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Meta model-based and cross entropy-based importance sampling algorithms for efficiently solving system failure probability function Probab. Eng. Mech. (IF 2.6) Pub Date : 2024-03-21 Yizhou Chen, Zhenzhou Lu, Xiaomin Wu
The multi-mode system failure probability function (SFPF) can quantify how the distribution parameters of the random input vector affect the system safety and decouple the system reliability-based design optimization model. However, for a problem with a time-consuming implicit performance function and rare failure domain, efficiently solving the SFPF remains significantly challenging. Therefore, in
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Seismic collaborative reliability analysis for a slope considering spatial variability base on optimized subset simulation Probab. Eng. Mech. (IF 2.6) Pub Date : 2024-03-19 Bin Xu, Dianjun Zhu, Mingyang Xu, Rui Pang
Seismic reliability analysis of actual slopes considering the spatial variability of soil materials is crucial. However, for the discretization of large-scale random fields, high-precision finite element analysis and analysis of small failure probability events, the random analysis of slopes under seismic loads is inefficient. To address this situation, this study proposes a collaborative reliability
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Bayesian-based probabilistic models for the ultimate drift capacity of rectangular reinforced concrete columns failed in flexure mode Probab. Eng. Mech. (IF 2.6) Pub Date : 2024-03-19 Ying Ma, Dong-Sheng Wang, Zhi-Guo Sun, Jia-Hao Mi, Ze-Bin Wu
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
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High-speed rolling bearing lubrication reliability analysis based on probability box model Probab. Eng. Mech. (IF 2.6) Pub Date : 2024-03-19 Qishui Yao, Liang Dai, Jiachang Tang, Haotian Wu, Tao Liu
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
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Small failure probability analysis of stochastic structures based on a new hybrid approach Probab. Eng. Mech. (IF 2.6) Pub Date : 2024-03-19 Huan Huang, Huiying Wang, Yingxiong Li, Gaoyang Li, Hengbin Zheng
The small failure probability problem of stochastic structures is investigated by using two types of surrogate models and the subset simulation method in conjunction with parallel computation. To achieve high computational efficiency, the explicit expression of dynamic responses of stochastic structures is first derived in the form based on the explicit time-domain method. Then, the small failure probability
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Probabilistic identification method for seismic failure modes of reinforced concrete beam-column joints using Gaussian process with deep kernel Probab. Eng. Mech. (IF 2.6) Pub Date : 2024-03-19 Zecheng Yu, Bo Yu, Bing Li
Identifying the seismic failure modes of beam-column joints (BCJs) is crucial for the safety and integrity of reinforced concrete (RC) buildings or structures withstanding seismic forces. However, traditional identification methods fail to provide any indication about the uncertainties within their predictions, which is beneficial for evaluating, interpreting and improving these predictions. This study
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Conditional simulation of stationary non-Gaussian processes based on unified hermite polynomial model Probab. Eng. Mech. (IF 2.6) Pub Date : 2024-03-19 Zhao Zhao, Zhao-Hui Lu, Yan-Gang Zhao
The conditional simulation of non-Gaussian excitations utilizing records from the monitoring system is of great significance for hazard mitigation. To this end, this paper proposes a novel conditional non-Gaussian simulation method. In this method, the Unified Hermite Polynomial Model (UHPM) is used to describe the transformation relationship between recorded and unrecorded non-Gaussian processes and
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Probabilistic slope stability analysis: A novel distribution for soils exhibiting highly variable spatial properties Probab. Eng. Mech. (IF 2.6) Pub Date : 2024-03-15 Vincent Renaud, Marwan Al Heib
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
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Time-dependent kinematic reliability of motion mechanisms with dynamic factors Probab. Eng. Mech. (IF 2.6) Pub Date : 2024-03-12 Xinchen Zhuang, Xin Li, Chang Liu, Tianxiang Yu, Bifeng Song
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
