Probabilistic Engineering Mechanics最新文献

英文中文

Robust-based design optimization of powertrain mounting system based on full vehicle model involving parametric uncertainty and correlation

IF 3 3区工程技术 Q2 ENGINEERING, MECHANICAL

Probabilistic Engineering Mechanics

Pub Date : 2025-01-01 DOI: 10.1016/j.probengmech.2024.103726

Hui Lü , Jiaming Zhang , Xiaoting Huang , Wen-Bin Shangguan

In engineering practice, the parametric uncertainty and correlation may coexist in the powertrain mounting system (PMS). An effective robust-based design optimization approach is proposed for uncertain PMS based on full vehicle model, where both the parametric uncertainty and correlation are considered. The uncertain parameters of PMS are firstly treated as probabilistic variables, and the Unscented Transformation Inspired (UTI) transformation is introduced to quantify the correlation of uncertain parameters. Then, to perform the uncertainty and correlation analysis, the UTI-Monte Carlo (UMC) method is developed based on UTI transformation and Monte Carlo sampling to estimate the means, standard deviations, variation ranges and correlation coefficients of PMS responses. Meanwhile, an efficient method named UTI-Arbitrary Polynomial Chaos Expansion (UAPCE) method is derived for the uncertainty and correlation analysis of PMS responses by combining UTI transformation and arbitrary polynomial chaos expansion. Next, an optimization model considering parametric uncertainty and correlation is formulated to perform the robust-based design of PMS, in which the weight coefficients of optimization components are calculated by principal component analysis. Finally, the numerical example is investigated to verify the effectiveness of the proposed methods.

{"title":"Robust-based design optimization of powertrain mounting system based on full vehicle model involving parametric uncertainty and correlation","authors":"Hui Lü , Jiaming Zhang , Xiaoting Huang , Wen-Bin Shangguan","doi":"10.1016/j.probengmech.2024.103726","DOIUrl":"10.1016/j.probengmech.2024.103726","url":null,"abstract":"<div><div>In engineering practice, the parametric uncertainty and correlation may coexist in the powertrain mounting system (PMS). An effective robust-based design optimization approach is proposed for uncertain PMS based on full vehicle model, where both the parametric uncertainty and correlation are considered. The uncertain parameters of PMS are firstly treated as probabilistic variables, and the Unscented Transformation Inspired (UTI) transformation is introduced to quantify the correlation of uncertain parameters. Then, to perform the uncertainty and correlation analysis, the UTI-Monte Carlo (UMC) method is developed based on UTI transformation and Monte Carlo sampling to estimate the means, standard deviations, variation ranges and correlation coefficients of PMS responses. Meanwhile, an efficient method named UTI-Arbitrary Polynomial Chaos Expansion (UAPCE) method is derived for the uncertainty and correlation analysis of PMS responses by combining UTI transformation and arbitrary polynomial chaos expansion. Next, an optimization model considering parametric uncertainty and correlation is formulated to perform the robust-based design of PMS, in which the weight coefficients of optimization components are calculated by principal component analysis. Finally, the numerical example is investigated to verify the effectiveness of the proposed methods.</div></div>","PeriodicalId":54583,"journal":{"name":"Probabilistic Engineering Mechanics","volume":"79 ","pages":"Article 103726"},"PeriodicalIF":3.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143147062","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

Stress–deformation analysis of concrete anti-seepage structure in earth-rock dam on overburden considering spatial variability of geomechanical parameter

IF 3 3区工程技术 Q2 ENGINEERING, MECHANICAL

Probabilistic Engineering Mechanics

Pub Date : 2025-01-01 DOI: 10.1016/j.probengmech.2024.103725

Xiang Yu , Zhuxin Li , Yuke Wang , Rui Pang , Xiaolong Lv , Meng Fu

Dams are inevitably built on a deep overburden constrained by site conditions. Moreover, the spatial variability of the geomechanical parameter in overburden tends to significantly affect the mechanical state of the dam foundation anti-seepage structure. In this study, random field theory was combined with finite element analysis to consider the spatial variability of geotechnical parameters in the overburden. Gaussian autocorrelation function and spectral representation were used in random field simulations followed by stochastic finite element calculation. Subsequently, the deformation modulus in Duncan-Chang E-B model was selected as a random parameter in combination with an engineering example. The tensile stress at the top of the anti-seepage structure, horizontal displacement at the top of the cutoff wall, and compressive stress of the cutoff wall were analyzed using statistical laws and the probability distribution tests of the mean value, maximum value, exceedance probability, and 95% confidence interval limit. The results show that when the spatial variability of geomechanical parameter in overburden is not considered, the stress and deformation of the anti-seepage structure are underestimated. Probability distribution statistics of the anti-seepage structure were different from those of geomechanical parameters. The horizontal displacement at the top of the cutoff wall demonstrated a stronger sensitivity to the coefficient of variation than to correlation distance. Therefore, numerical simulations considering the spatial variability of geomechanical parameter in overburden can reasonably reflect the stress and deformation of anti-seepage structure.

