Probabilistic Engineering Mechanics最新文献_第9页

Bayesian-based probabilistic models for the ultimate drift capacity of rectangular reinforced concrete columns failed in flexure mode 基于贝叶斯的钢筋混凝土矩形柱抗弯极限漂移能力概率模型

IF 2.6 3区工程技术

Probabilistic Engineering Mechanics Pub Date : 2024-03-19 DOI: 10.1016/j.probengmech.2024.103614

Ying Ma , Dongsheng Wang , Zhiguo Sun , Jiahao Mi , Zebin Wu

{"title":"Bayesian-based probabilistic models for the ultimate drift capacity of rectangular reinforced concrete columns failed in flexure mode","authors":"Ying Ma , Dongsheng Wang , Zhiguo Sun , Jiahao Mi , Zebin Wu","doi":"10.1016/j.probengmech.2024.103614","DOIUrl":"10.1016/j.probengmech.2024.103614","url":null,"abstract":"<div><p>To accurately predict the ultimate drift capacity of reinforced concrete (RC) columns failed in flexure mode under seismic loading, a probabilistic methodology is proposed to correct the biases in deterministic models and establish probabilistic models. Probabilistic correction models are constructed based on Bayesian updating, which can consider potential critical influences and also yield probability distribution associated with the model parameters and predictions. The probabilistic models are simplified to identify the significant informative terms by Bayesian updating. Then, the influences of the physical properties and size of the sample on the probabilistic models are discussed. The results show that the Bayesian-based correction method can increase the accuracy of predictions and quantify uncertainties. Additionally, adding new samples with different physical properties in Bayesian updating can expand the scope of application of probabilistic models, and the sample size should be at least two times the number of variables involved in Bayesian updating.</p></div>","PeriodicalId":54583,"journal":{"name":"Probabilistic Engineering Mechanics","volume":"76 ","pages":"Article 103614"},"PeriodicalIF":2.6,"publicationDate":"2024-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140201630","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

High-speed rolling bearing lubrication reliability analysis based on probability box model 基于概率盒模型的高速滚动轴承润滑可靠性分析

IF 2.6 3区工程技术

Probabilistic Engineering Mechanics Pub Date : 2024-03-19 DOI: 10.1016/j.probengmech.2024.103612

Qishui Yao , Liang Dai , Jiachang Tang , Haotian Wu , Tao Liu

{"title":"High-speed rolling bearing lubrication reliability analysis based on probability box model","authors":"Qishui Yao , Liang Dai , Jiachang Tang , Haotian Wu , Tao Liu","doi":"10.1016/j.probengmech.2024.103612","DOIUrl":"10.1016/j.probengmech.2024.103612","url":null,"abstract":"<div><p>An efficient and high-precision method is proposed for the analysis and evaluation of high-speed rolling bearing lubrication reliability based on a probability box (p-box) model. This method expands the application of mixed aleatory and epistemic uncertainties analysis within the realm of bearing lubrication reliability. Initially, the method establishes a reliability model for high-speed rolling bearing lubrication, taking into account the shear thermal effect through the analytical solution of a Γубин-type entrance zone. Subsequently, the uncertainty surrounding lubrication parameters under high-speed conditions is examined, with its mixed aleatory and epistemic uncertainties accurately depicted by using the p-box model. Furthermore, an effective and precise method for analyzing the reliability of rolling bearing lubrication is introduced based on the p-box model, in which the optimization model involved is efficiently solved using a decoupling method. Finally, lubrication reliability analysis and sensitivity analysis of parameter uncertainty levels are conducted for high-speed rolling bearings in this study. The research results demonstrate that the proposed method achieves higher accuracy and efficiency.</p></div>","PeriodicalId":54583,"journal":{"name":"Probabilistic Engineering Mechanics","volume":"76 ","pages":"Article 103612"},"PeriodicalIF":2.6,"publicationDate":"2024-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140201859","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

Probabilistic slope stability analysis: A novel distribution for soils exhibiting highly variable spatial properties 斜坡稳定性概率分析：针对空间特性变化极大的土壤的新型分布方法

IF 2.6 3区工程技术

Probabilistic Engineering Mechanics Pub Date : 2024-03-15 DOI: 10.1016/j.probengmech.2024.103586