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Efficient metamodel-based importance sampling coupled with single-loop estimation method for parameter global reliability sensitivity analysis Probab. Eng. Mech. (IF 2.6) Pub Date : 2024-03-12 Wanying Yun, Fengyuan Li, Xiangming Chen, Zhe Wang
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
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Effects of limit state data on constructing accurate surrogate models for structural reliability analyses Probab. Eng. Mech. (IF 2.6) Pub Date : 2024-03-12 Nhu Son Doan, Huu-Ba Dinh
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
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Separable Gaussian neural networks for high-dimensional nonlinear stochastic systems Probab. Eng. Mech. (IF 2.6) Pub Date : 2024-03-11 Xi Wang, Siyuan Xing, Jun Jiang, Ling Hong, Jian-Qiao Sun
This paper extends the recently developed method of separable Gaussian neural networks (SGNN) to obtain solutions of the Fokker–Planck–Kolmogorov (FPK) equation in high-dimensional state space. Several challenges when extending SGNN to high-dimensional state space are addressed including proper definition of domain for placing Gaussian neurons and region for data sampling, and numerical integration
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Influence of uncertainties on dynamic properties and responses of human-structure coupled system under bouncing excitations Probab. Eng. Mech. (IF 2.6) Pub Date : 2024-02-28 Dongjun Zeng, Haoqi Wang, Jun Chen
The consideration of inherent uncertainties of the human-structure coupled system during serviceability assessment has become a consensus in recent years. The uncertainties from human body and structures are coupled and propagate together through the human-structure interaction (HSI) effect. However, how each random source affects the system output under rhythmic motion remains unclear and lacks investigation
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Failure probability estimation of dynamic systems employing relaxed power spectral density functions with dependent frequency modeling and sampling Probab. Eng. Mech. (IF 2.6) Pub Date : 2024-02-24 Marco Behrendt, Meng-Ze Lyu, Yi Luo, Jian-Bing Chen, Michael Beer
This work addresses the critical task of accurately estimating failure probabilities in dynamic systems by utilizing a probabilistic load model based on a set of data with similar characteristics, namely the relaxed power spectral density (PSD) function. A major drawback of the relaxed PSD function is the lack of dependency between frequencies, which leads to unrealistic PSD functions being sampled
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A DPIM-based probability analysis framework to obtain railway vehicle vibration characteristics considering the randomness of OOR wheel Probab. Eng. Mech. (IF 2.6) Pub Date : 2024-02-24 Tengfei Wang, Jinsong Zhou, Wenjing Sun, Dao Gong, Kai Zhou, Zhanfei Zhang, Zhixin Liu, Guoshun Li
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
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Random vibration of the point-driven portal and multi-bay planar frames Probab. Eng. Mech. (IF 2.6) Pub Date : 2024-02-22 Richard Bachoo, Isaac Elishakoff
In this study, an analytical model is presented to determine the random response of point-driven portal and multi-bay planar frame structures. Coupling effects between bending and longitudinal deformations are taken into account, with the Timoshenko-Ehrenfest beam theory being applied to model the bending deformations. With the excitation taken as band-limited white noise, expressions are derived for
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Quantitative review of probabilistic approaches to fatigue design in the medium cycle fatigue regime Probab. Eng. Mech. (IF 2.6) Pub Date : 2024-02-21 Elvis Kufoin, Luca Susmel
To quantify the fatigue behaviour of materials, a Wöhler diagram is required. The state of the art shows that, over the years, numerous approaches suitable for determining Wöhler curves have been devised and validated through large fatigue data sets. The variation in experimental fatigue data elicits the use of statistics for analysis and design purposes. By focusing on the medium-cycle fatigue regime
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A method to reduce the sampling variability of time-domain fatigue life by optimizing parameters in Monte Carlo simulations Probab. Eng. Mech. (IF 2.6) Pub Date : 2024-02-20 Hong Sun, Yuanying Qiu, Jing Li, Jin Bai, Ming Peng
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
