Probabilistic Engineering Mechanics最新文献_第7页

Modal–based uncertainty quantification for deterministically estimated structural parameters in low-fidelity model updating of complex connections 复杂连接低保真模型更新中确定性估算结构参数的基于模态的不确定性量化

IF 3 3区工程技术

Probabilistic Engineering Mechanics Pub Date : 2024-07-01 DOI: 10.1016/j.probengmech.2024.103671

Milad Mehrkash, Erin Santini-Bell

{"title":"Modal–based uncertainty quantification for deterministically estimated structural parameters in low-fidelity model updating of complex connections","authors":"Milad Mehrkash, Erin Santini-Bell","doi":"10.1016/j.probengmech.2024.103671","DOIUrl":"10.1016/j.probengmech.2024.103671","url":null,"abstract":"<div><p>Modeling complex joints in structures entails significant time and effort, necessitating simplifications. Epistemic uncertainties arising from low-fidelity modeling can be quantified through probabilistic model updating. However, finding a surrogate physical model to represent simplified joint configurations poses challenges. Additionally, establishing a Bayesian formulation capable of incorporating structural parameters of connections is necessary. This study employs a validated simplifying parameterization approach for surrogate modeling of complex semi-rigid connections in a benchmark laboratory steel grid. It proposes a modal probabilistic Bayesian methodology to quantify uncertainties in the structure's joints. Three modal-based objective functions are utilized for finite element model updating. The modal properties of the structure are extracted by experimental modal analysis during an impact test, which will be utilized in the model updating process. Deterministic and probabilistic structural parameter estimations are integrated to enhance the robustness of the Bayesian technique. Furthermore, a guideline for selecting optimal hyperparameters is provided. Results demonstrate that utilizing deterministically estimated parameters as prior knowledge can facilitate and improve modal probabilistic model updating for structures with complex joints. Also, it is found that despite significant simplifications of joints, structural parameter tolerance around the maximum a posteriori estimate in surrogate models remains low.</p></div>","PeriodicalId":54583,"journal":{"name":"Probabilistic Engineering Mechanics","volume":"77 ","pages":"Article 103671"},"PeriodicalIF":3.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141934396","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

An improved interval prediction method for recurrence period wind speed 重现期风速的改进区间预测法

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Probabilistic Engineering Mechanics Pub Date : 2024-07-01 DOI: 10.1016/j.probengmech.2024.103675

Weihu Chen , Yuji Tian , Yiyi Tian , Haiwei Guan

{"title":"An improved interval prediction method for recurrence period wind speed","authors":"Weihu Chen , Yuji Tian , Yiyi Tian , Haiwei Guan","doi":"10.1016/j.probengmech.2024.103675","DOIUrl":"10.1016/j.probengmech.2024.103675","url":null,"abstract":"<div><p>Based on the improved interval operation theory, an improved expression of the return period wind speed interval prediction is constructed by using an approximate first-order Taylor series expansion. According to the measured wind speed data in Beijing, Jinan, Nanjing, Wuxi, Shanghai and Shenzhen, the improved method and the traditional method are respectively used to predict the interval of the return period wind speed. Furthermore, the interval results predicted by the improved method and the traditional method are compared and analyzed under the same confidence level. Results show that the improved method has good applicability for different parameter estimation methods under the condition of certain extreme value distribution model, and the interval prediction results of the return period wind speed are basically stable. Compared with the interval results predicted by the traditional method, the interval predicted by the improved method is more likely to be close to or contain the exact solution of the return period wind speed, which has higher prediction accuracy. In addition, the calculation process of the improved method is relatively simple and can realize the simplified calculation of interval prediction.</p></div>","PeriodicalId":54583,"journal":{"name":"Probabilistic Engineering Mechanics","volume":"77 ","pages":"Article 103675"},"PeriodicalIF":3.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141979146","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

Statistical model calibration of correlated unknown model variables through identifiability improvement 通过可识别性改进对相关未知模型变量进行统计模型校准

IF 3 3区工程技术

Probabilistic Engineering Mechanics Pub Date : 2024-07-01 DOI: 10.1016/j.probengmech.2024.103670