Vincent Renaud, Marwan Al Heib

{"title":"Probabilistic slope stability analysis: A novel distribution for soils exhibiting highly variable spatial properties","authors":"Vincent Renaud, Marwan Al Heib","doi":"10.1016/j.probengmech.2024.103586","DOIUrl":"https://doi.org/10.1016/j.probengmech.2024.103586","url":null,"abstract":"<div><p>Slope stability calculation depends on the soil properties (cohesion and the friction angle) of the soil. Heterogeneous terrains are frequently observed in civil and mining projects where the properties are highly spatially variable. Based on a real data from case studies, this paper presents a probabilistic analysis of the slope stability of highly heterogeneous terrains with a very high coefficient of variation (COV) of the cohesion distribution. The existing deterministic and probabilistic approaches for calculating slope stability lack the capability to effectively consider the significant heterogeneity present in the terrain The objective of the paper is to develop a new bounded interval distribution having a COV that is as high (>150%) as the COV of the cohesion distribution The results obtained with this new distribution are compared to 4 other semi-infinite distributions. To consider the correlation between cohesion and the friction angle, a specific formulation was developed to generate friction angles varying between fixed minimum and maximum limits and having the desired correlation coefficient, mean, and standard deviation. The new cohesion and friction angle distributions were incorporated and tested in a probabilistic numerical model. The new distribution can presently be applied to geotechnical studies for terrains and heterogenous materials with properties exhibiting high spatial variability.</p></div>","PeriodicalId":54583,"journal":{"name":"Probabilistic Engineering Mechanics","volume":"76 ","pages":"Article 103586"},"PeriodicalIF":2.6,"publicationDate":"2024-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140191059","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

Effects of limit state data on constructing accurate surrogate models for structural reliability analyses 极限状态数据对构建结构可靠性分析精确替代模型的影响

IF 2.6 3区工程技术

Probabilistic Engineering Mechanics Pub Date : 2024-03-12 DOI: 10.1016/j.probengmech.2024.103595

Nhu Son Doan , Huu-Ba Dinh

{"title":"Effects of limit state data on constructing accurate surrogate models for structural reliability analyses","authors":"Nhu Son Doan , Huu-Ba Dinh","doi":"10.1016/j.probengmech.2024.103595","DOIUrl":"10.1016/j.probengmech.2024.103595","url":null,"abstract":"<div><p>Engineering problems are mainly defined in implicit processes; hence, the fully probabilistic analyses, e.g., Monte Carlo simulations (MCS), are expensive to implement. In practice, two approaches to overcome the issues are either reducing the size of simulations or developing surrogate models for actual problems. The latter does not sacrifice the size of MCS and requires less insight into probabilistic calculation; hence, it is preferable to most engineers. This study proposes an efficient framework to develop reliable and accurate surrogate models by considering data at the limit state margins (LS data). Effects of involving LS data in the training process and performances of the proposed metamodels are investigated for most issues relating to reliability analyses, including nonlinear performance functions, multiple failure modes, and implicitly defined problems. Two machine learning algorithms, including artificial neural networks and the Gaussian process, are employed to prove the ability of the proposed method. Investigations reveal that the limit state data plays a vital role in developing accurate surrogate models for reliability analyses, and accumulating them into the training dataset helps quickly construct accurate metamodels. This work contributes a practical framework for reliability analyses because the LS data can be detected easily without insight into probabilistic calculations.</p></div>","PeriodicalId":54583,"journal":{"name":"Probabilistic Engineering Mechanics","volume":"76 ","pages":"Article 103595"},"PeriodicalIF":2.6,"publicationDate":"2024-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140125717","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 metamodel-based importance sampling coupled with single-loop estimation method for parameter global reliability sensitivity analysis 基于元模型的高效重要度抽样与单回路估算方法相结合，用于参数全局可靠性灵敏度分析

IF 2.6 3区工程技术

Probabilistic Engineering Mechanics Pub Date : 2024-03-12 DOI: 10.1016/j.probengmech.2024.103597

Wanying Yun , Fengyuan Li , Xiangming Chen , Zhe Wang

{"title":"Efficient metamodel-based importance sampling coupled with single-loop estimation method for parameter global reliability sensitivity analysis","authors":"Wanying Yun , Fengyuan Li , Xiangming Chen , Zhe Wang","doi":"10.1016/j.probengmech.2024.103597","DOIUrl":"10.1016/j.probengmech.2024.103597","url":null,"abstract":"<div><p>To efficiently estimate the main effects and total effects of uncertain distribution parameters on the uncertainty of failure probability, we construct single-loop estimation formulas by introducing auxiliary variables through the equal probability transformation. This approach circumvents the original nested triple-loop process. For generating samples used in the derived single-loop estimation formulas, direct Monte Carlo simulation can be employed. To reduce the number of samples in Monte Carlo simulation, the important sampling technique can be integrated into the proposed single-loop estimation formulas. Additionally, to enhance the efficiency of identifying the states (failure or safety) of all used samples, an adaptive Kriging model can be introduced. Subsequently, the adaptive Kriging model coupled with Monte Carlo simulation, and the adaptive Kriging model coupled with the importance sampling technique, are integrated into the derived single-loop formulas to concurrently and efficiently estimate the main effects and total effects of uncertain distribution parameters. The results of three case studies validate the accuracy and efficiency of the proposed method.</p></div>","PeriodicalId":54583,"journal":{"name":"Probabilistic Engineering Mechanics","volume":"76 ","pages":"Article 103597"},"PeriodicalIF":2.6,"publicationDate":"2024-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140125958","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

