Article
Mathematics, Applied
Biswarup Bhattacharyya
Summary: Global reliability sensitivity analysis determines the impact of uncertain parameters on system failure probability. This study proposes a sparse polynomial chaos expansion (PCE) model based on variational Bayesian (VB) inference to address the limitations of traditional Monte Carlo simulation (MCS) approach. The VB inference computes PCE coefficients and selects important terms using automatic relevance determination (ARD). The sparse PCE model is compared with MCS and least angle regression (LARS)-based PCE model in numerical examples, showing superior results with fewer model evaluations and higher accuracy in coefficient estimation.
JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS
(2023)
Article
Engineering, Civil
Biswarup Bhattacharyya
Summary: An efficient surrogate model, SVB-PCE, is proposed for accurate computation of failure probability and reliability index, achieving accurate assessment with fewer model evaluations. The model reduces computational cost by capturing the important terms in the polynomial bases using the automatic relevance determination (ARD) algorithm.
Article
Engineering, Mechanical
A. A. Basmaji, A. Fau, J. H. Urrea-Quintero, M. M. Dannert, E. Voelsen, U. Nackenhorst
Summary: This study introduces a new hp-adaptive variant of multi-element polynomial chaos expansion, which significantly reduces computational costs, improves efficiency and accuracy in high-dimensional problems. Through comparative studies on elastoplasticity in nonlinear structural analysis, it demonstrates the superiority of this method.
PROBABILISTIC ENGINEERING MECHANICS
(2022)
Article
Engineering, Mechanical
Kamaljyoti Nath, Anjan Dutta, Budhaditya Hazra
Summary: This study introduces an Iterative Polynomial Chaos method to solve structural mechanics problems, reducing the curse of dimensionality by iteratively solving problems with smaller sizes of PC expansions. By combining Polynomial Dimensional Decomposition, the method achieves higher computational efficiency and converged solutions.
PROBABILISTIC ENGINEERING MECHANICS
(2021)
Article
Engineering, Multidisciplinary
Yu Zhang, Jun Xu
Summary: This paper presents an efficient reliability analysis method based on contribution-degree analysis, an exact dimension-reduction model, and polynomial chaos expansion. By decomposing the original performance function and reconstructing component functions, it achieves a good trade-off of efficiency and accuracy for reliability analysis.
COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
(2021)
Article
Engineering, Multidisciplinary
Anna Kucerova, Jan Sykora, Petr Havlasek, Daniela Jaruskova, Milan Jirasek
Summary: This study investigates Bayesian inference with material model parameters for a damage-plastic model of concrete. It evaluates the experimental setup for estimating material parameters and demonstrates the efficiency of numerical tools for probabilistic identification. The paper provides detailed descriptions for the identification procedure and offers strategies for efficient surrogate construction.
COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
(2023)
Article
Engineering, Mechanical
Zesheng Zhang, Zhiping Qiu
Summary: This study proposes a novel uncertainty analysis method for structural fatigue life prediction, considering hybrid uncertainties. The method incorporates PCE, SCC collocation technique, and LHS method to establish a fatigue reliability analysis for unknown coefficients estimation. Validation of the proposed method's feasibility and efficiency is demonstrated through three numerical examples.
INTERNATIONAL JOURNAL OF FATIGUE
(2021)
Article
Engineering, Mechanical
H. C. Xie, D. H. Liao
Summary: This paper proposes a new time-dependent reliability analysis method based on a two-level meta-models technique for problems with interval variables, to efficiently obtain upper and lower bounds of reliability indexes of structures within a given design lifetime period. The method includes steps such as parameter transformation, polynomial chaos expansion, and meta-model construction, and its effectiveness is verified through three numerical examples.
PROBABILISTIC ENGINEERING MECHANICS
(2022)
Article
Engineering, Mechanical
Jing Qian, You Dong
Summary: Sparse polynomial chaos expansion is a powerful tool for efficient uncertainty quantification and sensitivity analysis in emulating the stochastic model output. An algorithm for efficient computation of sparse PCE is proposed in this study, which integrates acceleration techniques to improve computational efficiency and predictive performance.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2022)
Article
Computer Science, Interdisciplinary Applications
Tao Yang, Jin-Feng Zou, Qiujing Pan
Summary: The study combines Voronoi-based exploration and local linear approximation-based exploitation to determine experimental design samples for PCE constructions, and proposes a sequential sampling scheme to choose the most informative samples, improving computational efficiency and accuracy.