Jeonghwan Choo , Yongsu Jung , Hwisang Jo , Ikjin Lee

{"title":"Statistical model calibration of correlated unknown model variables through identifiability improvement","authors":"Jeonghwan Choo , Yongsu Jung , Hwisang Jo , Ikjin Lee","doi":"10.1016/j.probengmech.2024.103670","DOIUrl":"10.1016/j.probengmech.2024.103670","url":null,"abstract":"<div><p>A statistical model calibration problem is known to have unstable or non-unique optimal solutions due to its ill-posed inverse nature, which is further complicated by limited test data availability due to time and cost constraints. To overcome these challenges and improve the identifiability of calibration parameters, this study proposes a novel statistical model calibration framework. The proposed method integrates input test data for unknown model variables and output test data for a system response, employing a bivariate form of copula function to model the probability distribution while accounting for the correlations between unknown model variables. Furthermore, a sample-averaged log-likelihood is used as a calibration metric, assuming conditional independence to reflect input and output test data evenly in a single metric. Optimization-based model calibration (OBMC) is performed to identify the probability models that maximize the calibration metric for a given set of input and output test data, among candidates of marginal probability distributions and copula functions. Consequently, this proposed method enhances the identifiability of calibration parameters and overcomes insufficient data issues by taking observations of unknown model variables into account in the statistical model calibration procedure. The proposed framework is validated using numerical examples.</p></div>","PeriodicalId":54583,"journal":{"name":"Probabilistic Engineering Mechanics","volume":"77 ","pages":"Article 103670"},"PeriodicalIF":3.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142058279","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

Inference on the high-reliability lifetime estimation based on the three-parameter Weibull distribution 基于三参数威布尔分布的高可靠性寿命估计推论

IF 3 3区工程技术

Probabilistic Engineering Mechanics Pub Date : 2024-07-01 DOI: 10.1016/j.probengmech.2024.103665

Xiaoyu Yang , Liyang Xie , Bowen Wang , Jianpeng Chen , Bingfeng Zhao

{"title":"Inference on the high-reliability lifetime estimation based on the three-parameter Weibull distribution","authors":"Xiaoyu Yang , Liyang Xie , Bowen Wang , Jianpeng Chen , Bingfeng Zhao","doi":"10.1016/j.probengmech.2024.103665","DOIUrl":"10.1016/j.probengmech.2024.103665","url":null,"abstract":"<div><p>The high-reliability lifetime estimation of the lifting lug is of significant importance, as it is the most crucial component of the aerial bomb. This paper focuses on the high-reliability lifetime of the three-parameter Weibull distribution for lifting lug fatigue data. A novel method is developed to generate estimates of reliability lifetime according to the generalized fiducial inference, whose prior is calculated by the failure data. A posterior distribution is obtained based on Bayesian theory to compute the point estimate and the confidence interval of the generalized fiducial inference for reliability lifetime using the Monte Carlo Markov chain method. Subsequently, it is compared with the non-informative prior Bayesian inference. A Monte Carlo simulation demonstrates that the proposed method outperforms the non-informative prior Bayesian inference. The lower confidence limit of the generalized fiducial inference for the reliability lifetime exhibis satisfactory coverage probabilities. Finally, fatigue tests are performed on 18 lifting lugs under variable loads. The point estimate and the lower confidence limit of the high-reliability lifetime are estimated, which can illustrate the applicability of the proposed method.</p></div>","PeriodicalId":54583,"journal":{"name":"Probabilistic Engineering Mechanics","volume":"77 ","pages":"Article 103665"},"PeriodicalIF":3.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141852559","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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Probability density of the solution to nonlinear systems driven by Gaussian and Poisson white noises 高斯和泊松白噪声驱动的非线性系统解的概率密度

IF 3 3区工程技术

Probabilistic Engineering Mechanics Pub Date : 2024-07-01 DOI: 10.1016/j.probengmech.2024.103658

Wantao Jia , Zhe Jiao , Wanrong Zan , Weiqiu Zhu

引用次数: 0

Nonstationary response statistics of structures with hysteretic damping to evolutionary stochastic excitation 具有滞后阻尼的结构对演化随机激励的非稳态响应统计

IF 3 3区工程技术

Probabilistic Engineering Mechanics Pub Date : 2024-07-01 DOI: 10.1016/j.probengmech.2024.103659