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Probability distributions for dynamic and extreme responses of linear elastic structures under quasi-stationary harmonizable loads Probab. Eng. Mech. (IF 2.6) Pub Date : 2024-02-20 Zifeng Huang, Michael Beer
The non-stationary load models based on the evolutionary power spectral density (EPSD) may lead to ambiguous structural responses. Quasi-stationary harmonizable processes with non-negative Wigner-Ville spectra are suitable for modeling non-stationary loads and analyzing their induced structural responses. In this study, random environmental loads are modeled as quasi-stationary harmonizable processes
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The modified mesoscopic stochastic fracture model incorporating the random field of Young's modulus for the uniaxial constitutive law of concrete Probab. Eng. Mech. (IF 2.6) Pub Date : 2024-02-17 Yang-Yi Liu, Jian-Bing Chen, Jie Li
Concrete is a multi-phase composite material that exhibits nonlinear and random characteristics in various contexts. The mesoscopic stochastic fracture model (MSFM) was developed to capture the constitutive behaviors of concrete. However, it is still not accurate enough to quantify the randomness of stress-strain curves in the ascending phase, and the variability of the strength might be considerably
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Reliability estimation from two types of accelerated testing considering individual difference and measurement error Probab. Eng. Mech. (IF 2.6) Pub Date : 2024-02-02 Chengqiang Yang, Xiaohui Gu, Zhongmin Xiao
Accelerated testing is a valuable approach for enhancing testing efficiency and reducing time costs, thus playing a crucial role in reliability testing. This paper introduces a novel reliability evaluation method that effectively utilizes both accelerated degradation testing (ADT) and accelerated life testing (ALT) data simultaneously, aiming to comprehensively leverage the reliability information
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Wind speed monitoring using entropy theory and a copula-based approach Probab. Eng. Mech. (IF 2.6) Pub Date : 2024-01-30 Mohammad Nazeri Tahroudi, Yousef Ramezani, Carlo De Michele, Rasoul Mirabbasi
In this study, wind speeds in the Lut desert (Iran) was monitored at the Bam, Tabas and Birjand stations during the period 1973–2020 using entropy theory. The conventional entropy method was improved by considering the interaction between stations by a copula-based approach. Two different methods were examined for this purpose: 1) A trivariate vine copula was used to evaluate the interaction among
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Dimension reduction for constructing high-dimensional response distributions by accounting for unimportant and important variables Probab. Eng. Mech. (IF 2.6) Pub Date : 2024-01-24 Yongyong Xiang, Te Han, Yifan Li, Luojie Shi, Baisong Pan
Probability distributions of responses have been widely used in structural analysis and design because of their complete statistical information. In practice, the dimensionality of input variables could easily reach hundreds or thousands, making it computationally expensive to obtain accurate distributions. In this paper, a generalized most probable point (MPP) method is developed to effectively build
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Topology optimization of bridges under random traffic loading using stochastic reduced-order models Probab. Eng. Mech. (IF 2.6) Pub Date : 2024-01-24 Kaiming Luo, Xuhui He, Haiquan Jing
This paper presents a framework for robust topology optimization of bridges under random traffic loading. Traffic loading is simulated using a stream of random moving loads parameterized by their masses, speeds, directions, and arrival times. The stochastic reduced-order model approach is combined with the equivalent static load method to achieve uncertainty-informed dynamic response topology optimization
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Structural reliability analysis based on probability density evolution method and stepwise truncated variance reduction Probab. Eng. Mech. (IF 2.6) Pub Date : 2024-01-15 Tong Zhou, Tong Guo, You Dong, Yongbo Peng
To address the substantial computational burden associated with probability density evolution method (PDEM) in structural reliability analysis, 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
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A novel general method for simulating a one dimensional random field based on the active learning Kriging model Probab. Eng. Mech. (IF 2.6) Pub Date : 2024-01-13 Wenliang Fan, Shujun Yu, Haoyue Jiang, Xiaoping Xu