{"title":"Stress–deformation analysis of concrete anti-seepage structure in earth-rock dam on overburden considering spatial variability of geomechanical parameter","authors":"Xiang Yu , Zhuxin Li , Yuke Wang , Rui Pang , Xiaolong Lv , Meng Fu","doi":"10.1016/j.probengmech.2024.103725","DOIUrl":"10.1016/j.probengmech.2024.103725","url":null,"abstract":"<div><div>Dams are inevitably built on a deep overburden constrained by site conditions. Moreover, the spatial variability of the geomechanical parameter in overburden tends to significantly affect the mechanical state of the dam foundation anti-seepage structure. In this study, random field theory was combined with finite element analysis to consider the spatial variability of geotechnical parameters in the overburden. Gaussian autocorrelation function and spectral representation were used in random field simulations followed by stochastic finite element calculation. Subsequently, the deformation modulus in Duncan-Chang E-B model was selected as a random parameter in combination with an engineering example. The tensile stress at the top of the anti-seepage structure, horizontal displacement at the top of the cutoff wall, and compressive stress of the cutoff wall were analyzed using statistical laws and the probability distribution tests of the mean value, maximum value, exceedance probability, and 95% confidence interval limit. The results show that when the spatial variability of geomechanical parameter in overburden is not considered, the stress and deformation of the anti-seepage structure are underestimated. Probability distribution statistics of the anti-seepage structure were different from those of geomechanical parameters. The horizontal displacement at the top of the cutoff wall demonstrated a stronger sensitivity to the coefficient of variation than to correlation distance. Therefore, numerical simulations considering the spatial variability of geomechanical parameter in overburden can reasonably reflect the stress and deformation of anti-seepage structure.</div></div>","PeriodicalId":54583,"journal":{"name":"Probabilistic Engineering Mechanics","volume":"79 ","pages":"Article 103725"},"PeriodicalIF":3.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143147079","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

Stochastic dynamic response analysis via dimension-reduced probability density evolution equation (DR-PDEE) with enhanced tail-accuracy

IF 3 3区工程技术 Q2 ENGINEERING, MECHANICAL

Probabilistic Engineering Mechanics

Pub Date : 2025-01-01 DOI: 10.1016/j.probengmech.2025.103735

Yi Luo , Chao Dang , Matteo Broggi , Michael Beer

The stochastic dynamic analysis of high-dimensional nonlinear systems is a critical concern in engineering fields, especially when considering the reliability analysis of low-probability events. To address this challenge, the dimension-reduced probability density evolution equation (DR-PDEE) method has recently emerged as a promising tool. The DR-PDEE is the analytical governing equation for the probability density function (PDF) evolution of any path-continuous stochastic process. For a single response quantity of interest in a multi-dimensional nonlinear dynamic system, the corresponding DR-PDEE is merely a one- or two-dimensional partial differential equation. After estimating the intrinsic drift coefficient (IDC) in the DR-PDEE from sample data, this equation can be easily solved with rather high accuracy. However, if only a limited number of deterministic analyses are affordable, there is usually no sample information for the tail estimation of the IDC, resulting in an inaccurate PDF solution in the tail. In this work, a scheme is tailored for the DR-PDEE to further enhance its tail accuracy. Specifically, to increase the occurrence probability of tail samples, an additional set of samples is obtained by simply magnifying the excitation intensity of the system. Then, at each time step, samples in the response tail from this additional set are identified. By merging these samples with samples from the original system, a better IDC estimation in the tail is achieved. Several numerical examples are investigated to validate the effectiveness of the proposed DR-PDEE method. Comparisons with MCS and the classical DR-PDEE method show that the proposed scheme improves the accuracy and robustness of the PDF results in the tail.

