Probabilistic Engineering Mechanics最新文献_第10页

Geometrical uncertainties influence on the failure load estimation of lattice structures 几何不确定性对格状结构失效载荷估算的影响

IF 2.6 3区工程技术

Probabilistic Engineering Mechanics Pub Date : 2024-04-01 DOI: 10.1016/j.probengmech.2024.103636

Mattia Schiantella, Federico Cluni, Vittorio Gusella

{"title":"Geometrical uncertainties influence on the failure load estimation of lattice structures","authors":"Mattia Schiantella, Federico Cluni, Vittorio Gusella","doi":"10.1016/j.probengmech.2024.103636","DOIUrl":"10.1016/j.probengmech.2024.103636","url":null,"abstract":"<div><p>Lattice structures can provide high strength with modest weight. For this reason, they are found in many natural systems at the microscopic level and have also been adopted in engineering at many scales. Assessment of the load-bearing capacity of such structures is crucial and cannot ignore considerations of imperfections, whether due to natural factors if the material exists naturally or to manufacturing defects if it is created artificially. Defects can affect many geometrical aspects of the lattice such as the shape of cells and the thickness and the waviness of trusses. In this paper, we will focus on the first aspect, investigating the effect of variation of the shape of the cells by applying a perturbation to the periodic configuration for common geometries. The failure load of these systems is evaluated by means of an upper bound limit analysis through linear programming, varying the relative density of the lattice and the intensity of imperfections. The failure load is addressed by statistical moments and probability density functions.</p></div>","PeriodicalId":54583,"journal":{"name":"Probabilistic Engineering Mechanics","volume":"76 ","pages":"Article 103636"},"PeriodicalIF":2.6,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0266892024000584/pdfft?md5=4b785999cd0676bfdccb1a0216d419e6&pid=1-s2.0-S0266892024000584-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141131162","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

A direct analytical derivation of the multi-dimensional fragility spaces of structures under nonstationary mainshock-multi-aftershock sequences 非稳态主震-多余震序列下结构多维脆性空间的直接分析推导

IF 2.6 3区工程技术

Probabilistic Engineering Mechanics Pub Date : 2024-04-01 DOI: 10.1016/j.probengmech.2024.103630

Xu-Yang Cao , De-Cheng Feng

{"title":"A direct analytical derivation of the multi-dimensional fragility spaces of structures under nonstationary mainshock-multi-aftershock sequences","authors":"Xu-Yang Cao , De-Cheng Feng","doi":"10.1016/j.probengmech.2024.103630","DOIUrl":"https://doi.org/10.1016/j.probengmech.2024.103630","url":null,"abstract":"<div><p>Performance-based earthquake engineering (PBEE) is a popular direction in the earthquake community, and at this stage, risk-based PBEE has become mainstream. In the risk-based probabilistic framework, seismic fragility analysis constitutes the most important link, and corresponding research on the mainshock–aftershock sequence has received widespread attention in recent years. Since a mainshock is often accompanied by multiple aftershocks and there is great uncertainty in the vibration characteristics of aftershocks, a seismic fragility analysis of structures under a stochastic mainshock-multi-aftershock sequence is meaningful and necessary. The corresponding questions, such as how to derive the multi-dimensional analytical fragility form under a stochastic mainshock-multi-aftershock sequence and how to correlate multiple intensity measures with multiple demand parameters, still require further investigation. In this paper, a direct analytical derivation of the multi-dimensional seismic fragility spaces of structures under nonstationary stochastic mainshock-multi-aftershock sequences is introduced. The methodology framework, implementation steps, and application examples are also provided in detail. Moreover, two scenarios, the one-mainshock-one-aftershock and one-mainshock-two-aftershocks, are considered, and the obtained multi-dimensional analytical fragility spaces for both scenarios are validated. In general, the matching accuracy of the fragility results for both scenarios is proven to be high, and the direct analytical derivation of the multi-dimensional fragility spaces is validated to be ideally consistent, which further provides a reference for multi-dimensional risk analysis under nonstationary stochastic mainshock-multi-aftershock sequences in future work.</p></div>","PeriodicalId":54583,"journal":{"name":"Probabilistic Engineering Mechanics","volume":"76 ","pages":"Article 103630"},"PeriodicalIF":2.6,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141068398","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 layer assigned probability space partition method for structural small failure probability problem 结构小故障概率问题的层分配概率空间划分方法

