveness and superiority of the proposed method are validated through a numerical example and an engineering application.</div></div>","PeriodicalId":54583,"journal":{"name":"Probabilistic Engineering Mechanics","volume":"78 ","pages":"Article 103687"},"PeriodicalIF":3.0,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142420999","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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Special issue: Fractional calculus & stochastic dynamics

IF 3 3区工程技术

Probabilistic Engineering Mechanics Pub Date : 2024-10-01 DOI: 10.1016/j.probengmech.2024.103703

Antonina Pirrotta (Guest Editors), Mario Di Paola (Guest Editors), Massimiliano Zingales (Guest Editors)

引用次数: 0