Probabilistic Engineering Mechanics最新文献

{"title":"An imprecise multiscale uncertainty quantification framework for fiber reinforced composites","authors":"Haodong Zhao,&nbsp;Changcong Zhou","doi":"10.1016/j.probengmech.2024.103686","DOIUrl":"10.1016/j.probengmech.2024.103686","url":null,"abstract":"<div><div>The study focuses on the reliability and global sensitivity analysis of fiber-reinforced composite radome structures, considering uncertainty from a multiscale perspective. Macroparameters are estimated based on microparameters using the multiscale analysis method for composites, and a reliability analysis model of the composite structure at the macrolevel is constructed. The material performance mechanism is explored in depth, both \"from bottom to top\" and \"from top to bottom\", to reveal its inherent laws. Due to insufficient variable distribution information, an imprecise probabilistic model is introduced to characterize the uncertainty effect in multiscale composite analysis. A nested optimization calculation method is applied to obtain reliability and sensitivity results. To ensure both calculation accuracy and efficiency, the regression and classification problems encountered in the proposed framework are addressed using two support vector machine models. The reliability and sensitivity analysis under the imprecise probabilistic framework can help engineers identify significant influential factors, thereby guiding the design of composite radome structures.</div></div>","PeriodicalId":54583,"journal":{"name":"Probabilistic Engineering Mechanics","volume":"78 ","pages":"Article 103686"},"PeriodicalIF":3.0,"publicationDate":"2024-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142311996","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}

{"title":"Hybrid Bayesian-Copula-based damage probability estimation for steel-concrete composite tall buildings under concurrent seismic and wind loads","authors":"Xiao-Wei Zheng ,&nbsp;Jie Cheng ,&nbsp;Ling-Xin Zhang ,&nbsp;Xian-Xin Xie","doi":"10.1016/j.probengmech.2024.103693","DOIUrl":"10.1016/j.probengmech.2024.103693","url":null,"abstract":"<div><div>Tall buildings with long service periods inevitably face multiple hazards, and the uncertainty associated with various factors has a considerable impact on life-cycle structural safety estimation. This study presents a hybrid Bayesian-Copula-based methodology for evaluating the damage risk e of tall buildings under concurrent seismic and strong wind excitations that incorporate various uncertainties. The main contributions of this study to the field of probabilistic multi-hazard risk assessment include the following: (1) The Bayes statistic method is employed to develop posterior probability distributions of the unknown parameters in the marginal probability models of an individual earthquake and strong wind as well as parameters involved in the multi-hazard demand model for fragility estimation. (2) The Bayesian-based method is applied to update the existing joint probabilistic model of earthquakes and strong winds. (3) A new method is presented to estimate the muti-hazard fragility bounds. The damage risk assessment quantifies the epistemic uncertainties of the unknown demand model parameters by calculating the total probability in the domain of the definition of the model parameters. A representative composite building with 42 floors is selected to perform this multi-hazard damage risk assessment method. The application of this study highlights the considerable impact of epistemic uncertainties and loading directions on damage risk. This presented Bayesian-Copula-based method is beneficial for decision-making involving tall buildings subjected to multiple hazards.</div></div>","PeriodicalId":54583,"journal":{"name":"Probabilistic Engineering Mechanics","volume":"78 ","pages":"Article 103693"},"PeriodicalIF":3.0,"publicationDate":"2024-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142311995","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}

{"title":"Numerical investigation of turbulence effect on flight trajectory of spherical windborne debris: A multi-layered approach","authors":"Shaopeng Li ,&nbsp;Kurtis Gurley ,&nbsp;Yanlin Guo ,&nbsp;John van de Lindt","doi":"10.1016/j.probengmech.2024.103661","DOIUrl":"https://doi.org/10.1016/j.probengmech.2024.103661","url":null,"abstract":"<div><p>Accurate modeling of the turbulent wind field is a crucial component of risk analysis for structures to windborne debris damage. Existing studies typically simplify the complexities of wind turbulence, and the potential influence on the accuracy of debris flight modeling has not been systematically demonstrated. This study takes a multi-layered approach to numerically simulate the flight trajectory of spherical debris in a turbulent wind field. Complexities are incrementally added to the simulated wind field to systematically investigate the influence of spatial correlation and non-Gaussian features of turbulence on debris flight behavior. The sensitivity of debris flight behavior to turbulent wind features will inform the design of debris flight tracking wind tunnel tests and building façade debris vulnerability modeling efforts.</p></div>","PeriodicalId":54583,"journal":{"name":"Probabilistic Engineering Mechanics","volume":"77 ","pages":"Article 103661"},"PeriodicalIF":3.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141483784","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}