IF 2.6 3区工程技术

Probabilistic Engineering Mechanics Pub Date : 2024-04-01 DOI: 10.1016/j.probengmech.2024.103633

Yang Bai , Chaolie Ning , Jie Li

{"title":"A layer assigned probability space partition method for structural small failure probability problem","authors":"Yang Bai , Chaolie Ning , Jie Li","doi":"10.1016/j.probengmech.2024.103633","DOIUrl":"10.1016/j.probengmech.2024.103633","url":null,"abstract":"<div><p>The Physical Synthesis Method (PSM) stands out as a robust framework for conducting structural reliability analyses due to its clear conceptual foundation. However, this approach often necessitates significant computational resources when addressing scenarios with small failure probabilities. In response to this challenge, this study introduces a layer assigned probability space partition method designed to identify pivotal points based on the ultimate bearing capacity failure criterion of structural components within the PSM framework. Drawing inspiration from Harbitz's <em>β</em>-sphere, this method effectively utilizes the minimum reliability index of components to discern essential representative points within the probability space, thus streamlining computations. The efficacy of this approach is showcased through two case studies: a simply supported beam and a six-story reinforced concrete frame. The outcomes demonstrate that the proposed method, when integrated with PSM, exhibits a substantial enhancement in efficiency compared to the conventional Monte Carlo method. Besides, under equivalent computational resources, it achieves superior computational accuracy compared to the importance sampling method, particularly in scenarios with small failure probabilities. Furthermore, by introducing the notion of a common safe domain, this method addresses challenges in structural reliability analyses involving multiple failure surfaces.</p></div>","PeriodicalId":54583,"journal":{"name":"Probabilistic Engineering Mechanics","volume":"76 ","pages":"Article 103633"},"PeriodicalIF":2.6,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141056088","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

Uncertainty quantification for viscoelastic composite materials using time-separated stochastic mechanics 利用分时随机力学量化粘弹性复合材料的不确定性

IF 2.6 3区工程技术

Probabilistic Engineering Mechanics Pub Date : 2024-04-01 DOI: 10.1016/j.probengmech.2024.103618

Hendrik Geisler , Philipp Junker

{"title":"Uncertainty quantification for viscoelastic composite materials using time-separated stochastic mechanics","authors":"Hendrik Geisler , Philipp Junker","doi":"10.1016/j.probengmech.2024.103618","DOIUrl":"https://doi.org/10.1016/j.probengmech.2024.103618","url":null,"abstract":"<div><p>With the growing use of composite materials, the need for high-fidelity simulation techniques of the related behavior increases. One important aspect to take into account is the uncertainty of the response due to fluctuations of the material parameters of the constituent materials of the homogenized composite. This inherent randomness leads to stochastic stresses on the microscale and uncertain macroscale response. Until now, the viscoelastic response of the matrix material seriously hindered the application of efficient methods to predict the composite material behavior. In this work, a novel method based on the time-separated stochastic mechanics (TSM) is developed to address this problem. We present how the uncertainty of the microscale stresses of a representative volume element and the homogenized macroscale stresses can be approximated with a low number of deterministic finite element simulations. Quantities of interest are the expectation, standard deviation, and the probability distribution function of the stresses on micro- and macroscale. The results showcase that the TSM is able to approximate the reference results very well at a minimal fraction of the computational cost needed for Monte Carlo simulations.</p></div>","PeriodicalId":54583,"journal":{"name":"Probabilistic Engineering Mechanics","volume":"76 ","pages":"Article 103618"},"PeriodicalIF":2.6,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0266892024000407/pdfft?md5=693df2dfebad469599cd7accf6155b04&pid=1-s2.0-S0266892024000407-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140346775","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

Meta-model based sequential importance sampling method for structural reliability analysis under high dimensional small failure probability 基于元模型的高维小失效概率下结构可靠性分析序列重要性抽样法

IF 2.6 3区工程技术

Probabilistic Engineering Mechanics Pub Date : 2024-04-01 DOI: 10.1016/j.probengmech.2024.103620