{"title":"Stochastic dynamic response analysis via dimension-reduced probability density evolution equation (DR-PDEE) with enhanced tail-accuracy","authors":"Yi Luo , Chao Dang , Matteo Broggi , Michael Beer","doi":"10.1016/j.probengmech.2025.103735","DOIUrl":"10.1016/j.probengmech.2025.103735","url":null,"abstract":"<div><div>The stochastic dynamic analysis of high-dimensional nonlinear systems is a critical concern in engineering fields, especially when considering the reliability analysis of low-probability events. To address this challenge, the dimension-reduced probability density evolution equation (DR-PDEE) method has recently emerged as a promising tool. The DR-PDEE is the analytical governing equation for the probability density function (PDF) evolution of any path-continuous stochastic process. For a single response quantity of interest in a multi-dimensional nonlinear dynamic system, the corresponding DR-PDEE is merely a one- or two-dimensional partial differential equation. After estimating the intrinsic drift coefficient (IDC) in the DR-PDEE from sample data, this equation can be easily solved with rather high accuracy. However, if only a limited number of deterministic analyses are affordable, there is usually no sample information for the tail estimation of the IDC, resulting in an inaccurate PDF solution in the tail. In this work, a scheme is tailored for the DR-PDEE to further enhance its tail accuracy. Specifically, to increase the occurrence probability of tail samples, an additional set of samples is obtained by simply magnifying the excitation intensity of the system. Then, at each time step, samples in the response tail from this additional set are identified. By merging these samples with samples from the original system, a better IDC estimation in the tail is achieved. Several numerical examples are investigated to validate the effectiveness of the proposed DR-PDEE method. Comparisons with MCS and the classical DR-PDEE method show that the proposed scheme improves the accuracy and robustness of the PDF results in the tail.</div></div>","PeriodicalId":54583,"journal":{"name":"Probabilistic Engineering Mechanics","volume":"79 ","pages":"Article 103735"},"PeriodicalIF":3.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143147053","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

Translation models and extremes

IF 3 3区工程技术 Q2 ENGINEERING, MECHANICAL

Probabilistic Engineering Mechanics

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

M. Grigoriu

Translation models are constructed for non-Gaussian random vectors, time series and continuous time processes. They are memoryless, monotonically increasing transformations of corresponding Gaussian elements. It is shown that, generally, extremes of target non-Gaussian elements cannot be approximated by those of their translation models. This limitation has two sources. First, translation models cannot characterize accurately uncorrelated but dependent random variables. Second, extremes of correlated Gaussian variables are asymptotically independent and so are the extremes of the translation models constructed on these variables. Examples are presented to illustrate these limitations of translation models.

引用次数: 0

A two stage Kriging approach for Bayesian optimal experimental design

IF 3 3区工程技术 Q2 ENGINEERING, MECHANICAL

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

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.

{"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 Q2 ENGINEERING, MECHANICAL

Probabilistic Engineering Mechanics

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

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

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.

{"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 Q2 ENGINEERING, MECHANICAL

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

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.

{"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 Q2 ENGINEERING, MECHANICAL

Probabilistic Engineering Mechanics

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

Shaojuan Ma , Yuan Liu , Xiaoyan Ma , Yantong Liu

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.

随机共振在探测微弱信号方面得到了广泛的研究。为了提高对微弱信号的诊断能力，本文研究了一种新型非线性耦合非对称随机共振（NCASR）系统。首先，通过将非对称双稳态系统与单稳态系统耦合，建立了 NCASR 系统。接着，根据绝热近似理论推导出了所提系统的稳态概率密度（SPD）函数、平均首次通过时间（MFPT）和信噪比（SNR）的表达式。此外，还分析了系统参数对 SPD、MFPT 和信噪比的影响。然后，通过模拟实验，我们验证了具有 Lévy 噪声的 NCASR 系统检测微弱信号的有效性。最后，应用自适应加权粒子群优化算法（AWPSO）优化的 NCASR 系统检测轴承故障信号。与优化后的经典双稳态随机共振（CBSR）系统相比，发现 NCASR 系统在检测轴承故障信号方面的性能优于 CBSR 系统。

{"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 Q2 ENGINEERING, MECHANICAL

Probabilistic Engineering Mechanics

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

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

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.