Random fields are widely used to represent the uncertainty of some parameters in engineering, and numerous simulation approaches have been developed for Gaussian and non-Gaussian random fields. However, the unified methods among them suffer from low computational accuracy and efficiency or discontinuities in the simulated random fields. Therefore, an easy-to-implement general simulation method based
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Probabilistic analysis of seasonal influence on the prediction of pile bearing capacity by CPT: A case study Probab. Eng. Mech. (IF 2.6) Pub Date : 2023-12-23 Isabela Augusto Silveira, Heraldo Luiz Giacheti, Breno Padovezi Rocha, Caio Gorla Nogueira
In this study, we used a probabilistic approach to evaluate the probability of failure of small diameter bored piles in unsaturated sandy soils. The prediction of the bearing capacity was based on the Cone Penetration Test (CPT). We assessed the seasonal influence by analyzing CPT data from dry and wet seasons. The LCPC and Aoki-Velloso semiempirical methods were deterministically applied, and the
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Random distribution of interphase characteristics on the overall electro-mechanical properties of CNT piezo nanocomposite: Micromechanical modeling and Monte Carlo simulation Probab. Eng. Mech. (IF 2.6) Pub Date : 2023-12-22 M.J. Mahmoodi, M. Khamehchi
A phenomenological study is carried out to speculate the statistical impacts of the CNT/polymer interphase on the overall electro-elastic behavior of piezo-polymer nanocomposites by presenting a full-field micromechanical model. The nanocomposite system consists of carbon nanotube (CNT) and PVDF. Various statistical distributions, including Weibull, log-normal, normal, beta, and uniform distributions
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Effect of interface roughness on the elastic properties of 3D layered media Probab. Eng. Mech. (IF 2.6) Pub Date : 2023-12-21 Tatyana Khachkova, Vadim Lisitsa, Galina Reshetova
In our work, we numerically investigated how the roughness of interfaces between different layers in 3D layered models affects the upscaled and downscaled elastic properties. We considered layered models with random rough interfaces and fixed elastic properties in the background and the middle layer, upscaled them to compute the effective stiffness tensor and then downscaled them to obtain new elastic
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A novel non-intrusive ROM for randomly excited linear dynamical systems with high stochastic dimension using ANN Probab. Eng. Mech. (IF 2.6) Pub Date : 2023-12-21 Chandan Bharti, Debraj Ghosh
Analyzing large stochastic dynamical systems is computationally very expensive. A statistical simulation framework requires invoking the solver multiple times — ranging from thousands to millions. A non-intrusive reduced order model (ROM) serves as a computationally efficient alternative in this framework. Uncertainties in dynamical systems originate from two sources: system parameters and excitation
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Non-stationary probabilistic analysis of non-linear ship roll motion due to modulated periodic and random excitations Probab. Eng. Mech. (IF 2.6) Pub Date : 2023-12-20 Jie Luo, Guo-Kang Er, Vai Pan Iu, Ze-Xin Ren
This paper is about the study on the non-stationary probability density function (PDF) solution of the non-linear ship rolling system under modulated periodic and random excitations. The quadratic and cubic damping terms are introduced to describe the non-linear damping moment and the power terms in state variables are adopted to describe the righting moment in the ship rolling system, respectively
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Gaussian process metamodel and Markov chain Monte Carlo-based Bayesian inference framework for stochastic nonlinear model updating with uncertainties Probab. Eng. Mech. (IF 2.6) Pub Date : 2023-12-19 Ya-Jie Ding, Zuo-Cai Wang, Yu Xin
The estimation of the posterior probability density function (PDF) of unknown parameters remains a challenge in stochastic nonlinear model updating with uncertainties; thus, a novel Bayesian inference framework based on the Gaussian process metamodel (GPM) and the advanced Markov chain Monte Carlo (MCMC) method is proposed in this paper. The instantaneous characteristics (ICs) of the decomposed measurement