Yuming Zhang , Juan Ma

{"title":"Meta-model based sequential importance sampling method for structural reliability analysis under high dimensional small failure probability","authors":"Yuming Zhang , Juan Ma","doi":"10.1016/j.probengmech.2024.103620","DOIUrl":"https://doi.org/10.1016/j.probengmech.2024.103620","url":null,"abstract":"<div><p>Reliability analysis poses a significant challenge for complex structures with stringent reliability requirements. While Sequential Importance Sampling (SIS) and Subset Simulation (SUS) have proven highly effective in addressing high-dimensional problems with small failure probabilities, the computational burden of mechanical simulations remains substantial due to the time-consuming nature of numerical simulation processes. Consequently, this paper introduces a novel approach, denoted as AK-SIS, which combines SIS with Kriging metamodeling specifically designed to address computational challenges associated with small failure probabilities. The fundamental principle of this approach involves utilizing AK-MCS technology (Echard et al., 2011) [1] as a precursor to the SIS approach to initially generate metamodels. These metamodels are then employed in lieu of performance functions in subsequent steps, significantly reducing the number of function calls required to simulate complex engineering problems when applying SIS techniques directly. By inheriting the advantages of SIS, AK-SIS has demonstrated its suitability for reliability analysis in scenarios involving high-dimensional spaces and small fault probabilities. Furthermore, AK-SIS is not limited by the shape of the failure domain, eliminates the need to solve the design point, and is particularly well-suited for analyzing reliability in cases of discontinuous failure domains, multiple failure domains, as well as complex failure domains and rare events. The efficacy of AK-SIS is substantiated through rigorous evaluation encompassing nonlinear, high-dimensional examples, and an engineering application. These empirical validations collectively contribute to a robust methodological framework for reliability analysis of intricate structures characterized by stringent reliability requirements.</p></div>","PeriodicalId":54583,"journal":{"name":"Probabilistic Engineering Mechanics","volume":"76 ","pages":"Article 103620"},"PeriodicalIF":2.6,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140535085","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

Reference prior for Bayesian estimation of seismic fragility curves 地震脆性曲线贝叶斯估算的参考先验

IF 2.6 3区工程技术

Probabilistic Engineering Mechanics Pub Date : 2024-04-01 DOI: 10.1016/j.probengmech.2024.103622

Antoine Van Biesbroeck , Clément Gauchy , Cyril Feau , Josselin Garnier

{"title":"Reference prior for Bayesian estimation of seismic fragility curves","authors":"Antoine Van Biesbroeck , Clément Gauchy , Cyril Feau , Josselin Garnier","doi":"10.1016/j.probengmech.2024.103622","DOIUrl":"https://doi.org/10.1016/j.probengmech.2024.103622","url":null,"abstract":"<div><p>One of the key elements of probabilistic seismic risk assessment studies is the fragility curve, which represents the conditional probability of failure of a mechanical structure for a given scalar measure derived from seismic ground motion. For many structures of interest, estimating these curves is a daunting task because of the limited amount of data available; data which is only binary in our framework, i.e., only describing the structure as being in a failure or non-failure state. A large number of methods described in the literature tackle this challenging framework through parametric log-normal models. Bayesian approaches, on the other hand, allow model parameters to be learned more efficiently. However, the impact of the choice of the prior distribution on the posterior distribution cannot be readily neglected and, consequently, neither can its impact on any resulting estimation. This paper proposes a comprehensive study of this parametric Bayesian estimation problem for limited and binary data. Using the reference prior theory as a cornerstone, this study develops an objective approach to choosing the prior. This approach leads to the Jeffreys prior, which is derived for this problem for the first time. The posterior distribution is proven to be proper (i.e., it integrates to unity) with the Jeffreys prior but improper with some traditional priors found in the literature. With the Jeffreys prior, the posterior distribution is also shown to vanish at the boundaries of the parameters’ domain, which means that sampling the posterior distribution of the parameters does not result in anomalously small or large values. Therefore, the use of the Jeffreys prior does not result in degenerate fragility curves such as unit-step functions, and leads to more robust credibility intervals. The numerical results obtained from two different case studies—including an industrial example—illustrate the theoretical predictions.</p></div>","PeriodicalId":54583,"journal":{"name":"Probabilistic Engineering Mechanics","volume":"76 ","pages":"Article 103622"},"PeriodicalIF":2.6,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140547321","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