Qianying Cao , Sau-Lon James Hu , Huajun Li

{"title":"Nonstationary response statistics of structures with hysteretic damping to evolutionary stochastic excitation","authors":"Qianying Cao , Sau-Lon James Hu , Huajun Li","doi":"10.1016/j.probengmech.2024.103659","DOIUrl":"https://doi.org/10.1016/j.probengmech.2024.103659","url":null,"abstract":"<div><p>The damping of a structure has often been modeled as linear hysteretic damping (LHD), so its corresponding equation of motion (EOM) is an integro-differential equation that involves the Hilbert transform of response displacement. As a result, the system is non-causal in nature, and it is challenging to compute its nonstationary response statistics under evolutionary stochastic excitation. This article develops an efficient solution method to obtain closed-form solutions for various nonstationary response statistics, including the evolutionary power spectrum (EPS), correlation function and mean square values. The novel solution method utilizes the concept of causalization time to introduce a “causalized” impulse response function (IRF), by which causal response statistics are computed based on a pole-residue approach. This approach requires obtaining a pole-residue form of the transfer function (TF) from the frequency response function (FRF) of the system, which is readily obtained from the EOM. Subsequently, the desired response statistics are obtained by shifting the causal response statistics back to the original time. To obtain the pole-residue form of the TF, two steps are necessary: (1) taking the inverse Fourier transform of the FRF of the oscillator to obtain a discrete IRF and (2) using the Prony-SS method to decompose this discrete IRF to obtain the pole residues associated with the TF. The correctness of the proposed method is numerically verified by Monte Carlo simulations through examples of hysteretic damping and mixed viscous-hysteretic damping oscillators that are subjected to white noise, modulated white noise and modulated Kanai–Tajimi model random excitations.</p></div>","PeriodicalId":54583,"journal":{"name":"Probabilistic Engineering Mechanics","volume":"77 ","pages":"Article 103659"},"PeriodicalIF":3.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141541906","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

Multi-fidelity wavelet neural operator surrogate model for time-independent and time-dependent reliability analysis 用于与时间无关和与时间有关的可靠性分析的多保真小波神经算子代用模型

IF 3 3区工程技术

Probabilistic Engineering Mechanics Pub Date : 2024-07-01 DOI: 10.1016/j.probengmech.2024.103672

Tapas Tripura , Akshay Thakur , Souvik Chakraborty

{"title":"Multi-fidelity wavelet neural operator surrogate model for time-independent and time-dependent reliability analysis","authors":"Tapas Tripura , Akshay Thakur , Souvik Chakraborty","doi":"10.1016/j.probengmech.2024.103672","DOIUrl":"10.1016/j.probengmech.2024.103672","url":null,"abstract":"<div><p>Operator learning frameworks have recently emerged as an effective scientific machine learning tool for learning complex nonlinear operators of differential equations. Since neural operators learn an infinite-dimensional functional mapping, it is useful in applications requiring rapid prediction of solutions for a wide range of input functions. A task of a similar nature arises in many applications of uncertainty quantification, including reliability estimation and design under uncertainty, each of which demands thousands of samples subjected to a wide range of possible input conditions, an aspect to which neural operators are specialized. Although the neural operators are capable of learning complex nonlinear solution operators, they require an extensive amount of data for successful training. Unlike the applications in computer vision, the computational complexity of the numerical simulations and the cost of physical experiments contributing to the synthetic and real training data compromise the performance of the trained neural operator model, thereby directly impacting the accuracy of uncertainty quantification results. We aim to alleviate the data bottleneck by using multi-fidelity learning in neural operators, where a neural operator is trained by using a large amount of inexpensive low-fidelity data along with a small amount of expensive high-fidelity data. We propose the multi-fidelity wavelet neural operator, capable of learning solution operators from a multi-fidelity dataset, for efficient and effective data-driven reliability analysis of dynamical systems. We illustrate the performance of the proposed framework on bi-fidelity data simulated on coarse and refined grids for spatial and spatiotemporal systems.</p></div>","PeriodicalId":54583,"journal":{"name":"Probabilistic Engineering Mechanics","volume":"77 ","pages":"Article 103672"},"PeriodicalIF":3.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141934397","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

Description of the spatial variability of concrete via composite random field and failure analysis of chimney 通过复合随机场和烟囱失效分析描述混凝土的空间变异性

IF 3 3区工程技术

Probabilistic Engineering Mechanics Pub Date : 2024-07-01 DOI: 10.1016/j.probengmech.2024.103677