Time-dependent kinematic reliability of motion mechanisms with dynamic factors 具有动态因素的运动机构的运动可靠性与时间有关

IF 2.6 3区工程技术

Probabilistic Engineering Mechanics Pub Date : 2024-03-12 DOI: 10.1016/j.probengmech.2024.103598

Xinchen Zhuang, Xin Li, Chang Liu, Tianxiang Yu, Bifeng Song

{"title":"Time-dependent kinematic reliability of motion mechanisms with dynamic factors","authors":"Xinchen Zhuang, Xin Li, Chang Liu, Tianxiang Yu, Bifeng Song","doi":"10.1016/j.probengmech.2024.103598","DOIUrl":"10.1016/j.probengmech.2024.103598","url":null,"abstract":"<div><p>Time-dependent kinematic reliability of a motion mechanism is critical for optimizing its operational performance. Dynamic factors, including material deterioration and wear in the joints, are disregarded in the prior study. As such, the envelope method is employed to undertake time-dependent kinematic reliability analysis of motion mechanisms, accounting for dynamic factors. Firstly, a decoupling strategy is proposed for decoupling the time-dependent motion error stemming from motion input and the dynamic factors. Thus, the kinematic reliability is delineated into two distinct temporal parameter-dependent issues. Subsequently, the envelope function is extended to solve the kinematic reliability. The expansion temporal points determination function (ETPDF) in the envelope function is approximated using a first-order method coupled with an active learning Kriging mode. After the expansion temporal points are found, the time-dependent reliability can be efficiently calculated via a multivariate Gaussian integral. Finally, the effectiveness and accuracy of the proposed method is verified by means of a 4-bar function generating mechanism.</p></div>","PeriodicalId":54583,"journal":{"name":"Probabilistic Engineering Mechanics","volume":"76 ","pages":"Article 103598"},"PeriodicalIF":2.6,"publicationDate":"2024-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140125482","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

Separable Gaussian neural networks for high-dimensional nonlinear stochastic systems 用于高维非线性随机系统的可分离高斯神经网络

IF 2.6 3区工程技术

Probabilistic Engineering Mechanics Pub Date : 2024-03-11 DOI: 10.1016/j.probengmech.2024.103594

Xi Wang , Siyuan Xing , Jun Jiang , Ling Hong , Jian-Qiao Sun

引用次数: 0

A method to reduce the sampling variability of time-domain fatigue life by optimizing parameters in Monte Carlo simulations 通过优化蒙特卡洛模拟参数减少时域疲劳寿命采样变异性的方法

IF 2.6 3区工程技术

Probabilistic Engineering Mechanics Pub Date : 2024-01-01 DOI: 10.1016/j.probengmech.2024.103591

Hong Sun , Yuanying Qiu , Jing Li , Jin Bai , Ming Peng

{"title":"A method to reduce the sampling variability of time-domain fatigue life by optimizing parameters in Monte Carlo simulations","authors":"Hong Sun , Yuanying Qiu , Jing Li , Jin Bai , Ming Peng","doi":"10.1016/j.probengmech.2024.103591","DOIUrl":"10.1016/j.probengmech.2024.103591","url":null,"abstract":"<div><p>Monte Carlo numerical simulations for generating stationary Gaussian random time-domain signal samples fulfil an important role in random fatigue life prediction. Control parameters such as the random seed, the sampling frequency and the number of sampling points in the numerical simulations have significant effects on the time-domain random fatigue life. In this paper, the effects are investigated systematically by utilizing commonly used power spectrum samples and engineering materials, and so a new method for optimizing the control parameter values is proposed. The proposed method solves the critical problem found in many papers that the relative error between the frequency-domain fatigue life and the time-domain fatigue life increases with the slope <em>K</em> of the <em>S–N</em> curve. Furthermore, it observably reduces the sampling variability of time-domain fatigue life for the large slope <em>K</em>, which will help the related researchers to establish better frequency-domain models for fatigue life prediction by using the time-domain fatigue life values as standard data.</p></div>","PeriodicalId":54583,"journal":{"name":"Probabilistic Engineering Mechanics","volume":"75 ","pages":"Article 103591"},"PeriodicalIF":2.6,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139923099","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