Maximum likelihood estimation of probabilistically described loads in beam structures 梁结构中概率描述荷载的最大似然估计

IF 2.6 3区工程技术

Probabilistic Engineering Mechanics Pub Date : 2024-04-01 DOI: 10.1016/j.probengmech.2024.103627

Andreas Tsiotas-Niachopetros, Nicholas E. Silionis, Konstantinos N. Anyfantis

{"title":"Maximum likelihood estimation of probabilistically described loads in beam structures","authors":"Andreas Tsiotas-Niachopetros, Nicholas E. Silionis, Konstantinos N. Anyfantis","doi":"10.1016/j.probengmech.2024.103627","DOIUrl":"https://doi.org/10.1016/j.probengmech.2024.103627","url":null,"abstract":"<div><p>In recent years, focus has been shifted towards predictive maintenance in an effort to improve the reliability of operating structures. Processing structural response data obtained from in-situ sensors during operation can provide added value towards this direction. Structural Health Monitoring (SHM) methods are uniquely suited for this task; however, accounting for the effect of stochastic structural loads is critical for their robustness. In this work, a framework based on Maximum Likelihood Estimation (MLE) is presented, whose goal is to obtain inferences on typically unobservable quantities that describe stochastic structural loading. A structural beam is employed as a demonstrative case study, that is subjected to point loads with stochastic magnitude and application points. The hyperparameters that govern their underlying probability distribution functions (pdf) are the quantities of inferential interest. The inverse (load) identification process is performed using a marginalized MLE objective, where stochastic Monte Carlo (MC) integration is employed to perform the marginalization and Genetic Algorithms (GAs) are used as the optimizer. The Cramer–Rao (CR) lower bound is used to produce 95 % Confidence Intervals (CIs) to quantify estimation uncertainty.</p></div>","PeriodicalId":54583,"journal":{"name":"Probabilistic Engineering Mechanics","volume":"76 ","pages":"Article 103627"},"PeriodicalIF":2.6,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140650079","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

Topological detection of phenomenological bifurcations with unreliable kernel density estimates 利用不可靠的核密度估计对现象学分岔进行拓扑检测

IF 2.6 3区工程技术

Probabilistic Engineering Mechanics Pub Date : 2024-04-01 DOI: 10.1016/j.probengmech.2024.103634

Sunia Tanweer, Firas A. Khasawneh

{"title":"Topological detection of phenomenological bifurcations with unreliable kernel density estimates","authors":"Sunia Tanweer, Firas A. Khasawneh","doi":"10.1016/j.probengmech.2024.103634","DOIUrl":"https://doi.org/10.1016/j.probengmech.2024.103634","url":null,"abstract":"<div><p>Phenomenological (P-type) bifurcations are qualitative changes in stochastic dynamical systems whereby the stationary probability density function (PDF) changes its topology. The current state of the art for detecting these bifurcations requires reliable kernel density estimates computed from an ensemble of system realizations. However, in several real world signals such as Big Data, only a single system realization is available—making it impossible to estimate a reliable kernel density. This study presents an approach for detecting P-type bifurcations using unreliable density estimates. The approach creates an ensemble of objects from Topological Data Analysis (TDA) called persistence diagrams from the system’s sole realization and statistically analyzes the resulting set. We compare several methods for replicating the original persistence diagram including Gibbs point process modelling, Pairwise Interaction Point Modelling, and subsampling. We show that for the purpose of predicting a bifurcation, the simple method of subsampling exceeds the other two methods of point process modelling in performance.</p></div>","PeriodicalId":54583,"journal":{"name":"Probabilistic Engineering Mechanics","volume":"76 ","pages":"Article 103634"},"PeriodicalIF":2.6,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141089848","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

Hybrid machine learning with Bayesian optimization methods for prediction of patch load resistance of unstiffened plate girders 混合机器学习与贝叶斯优化方法用于预测非刚度板梁的贴片抗荷载能力

IF 2.6 3区工程技术

Probabilistic Engineering Mechanics Pub Date : 2024-04-01 DOI: 10.1016/j.probengmech.2024.103624

Dai-Nhan Le , Thai-Hoan Pham , George Papazafeiropoulos , Zhengyi Kong , Viet-Linh Tran , Quang-Viet Vu