Jinju Tao , Jingran He , Beibei Xiong , Yupeng Song

{"title":"Description of the spatial variability of concrete via composite random field and failure analysis of chimney","authors":"Jinju Tao , Jingran He , Beibei Xiong , Yupeng Song","doi":"10.1016/j.probengmech.2024.103677","DOIUrl":"10.1016/j.probengmech.2024.103677","url":null,"abstract":"<div><p>The inherent variability of concrete significantly affects the structural safety and performance. The variability of concrete is a complex phenomenon influenced by multiple factors, including material properties, production processes, and environmental conditions. Understanding and quantifying the variability of concrete is crucial for reliable and safe structural design. Probabilistic methods are commonly used to account for concrete variability in structural design. In this paper, a composite random field approach combined with a hierarchy model is used to consider the multi-scale spatial variability of concrete. The random field of compressive strength is expressed as a sum of independent component random fields. To investigate the impact of concrete's spatial variability on structural response and failure modes, the failure analysis of a 115-m-tall chimney was conducted. The results indicate that the composite random field approach proves to be a valuable method for incorporating concrete's spatial variability at different scales. The spatial variability of concrete exerts a substantial influence on the potential positions where severe compressive damage might occur. Additionally, the failure modes are also affected by the spatial variability of concrete. When taking into account the spatial variability of concrete, an extra collapse mode emerges, aligning more closely with the chimney's actual collapse mode during an earthquake. Furthermore, the spatial variability of concrete also moderately impacts the variability of the base shear force and the maximum inter-section drift angle. Notably, improper approaches to considering the spatial variability of concrete significantly impact the concrete's compressive damage and structural response.</p></div>","PeriodicalId":54583,"journal":{"name":"Probabilistic Engineering Mechanics","volume":"77 ","pages":"Article 103677"},"PeriodicalIF":3.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141992670","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 computing technique for reliability analysis of high-dimensional and low-failure probability problems using active learning method 利用主动学习法对高维低故障概率问题进行可靠性分析的高效计算技术

IF 3 3区工程技术

Probabilistic Engineering Mechanics Pub Date : 2024-07-01 DOI: 10.1016/j.probengmech.2024.103662

Pijus Rajak, Pronab Roy

{"title":"Efficient computing technique for reliability analysis of high-dimensional and low-failure probability problems using active learning method","authors":"Pijus Rajak, Pronab Roy","doi":"10.1016/j.probengmech.2024.103662","DOIUrl":"10.1016/j.probengmech.2024.103662","url":null,"abstract":"<div><p>In spite of recent advancements in reliability analysis, high-dimensional and low-failure probability problems remain challenging because many samples and function calls are required for an accurate result. Function calls lead to a sharp increase in computational cost in terms of time. For this reason, an active learning algorithm is proposed using Kriging metamodel, where an unsupervised algorithm is used to select training samples from random samples for the first and second iterations. Then, the metamodel is improved iteratively by enriching the concerned domain with samples near the limit state function and samples obtained from a space-filling design. Hence, rapid convergence with the minimum number of function calls occurs using this active learning algorithm. An efficient stopping criterion has been developed to avoid premature or late-mature terminations of the metamodel and to regulate the accuracy of the failure probability estimations. The efficacy of this algorithm is examined using relative error, number of function calls, and coefficient of efficiency in five examples which are based on high-dimensional and low-failure probability with random and interval variables.</p></div>","PeriodicalId":54583,"journal":{"name":"Probabilistic Engineering Mechanics","volume":"77 ","pages":"Article 103662"},"PeriodicalIF":3.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141729734","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

Higher-order moments of spline chaos expansion 样条混沌扩展的高阶矩

IF 3 3区工程技术

Probabilistic Engineering Mechanics Pub Date : 2024-07-01 DOI: 10.1016/j.probengmech.2024.103666

Sharif Rahman

{"title":"Higher-order moments of spline chaos expansion","authors":"Sharif Rahman","doi":"10.1016/j.probengmech.2024.103666","DOIUrl":"10.1016/j.probengmech.2024.103666","url":null,"abstract":"<div><p>Spline chaos expansion, referred to as SCE, is a finite series representation of an output random variable in terms of measure-consistent orthonormal splines in input random variables and deterministic coefficients. This paper reports new results from an assessment of SCE’s approximation quality in calculating higher-order moments, if they exist, of the output random variable. A novel mathematical proof is provided to demonstrate that the moment of SCE of an arbitrary order converges to the exact moment for any degree of splines as the largest element size decreases. Complementary numerical analyses have been conducted, producing results consistent with theoretical findings. A collection of simple yet relevant examples is presented to grade the approximation quality of SCE with that of the well-known polynomial chaos expansion (PCE). The results from these examples indicate that higher-order moments calculated using SCE converge for all cases considered in this study. In contrast, the moments of PCE of an order larger than two may or may not converge, depending on the regularity of the output function or the probability measure of input random variables. Moreover, when both SCE- and PCE-generated moments converge, the convergence rate of the former is markedly faster than the latter in the presence of nonsmooth functions or unbounded domains of input random variables.</p></div>","PeriodicalId":54583,"journal":{"name":"Probabilistic Engineering Mechanics","volume":"77 ","pages":"Article 103666"},"PeriodicalIF":3.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141840884","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