{"title":"Uncertainties quantification for damage localization in concrete based on Bayesian method","authors":"Minghui Zhang,&nbsp;Deyuan Zhou,&nbsp;Xia Yang,&nbsp;Xiangtao Sun,&nbsp;Qingzhao Kong","doi":"10.1016/j.probengmech.2024.103660","DOIUrl":"https://doi.org/10.1016/j.probengmech.2024.103660","url":null,"abstract":"<div><p>The presence of defects in concrete can diminish load-bearing capacity of structures, giving rise to potential concerns regarding safety and durability. Thus, a method that enhances the sensitivity, resolution and robustness of damage localization is critically necessary to assess the condition of concrete structures. This research presents a damage localization method based on Bayesian probabilistic fusion, and uncertainties from measurement and identification process are considered and quantified. The likelihood function is constructed based on the hyperbola-based damage localization method, and the posterior distributions of unknown parameters are calculated via Bayesian theorem combined with measurement data. Furthermore, a meso-level finite element model is established, wherein the concrete medium is considered as a three-phase composite material consisting of polygonal aggregates, mortar matrix and interface transition zones. Owing to the meso-level modeling, the propagation behavior of stress waves within concrete and complicated interactions between stress waves and concrete internal structures can be better characterized. Finally, the damage information, time-difference-of arrival, is extracted from the response signals and the efficiency of the proposed method is verified numerically. The numerical results demonstrate that the proposed probabilistic fusion method outperforms the conventional hyperbola-based method in terms of achieving high spatial resolution and resilience in damage localization.</p></div>","PeriodicalId":54583,"journal":{"name":"Probabilistic Engineering Mechanics","volume":"77 ","pages":"Article 103660"},"PeriodicalIF":3.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141596427","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}

{"title":"Performance-based target reliability analysis of offshore wind turbine mooring lines subjected to the wind and wave","authors":"Mohammad Safari ,&nbsp;Seyed Hooman Ghasemi ,&nbsp;Andrzej S. Nowak","doi":"10.1016/j.probengmech.2024.103673","DOIUrl":"10.1016/j.probengmech.2024.103673","url":null,"abstract":"<div><p>Reliability assessment is a crucial aspect of the design and operation of structures, particularly in balancing safety and cost considerations. This paper introduces a novel method for evaluating the performance-based target reliability of floating wind turbine platforms in offshore environments. The method focuses on the platform's motion modes and wave frequencies, which significantly influence the system's structural integrity and performance. An improved limit state function is proposed to enhance the accuracy of reliability calculations, specifically for steady-state conditions. The platform's six degrees of freedom motions are carefully analyzed to investigate their dependence on wave frequencies. By considering the time response of these motions and accounting for uncertainties in wave characteristics, wave impact directions, and wind effects, a comprehensive reliability analysis is conducted to assess the stability modes of the platform. This paper introduces the term 'Reliability Performance-Based' (RPB) analysis as a new concept to evaluate the system's reliability at a given performance level. Furthermore, an optimal target reliability index is defined to address the economic aspect of the design process. The proposed methodology's PEB analysis focuses on capturing uncertainties in wave characteristics and wind effects on floating wind turbine platforms. This includes a detailed examination of wave and wind-induced loads and their propagation through the system concerning its performance level. Statistical models were integrated to quantify these uncertainties, applying Monte Carlo simulations to assess their effects on the platform's reliability. This approach allows for a nuanced understanding of the interactions between environmental factors and structural responses, enhancing the precision of our reliability assessments. It enables the consideration of economic efficiency alongside safety, ensuring a balanced approach to the design and operation of the floating wind turbine platform. By providing a comprehensive reliability assessment framework, it aids in the optimization of design and decision-making processes for floating wind turbine platforms.</p></div>","PeriodicalId":54583,"journal":{"name":"Probabilistic Engineering Mechanics","volume":"77 ","pages":"Article 103673"},"PeriodicalIF":3.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141934393","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}

{"title":"Modal–based uncertainty quantification for deterministically estimated structural parameters in low-fidelity model updating of complex connections","authors":"Milad Mehrkash,&nbsp;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}

{"title":"An improved interval prediction method for recurrence period wind speed","authors":"Weihu Chen ,&nbsp;Yuji Tian ,&nbsp;Yiyi Tian ,&nbsp;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}

{"title":"Statistical model calibration of correlated unknown model variables through identifiability improvement","authors":"Jeonghwan Choo ,&nbsp;Yongsu Jung ,&nbsp;Hwisang Jo ,&nbsp;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}

{"title":"Inference on the high-reliability lifetime estimation based on the three-parameter Weibull distribution","authors":"Xiaoyu Yang ,&nbsp;Liyang Xie ,&nbsp;Bowen Wang ,&nbsp;Jianpeng Chen ,&nbsp;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}

{"title":"Probability density of the solution to nonlinear systems driven by Gaussian and Poisson white noises","authors":"Wantao Jia ,&nbsp;Zhe Jiao ,&nbsp;Wanrong Zan ,&nbsp;Weiqiu Zhu","doi":"10.1016/j.probengmech.2024.103658","DOIUrl":"https://doi.org/10.1016/j.probengmech.2024.103658","url":null,"abstract":"<div><p>A new method is proposed to compute the probability density of the multi-dimensional nonlinear dynamical system perturbed by a combined excitation of Gaussian and Poisson white noises. We first deduce a probability-density solver from the Euler–Maruyama scheme of the stochastic system and the corresponding Chapman–Kolmogorov equation. This solver actually is an explicit numerical formula of the probability density of the solution to this stochastic system. To compute the probability density, we propose an efficient algorithm for this solver, which actually is the implementation of a numerical integration. Furthermore, we prove this solver is an approximated solution of the corresponding forward Kolmogorov equation. Numerical examples are conducted to illustrate our probability-density solver.</p></div>","PeriodicalId":54583,"journal":{"name":"Probabilistic Engineering Mechanics","volume":"77 ","pages":"Article 103658"},"PeriodicalIF":3.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141486674","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}