A DPIM-based probability analysis framework to obtain railway vehicle vibration characteristics considering the randomness of OOR wheel 基于 DPIM 的概率分析框架，用于获取考虑到 OOR 车轮随机性的铁路车辆振动特性

IF 2.6 3区工程技术

Probabilistic Engineering Mechanics Pub Date : 2024-01-01 DOI: 10.1016/j.probengmech.2024.103587

Tengfei Wang , Jinsong Zhou , Wenjing Sun , Dao Gong , Kai Zhou , Zhanfei Zhang , Zhixin Liu , Guoshun Li

{"title":"A DPIM-based probability analysis framework to obtain railway vehicle vibration characteristics considering the randomness of OOR wheel","authors":"Tengfei Wang , Jinsong Zhou , Wenjing Sun , Dao Gong , Kai Zhou , Zhanfei Zhang , Zhixin Liu , Guoshun Li","doi":"10.1016/j.probengmech.2024.103587","DOIUrl":"https://doi.org/10.1016/j.probengmech.2024.103587","url":null,"abstract":"<div><p>The OOR (out-of-roundness) wheel is one of the main excitation sources causing vehicle vibration. However, the OOR wheel occurs randomly, indicating that the vibration behavior of a vehicle cannot be comprehensively evaluated using a deterministic approach. Thus, a probability analysis framework is proposed to obtain vehicle vibration characteristics while considering the randomness of the OOR wheel. The probability model of the random OOR wheel is derived by reducing the high-dimensional variables into a few independent variables of the radius, amplitude, and phase. Then, the vertical vehicle-track coupled system with OOR wheels is modelled. A DPIM (direct probability integral method) is further developed to analyze the evolution of excitation to response probabilities. Finally, the statistics of the random vibration of the vehicle are calculated. The proposed framework is verified using a numerical case. Results show that the PDF (probability density function) shape of the vehicle random vibration, induced by the Gaussian-distributed OOR wheel, deviates from the Gaussian distribution due to the nonlinear wheel/rail contact force. Instead, it exhibits a right-skewed shape, significantly impacting the dynamic performance. As the mean or coefficient of variation of the OOR wheel amplitude increases linearly, the reliability of the vehicle Sperling index experiences a quadratic or double-sloping decrease. Consequently, a maintenance threshold for OOR wheel amplitudes is given based on reliability considerations. Compared to Monte Carlo simulation, the proposed framework offers a computational efficiency improvement of at least one order of magnitude.</p></div>","PeriodicalId":54583,"journal":{"name":"Probabilistic Engineering Mechanics","volume":"75 ","pages":"Article 103587"},"PeriodicalIF":2.6,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139992411","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

Structural reliability analysis based on probability density evolution method and stepwise truncated variance reduction 基于概率密度演化法和逐步截断方差缩小法的结构可靠性分析

IF 2.6 3区工程技术

Probabilistic Engineering Mechanics Pub Date : 2024-01-01 DOI: 10.1016/j.probengmech.2024.103580

Tong Zhou , Tong Guo , You Dong , Yongbo Peng

{"title":"Structural reliability analysis based on probability density evolution method and stepwise truncated variance reduction","authors":"Tong Zhou , Tong Guo , You Dong , Yongbo Peng","doi":"10.1016/j.probengmech.2024.103580","DOIUrl":"10.1016/j.probengmech.2024.103580","url":null,"abstract":"<div><p>To address the substantial computational burden associated with probability density evolution method (PDEM) in structural reliability analysis<span>, this study proposes a novel look-ahead learning function named stepwise truncated variance reduction (STVR), integrating polynomial chaos Kriging (PCK) and PDEM. Three key features of STVR are highlighted. First, it enables quantifying the maximum reduction in predictive errors of PCK within the regions of interest (ROI) when adding a new point. Second, closed-form expression for STVR is derived through Kriging update formulas, eliminating the need for computationally intensive Gauss–Hermite quadrature or extensive conditional simulations of PCK. Third, a dynamic adjustment procedure is proposed for the probability level-related parameter in STVR, with the aim of achieving a good balance between the exploitation and exploration of ROI during the sequential experimental design process. The performance of STVR is demonstrated through two benchmark analytical functions and three numerical examples of varying complexity. Results indicate that the dynamic adjustment procedure for the probability level-related parameter in STVR outperforms the empirical setting of a minor value. Then, STVR proves more advantageous than existing pointwise and look-ahead learning functions, particularly in addressing complex dynamic reliability problems.</span></p></div>","PeriodicalId":54583,"journal":{"name":"Probabilistic Engineering Mechanics","volume":"75 ","pages":"Article 103580"},"PeriodicalIF":2.6,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139471262","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