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A new active learning method for reliability analysis based on local optimization and adaptive parallelization strategy Probab. Eng. Mech. (IF 2.6) Pub Date : 2023-12-19 Fan Yang, Rui Kang, Qiang Liu, Cheng Shen, Ruijie Du, Feng Zhang
In recent years, an active learning method combining Kriging and Monte Carlo Simulation (AK-MCS), has been developed for calculating the failure probability. However, the original AK-MCS only uses serial computing, which limits its ability to take advantage of distributed computing. Thus, this work introduces a novel adaptive learning approach for reliability analysis by combining local optimization
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P-AK-MCS: Parallel AK-MCS method for structural reliability analysis Probab. Eng. Mech. (IF 2.6) Pub Date : 2023-12-19 Zhao Zhao, Zhao-Hui Lu, Yan-Gang Zhao
In recent years, the active learning reliability method that combines the Kriging model and Monte Carlo simulation (AK-MCS) has emerged as a promising approach due to its computational efficiency and accuracy. However, the commonly used learning functions, such as the expected feasibility function (), function, function, and expected risk function (), can only select one training point at each iteration
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Computing exit location distribution of stochastic dynamical systems with noncharacteristic boundary based on deep learning Probab. Eng. Mech. (IF 2.6) Pub Date : 2023-12-16 Yang Li, Feng Zhao, Jianlong Wang, Shengyuan Xu
Rare events induced by random perturbations are ubiquitous phenomena in natural systems, where the exit location distribution is a significant quantity, and its computation is challenging. In this study, we compute the exit location distribution of stochastic dynamical systems with weak Gaussian noise for a noncharacteristic boundary based on deep learning and large deviation theory. First, we introduce
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Partial safety factor calibration using surrogate models: An application for running safety of ballasted high-speed railway bridges Probab. Eng. Mech. (IF 2.6) Pub Date : 2023-12-16 R. Allahvirdizadeh, A. Andersson, R. Karoumi
Traditionally, regulations employ semi-probabilistic methods with partial safety factors to control design limits. Calibrating these partial safety factors involves estimating the target reliability level and optimizing the partial safety factor values in order to minimize the deviation of the safety index between the considered design scenarios and the target value. This procedure necessitates performing
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Analytical response and Markovianity of systems governed by fractional differential equations driven by [formula omitted]-stable white noise processes Probab. Eng. Mech. (IF 2.6) Pub Date : 2023-12-16 Gioacchino Alotta
This paper investigates the probabilistic response of systems governed by fractional differential equations and forced by -stable white noise processes. The response in terms of scale of the response process is built starting from a finite differences version of the governing equation. An efficient formulation in terms of scale of the response process is obtained for both non-stationary and stationary
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A novel hybrid time-variant reliability analysis method through approximating bound-most-probable point trajectory Probab. Eng. Mech. (IF 2.6) Pub Date : 2023-11-27 Nanzheng Zou, Chunlin Gong, Licong Zhang, Yunwei Zhang, Xiaowei Wang, Chunna Li
In the engineering field, time-variant reliability analysis (TRA) is used to measure the safety level of structures under time-variant uncertainties. Lacking in information or data, some uncertainties cannot be directly quantified as stochastic models, which results in the simultaneous existence of aleatory and epistemic uncertainties in most of problems. In general, stochastic and interval models
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Wind pressure field reconstruction using a variance-extended KSI method: Both deterministic and probabilistic applications Probab. Eng. Mech. (IF 2.6) Pub Date : 2023-11-27 Ning Zhao, Xiaowei Chen, Yi Su, Yan Jiang, Xuewei Wang
Wind tunnel experiment is an essential measure for acquiring wind pressure information on the surface of structures. However, it is hard to acquire the complete wind pressure field information because of the restrictions of the measuring equipment capability or inner space of rigid experimental models. For this reason, this paper proposes a reliable wind pressure field reconstruction method using a