针对风湍流随机激励模型开发了一种分数阶滤波器近似方法。具体来说，未知滤波器参数是通过最小化原始功率谱密度和近似功率谱密度之间的频域误差来确定的。结果表明，与标准整数阶滤波器的极限情况相比，在需要优化的参数数量相同的情况下，所确定的带有有理阶导数元素的分数阶滤波器能获得更高的精度。此外，所开发的滤波器近似方法可以分析计算线性结构系统的静态响应矩，而计算成本几乎为零。这是通过对滤波器状态变量方程进行复杂模态分析处理，并依靠柯西残差定理对相关随机振动积分进行分析评估实现的。与基于蒙特卡罗模拟数据的估算结果进行比较后发现，其精确度相当高。

{"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 Q2 ENGINEERING, MECHANICAL

Probabilistic Engineering Mechanics

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

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

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.

地震荷载下的结构可靠性分析是一项重大的工程挑战。这是因为需要进行非线性动态分析，且涉及大量数值计算；当分析目标为较小的失效概率时，计算负担会很大；合成地震模型表示可能包含数千个随机变量。子集模拟是一种高效的可靠性分析技术，与粗略的蒙特卡罗模拟相比，它可以通过减少结构分析调用次数来应对高维空间的挑战。在本文中，我们首先研究了子集模拟高效执行的条件。然后，我们对现有的子集模拟方案提出了一个改进方案，该方案在子集模拟中每达到一个新水平时，都能显示出改进马尔可夫链蒙特卡罗模拟启动策略的巨大潜力。最后，我们对从模拟中收集到的信息进行了研究，以验证子集模拟从物理角度提供了有意义的结果。

{"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

首页上一页

下一页尾页

类型

全部化学•材料生命科学医学物理工程技术环境•农林材料科学地球科学法学管理学化学环境科学与生态学计算机科学教育学经济学农林科学人文科学生物学数学物理与天体物理心理学综合性期刊其他工业工程理学历史学农学文学信息工程

数据库

全部 ACS Publications Elsevier ieeexplore Springer The Royal Society of Chemistry Wiley

期刊

Probabilistic Engineering Mechanics

全部 Acc. Chem. Res. ACS Applied Bio Materials ACS Appl. Electron. Mater. ACS Appl. Energy Mater. ACS Appl. Mater. Interfaces ACS Appl. Nano Mater. ACS Appl. Polym. Mater. ACS BIOMATER-SCI ENG ACS Catal. ACS Cent. Sci. ACS Chem. Biol. ACS Chemical Health & Safety ACS Chem. Neurosci. ACS Comb. Sci. ACS Earth Space Chem. ACS Energy Lett. ACS Infect. Dis. ACS Macro Lett. ACS Mater. Lett. ACS Med. Chem. Lett. ACS Nano ACS Omega ACS Photonics ACS Sens. ACS Sustainable Chem. Eng. ACS Synth. Biol. Anal. Chem. BIOCHEMISTRY-US Bioconjugate Chem. BIOMACROMOLECULES Chem. Res. Toxicol. Chem. Rev. Chem. Mater. CRYST GROWTH DES ENERG FUEL Environ. Sci. Technol. Environ. Sci. Technol. Lett. Eur. J. Inorg. Chem. IND ENG CHEM RES Inorg. Chem. J. Agric. Food. Chem. J. Chem. Eng. Data J. Chem. Educ. J. Chem. Inf. Model. J. Chem. Theory Comput. J. Med. Chem. J. Nat. Prod. J PROTEOME RES J. Am. Chem. Soc. LANGMUIR MACROMOLECULES Mol. Pharmaceutics Nano Lett. Org. Lett. ORG PROCESS RES DEV ORGANOMETALLICS J. Org. Chem. J. Phys. Chem. J. Phys. Chem. A J. Phys. Chem. B J. Phys. Chem. C J. Phys. Chem. Lett. Analyst Anal. Methods Biomater. Sci. Catal. Sci. Technol. Chem. Commun. Chem. Soc. Rev. CHEM EDUC RES PRACT CRYSTENGCOMM Dalton Trans. Energy Environ. Sci. ENVIRON SCI-NANO ENVIRON SCI-PROC IMP ENVIRON SCI-WAT RES Faraday Discuss. Food Funct. Green Chem. Inorg. Chem. Front. Integr. Biol. J. Anal. At. Spectrom. J. Mater. Chem. A J. Mater. Chem. B J. Mater. Chem. C Lab Chip Mater. Chem. Front. Mater. Horiz. MEDCHEMCOMM Metallomics Mol. Biosyst. Mol. Syst. Des. Eng. Nanoscale Nanoscale Horiz. Nat. Prod. Rep. New J. Chem. Org. Biomol. Chem. Org. Chem. Front. PHOTOCH PHOTOBIO SCI PCCP Polym. Chem.

﹀