{"title":"Hybrid machine learning with Bayesian optimization methods for prediction of patch load resistance of unstiffened plate girders","authors":"Dai-Nhan Le , Thai-Hoan Pham , George Papazafeiropoulos , Zhengyi Kong , Viet-Linh Tran , Quang-Viet Vu","doi":"10.1016/j.probengmech.2024.103624","DOIUrl":"https://doi.org/10.1016/j.probengmech.2024.103624","url":null,"abstract":"<div><p>This paper aims to propose a new hybrid Machine Learning (ML) with Bayesian Optimization (BO) methods for predicting the patch loading resistance, <em>P</em><sub><em>u</em></sub> of longitudinally unstiffened plate girders. A total of 354 tests of the unstiffened plate girder under patch loading are collected and used for the training and testing to establish the proposed models. Five ML models including Support Vector Machines (SVM), Decision Tree (DT), Gradient Boosted Tree (GBT), Extreme Gradient Boosting algorithm (XGBoost), and CatBoost regression (CAT) are employed, and the BO method is used to optimize the hyperparameters of these ML models, in order to show which of them is best-suited for prediction of the PLR of longitudinally unstiffened plate girders. It was found that the BO-GBT presents the best accuracy compared to others. The performance of the BO-GBT model is validated by comparing its predictive results with the current design standards and the existing formulae. Additionally, the Shapley Additive Explanations (SHAP) method is employed to evaluate the importance and contributions of each input variable on the proposed model, and a Graphical User Interface (GUI) tool is developed to conveniently estimate the <em>P</em><sub><em>u</em></sub> of the unstiffened plate girders. Finally, the BO-GBT model is used to develop a support tool for finding suitable geometric dimensions and material properties of longitudinally unstiffened girder under patch loading in the preliminary design stage. The optimization tool is accessible online for the users for more convenient use in practical design purposes.</p></div>","PeriodicalId":54583,"journal":{"name":"Probabilistic Engineering Mechanics","volume":"76 ","pages":"Article 103624"},"PeriodicalIF":2.6,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140551712","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

Probability density evolution method based stochastic simulation of near-fault pulse-like ground motions 基于概率密度演化法的近断层脉冲地动随机模拟

IF 2.6 3区工程技术

Probabilistic Engineering Mechanics Pub Date : 2024-04-01 DOI: 10.1016/j.probengmech.2024.103629

Chengrui Luo , Yongbo Peng

{"title":"Probability density evolution method based stochastic simulation of near-fault pulse-like ground motions","authors":"Chengrui Luo , Yongbo Peng","doi":"10.1016/j.probengmech.2024.103629","DOIUrl":"https://doi.org/10.1016/j.probengmech.2024.103629","url":null,"abstract":"<div><p>Quantifying the near-fault effect and establishing a reasonable model of near-fault pulse-like ground motions are particularly important for seismic design of structures in near-fault regions. Given the pronounced randomness associated with earthquakes, this study first proposes a novel stochastic model of near-fault pulse-like ground motions by combining the improved finite-fault model (IFFM) and the multivariate copula-based velocity-pulse model (CVPM). Further, a probability density evolution method (PDEM) based stochastic simulation method is developed, by which the model parameters can be determined in a unified probability space so as to ensure the consistency of two independent models. For illustrative purposes, the observed records collected from the 1999 Chi-Chi earthquake are used to generate new stochastic ground motions set. Two ground motions sets based on classical stochastic simulation methods are also presented for comparison. Numerical results show that the proposed method for stochastic simulation of near-fault pulse-like ground motions is reliable; the statistics of peak ground accelerations and spectral characteristics of simulated samples are consistent with station records. Besides, the proposed method accommodates the noteworthy randomness and proportion consistency of components associated with near-fault pulse-like ground motions, making it suitable for the stochastic response and reliability analysis of seismic structures in near-fault regions. This superiority is challenging to classical stochastic simulation methods that lack reasonable consideration of randomness and correlation associated with model parameters.</p></div>","PeriodicalId":54583,"journal":{"name":"Probabilistic Engineering Mechanics","volume":"76 ","pages":"Article 103629"},"PeriodicalIF":2.6,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140650078","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