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Sparse polynomial chaos expansion for high-dimensional nonlinear damage mechanics Probab. Eng. Mech. (IF 2.6) Pub Date : 2023-11-24 Esther dos Santos Oliveira, Udo Nackenhorst
Finite Element Simulations in solid mechanics are nowadays common practice in engineering. However, considering uncertainties based on this powerful method remains a challenging task, especially when nonlinearities and high stochastic dimensions have to be taken into account. Although Monte Carlo Simulation (MCS) is a robust method, the computational burden is high, especially when a nonlinear finite
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Surrogate-assisted investigation on influence of epistemic uncertainties on running safety of high-speed trains on bridges Probab. Eng. Mech. (IF 2.6) Pub Date : 2023-11-24 R. Allahvirdizadeh, A. Andersson, R. Karoumi
The operational safety of high-speed trains traversing ballasted bridges is contingent upon the prevention of the ballast destabilization, which can interrupt load transfer from the rail to the bridge. Current design regulations indirectly address this limit-state by specifying a threshold value for the vertical acceleration of the superstructure. This value represents the condition at which the inertial
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Using statistical linearization to optimize a class of semi-active on-off control in a general state space system Probab. Eng. Mech. (IF 2.6) Pub Date : 2023-11-18 Viet Duc La, Ngoc Tuan Nguyen
This paper uses statistical linearization to analyze a state space system controlled by a class of semi-active on-off control. The control input is proportional to a feedback state, while the coefficient of proportionality is switched based on the sign of the product of feedback state and a control state. The explicit simultaneous equations are derived to obtain the system's statistics. The usefulness
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Two-phase optimized experimental design for fatigue limit testing Probab. Eng. Mech. (IF 2.6) Pub Date : 2023-11-17 Lujie Shi, Leila Khalij, Christophe Gautrelet, Chen Shi, Denis Benasciutti
This study proposes an innovative Two-phase method, based on the Langlie method and the D-optimality criterion, to overcome the intrinsic shortcomings of the staircase method used in estimating the fatigue limit distribution. This paper identifies the current challenges and provides an overview of existing solutions, setting the goal of developing an efficient data collection protocol. It further explains
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A novel reliability updating based method for efficient estimation of failure-probability global sensitivity Probab. Eng. Mech. (IF 2.6) Pub Date : 2023-11-14 Jiaqi Wang, Zhenzhou Lu, Lu Wang
Failure-probability (FP) global sensitivity (FP-GS) can measure the average effect of random input on FP, and it is significant in reliability-based design optimization. The key of FP-GS is estimating the conditional FPs on the different realizations of random inputs, which usually requires a time-demanding double-loop structure analysis. This paper originally discovers a reliability updating perspective
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A comparative study of various metamodeling approaches in tunnel reliability analysis Probab. Eng. Mech. (IF 2.6) Pub Date : 2023-11-13 Axay Thapa, Atin Roy, Subrata Chakraborty
Various metamodeling approaches are applied in conjunction with Monte Carlo simulation and or the second moment-based method for reliability analyses of underground tunnels. However, there is no study regarding the suitability of such metamodels for reliability analyses of tunnels. An attempt is made here to make a comparative assessment of different metamodeling approaches for tunnel reliability analysis
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Time-dependent reliability analysis of planar mechanisms considering truncated random variables and joint clearances Probab. Eng. Mech. (IF 2.6) Pub Date : 2023-11-10 Yufan Cheng, Xinchen Zhuang, Tianxiang Yu
The dimension variables and joint clearances are key factors affecting the motion accuracy of a mechanism. The existing time-dependent reliability analysis methods usually assume that the dimension variables follow ideal distribution, ignoring the truncation restrictions due to production and manufacturing. In addition, improper handling of the correlation between joint clearance variables leads to
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Statistical analysis of wind load probabilistic models considering wind direction and calculation of reference wind pressure values in Liaoning Province, China Probab. Eng. Mech. (IF 2.6) Pub Date : 2023-11-08 Jiaxu Li, Ming Liu, Xu Yan, Qianting Yang
Wind pressure serves as the fundamental base for architectural design, particularly the reference wind pressure value, which affects the reliability and economy of high-rise, towering, and large-span structures. To facilitate the calculation of reference wind pressure values for design reference periods, load codes from various countries offer probabilistic distribution models for wind speed or reference
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Random walks and moving boundaries: Estimating the penetration of diffusants into dense rubbers Probab. Eng. Mech. (IF 2.6) Pub Date : 2023-11-02 Surendra Nepal, Magnus Ögren, Yosief Wondmagegne, Adrian Muntean
For certain materials science scenarios arising in rubber technology, one-dimensional moving boundary problems with kinetic boundary conditions are capable of unveiling the large-time behavior of the diffusants penetration front, giving a direct estimate on the service life of the material. Driven by our interest in estimating how a finite number of diffusant molecules penetrate through a dense rubber
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A wavelet transform based stationary transformation method for estimating the extreme value of the non-stationary wind speeds Probab. Eng. Mech. (IF 2.6) Pub Date : 2023-11-04 Jinhua Li, Desen Zhu, Liyuan Cao, Chunxiang Li
The rational estimation of extreme wind speeds is crucial. As reported, in the Chinese area, the measured wind speed variation from different meteorological observatories shows a general non-stationary trend in the past 40 years due to urbanization factors or temperature variation. Thus, the non-stationary extreme value analysis (TNGEV) of the measured wind speed is required. However, TNGEV is relatively
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A novel method for global reliability sensitivity analysis by adaptive bivariate multiplicative dimensional reduction integral Probab. Eng. Mech. (IF 2.6) Pub Date : 2023-10-28 Xiaomin Wu, Zhenzhou Lu, Ning Wei
The existing fractional moment-constrained maximum entropy method can efficiently analyse global reliability sensitivity (GRS). However, this method estimates the fractional moments by the univariate multiplicative dimensional reduction integral (UM-DR-I) that generates a significant error in GRS estimation, which may result from ignoring the interactions between input variables. Moreover, due to the
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Reliability evaluation of folding wing mechanism deployment performance based on improved active learning Kriging method Probab. Eng. Mech. (IF 2.6) Pub Date : 2023-10-28 Zhiqiang Zhao, Liyang Xie, Bingfeng Zhao, Jiaxin Song, Lei Wang
The folding wing mechanism is essential for the normal flight of an aircraft, and it is necessary to evaluate the reliability of its deployment performance to ensure the long-term stable operation of the aircraft. In this study, a deployment dynamic model of the folding wing mechanism was developed and solved, and the essential factors affecting the deployment performance of the folding wing were analysed
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Uncertainty and bias in fragility estimates by intensifying artificial accelerations Probab. Eng. Mech. (IF 2.6) Pub Date : 2023-10-10 Mohammad Amin Hariri-Ardebili, Siamak Sattar
To comply with the objectives of Performance-Based Earthquake Engineering and gain a comprehensive understanding of a structure’s seismic response, the utilization of multiple ground motion records selected or scaled at various hazard levels is often necessary. Incremental Dynamic Analysis (IDA) and cloud analysis methods represent wide-range probabilistic approaches employed for this purpose. However
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Counter-checking uncertainty calculations in Bayesian operational modal analysis with EM techniques Probab. Eng. Mech. (IF 2.6) Pub Date : 2023-10-13 Xinda Ma, Siu-Kui Au
Bayesian operational modal analysis makes inference about the modal properties (e.g., natural frequency, damping ratio) of a structure using ‘output-only’ ambient vibration data. With sufficient data in applications, the posterior probability density function (PDF) of modal properties can be approximated by a Gaussian PDF, whose covariance matrix is given by the inverse of the Hessian of negative log-likelihood
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Pattern-moving-based dynamic description for a class of nonlinear systems using the generalized probability density evolution Probab. Eng. Mech. (IF 2.6) Pub Date : 2023-10-05 Cheng Han, Zhengguang Xu
For a class of complex nonlinear systems governed by statistical laws, a new pattern-moving-based description method using the generalized probability density evolution was proposed. Due to the fact that the states and outputs of such systems typically exhibit a probability distribution rather than deterministic variables, and the outputs obtained by the system under similar operating conditions typically
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Polynomial response surface-based transformation function for the performance improvement of low-fidelity models for concrete gravity dams Probab. Eng. Mech. (IF 2.6) Pub Date : 2023-10-07 Rodrigo José de Almeida Torres Filho, Rocio L. Segura, Patrick Paultre
The behavior of concrete gravity dams under seismic loading is a complex engineering problem dependent on a wide range of variables. Probabilistic methods can be used to evaluate the capacity of an individual or a portfolio of dams to withstand seismic events. However, due to the high number of re-evaluations required by such methods, simplified models that may not fully capture the complexity of the
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Data-driven polynomial chaos-interval metamodel for dynamics and reliability analysis under hybrid uncertainty Probab. Eng. Mech. (IF 2.6) Pub Date : 2023-10-03 Xiang Guo, Yanfei Jin
As the complexity of engineering systems increases, the probability distribution information of model parameters becomes limited owing to the influence of environmental or natural phenomena. To solve this problem, a data-driven polynomial chaos-interval metamodel (DDPCIM) is proposed herein to quantify random and interval uncertainties in an engineering system. The orthogonal polynomial basis corresponding
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Reliability assessment of civil structures with incomplete probability distribution information Probab. Eng. Mech. (IF 2.6) Pub Date : 2023-09-29 Pinghe Ni, Zhishen Yuan, Qiang Han, Xiuli Du, Jinlong Fu
The probability distribution of random variables is usually incomplete owing to a limited amount of data, making it a challenging problem for structural reliability assessments. This paper presents a novel reliability analysis method for civil structures with incomplete data. The probability distributions of the random variables were estimated using an improved Bayesian inference method. In the proposed
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Propagation of material uncertainty in modal parameters and its influence in damage quantification of shear buildings Probab. Eng. Mech. (IF 2.6) Pub Date : 2023-09-27 Saranika Das, Koushik Roy
Structural health monitoring (SHM) plays a crucial role in post-disaster mitigation in investigating the serviceability of an existing structure. The physical properties, namely mass density, cross-sectional area, Poisson’s ratio, modulous of elasticity, etc., vary spatially throughout a real system. These uncertainties propagate in the modal parameters involved in SHM techniques. In this study, the
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Prediction of stochastic responses of vehicle running through the multi-spans bridge based on an optimized method Probab. Eng. Mech. (IF 2.6) Pub Date : 2023-09-27 Siyu Zhu, Rui Yi, Xinyu Xu, Tianyu Xiang
The dynamic response of a vehicle-bridge system (VBS) has been a key problem in assessments of train-running safety on bridges, ultimately requiring reliability analyses owing to uncertain parameters and random rail irregularities. To improve the computational efficiency and promote the development of a reliability analysis for VBS, this paper proposes a novel approach incorporating a stochastic pseudo-excitation
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Robust scalable initialization for Bayesian variational inference with multi-modal Laplace approximations Probab. Eng. Mech. (IF 2.6) Pub Date : 2023-09-25 Wyatt Bridgman, Reese E. Jones, Mohammad Khalil
Predictive modeling typically relies on Bayesian model calibration to provide uncertainty quantification. Variational inference utilizing fully independent (“mean-field”) Gaussian distributions are often used as approximate probability density functions. This simplification is attractive since the number of variational parameters grows only linearly with the number of unknown model parameters. However