COMPUTERS AND GEOTECHNICS
(2021)
Article
Engineering, Civil
Wanxin He, Gang Zhao, Gang Li, Ye Liu
Summary: This study proposes an adaptive sparse learning method based on automatic relevance determination (ARD) and Bayesian model averaging (BMA) to solve the challenges faced by Polynomial Chaos Expansion (PCE) when dealing with high-dimensional problems. The proposed method constructs a sparse PCE model using the framework of sparse Bayesian learning, reduces the size of candidate PCE bases using a dimension-reduction method (DRM) that considers high-dimensional components adaptively, and prunes the candidate PCE bases using a novel ARD method based on analytical B-LASSO. The results demonstrate that the proposed method is a good choice for stochastic uncertainty quantification (UQ) under limited design samples.
Article
Engineering, Mechanical
Louis Jezequel, Hugo de Filippis, Alexy Mercier
Summary: This study presents an original approach to obtain a closed-form solution for a class of random dynamical system by introducing a new class of modes called eigenmodes of perturbation and resolving periodic systems. It provides a physical interpretation of the damping effect observed on the mean response of linear random dynamical systems by referring to the propagation of waves in a fictitious semiperiodic system associated with the eigenmodes of perturbation.
JOURNAL OF ENGINEERING MECHANICS
(2022)
Article
Engineering, Multidisciplinary
Bei-Yang Zhang, Yi-Qing Ni
Summary: This paper proposes a novel adaptive modelling framework for sparse polynomial chaos expansion. It automatically determines the truncation degree and training sample set, and alleviates the curse of dimensionality issue in polynomial chaos expansion. The framework incorporates an adaptive basis selection strategy, a sequential sampling strategy and a sparse representation method to improve the precision and convergence rate of the model.
APPLIED MATHEMATICAL MODELLING
(2023)
Article
Engineering, Civil
YiFei Li, Hoang-Le Minh, S. Khatir, Thanh Sang-To, Thanh Cuong-Le, MaoSen Cao, Magd Abdel Wahab
Summary: A novel method for structural damage identification is proposed, which combines a surrogate modelling technique and a hybrid optimization strategy. The method utilizes a sparse polynomial chaos expansion model as a cost-effective alternative to computationally expensive finite element models, greatly improving optimization efficiency. Comparative results show that the proposed method outperforms seven other optimization algorithms for benchmark functions. Case studies demonstrate the efficiency and reliability of the proposed method for finite element model updating and structural damage identification.
ENGINEERING STRUCTURES
(2023)
Article
Computer Science, Interdisciplinary Applications
Jacqueline Wentz, Alireza Doostan
Summary: In this study, a method for quantifying uncertainty in high-dimensional PDE systems with random parameters is proposed. The method utilizes a generative model to approximate the coefficients of the solutions. The approach outperforms sparsity promoting methods at small sample sizes in the examined high-dimensional problems.
JOURNAL OF COMPUTATIONAL PHYSICS
(2023)
Article
Engineering, Civil
A. R. Ibrahim, D. A. Makhloof
Summary: The unavoidable heterogeneity in the mechanical characteristics of concrete is crucial to consider in the design of high-rise buildings. This study investigates the spatial variability of material properties and proposes a framework to capture the stochastic response and assess the reliability of structural components. The results demonstrate the importance of accounting for material uncertainty in ensuring the safety of high-rise buildings.
Article
Engineering, Civil
Zhiqiang Wan
Summary: This paper emphasizes the importance of global sensitivity analysis for stochastic dynamical systems with multiple uncertain parameters and proposes a global sensitivity index suitable for this purpose. The research findings demonstrate that the proposed approach exhibits high efficiency and accuracy.
Article
Engineering, Civil
Max Ehre, Iason Papaioannou, Daniel Straub
Summary: Reliability sensitivity analysis is a method to measure the influence of uncertain input parameters on the probability of failure in a system. Statistically dependent inputs pose challenges in computing and interpreting sensitivity indices. This study introduces a separation of effects between the probabilistic model and computational model to compute the independent and full contributions of all inputs. By using hierarchically structured isoprobabilistic transformations, the full set of variance-based sensitivity indices can be computed with a single set of failure samples obtained from a rare event estimation method.
Article
Engineering, Civil
Ze Yuan, Quanwang Li, Kefei Li
Summary: This paper proposes a method to determine a measurement plan for durability assessment of concrete structures, by calibrating the models using Bayesian updating and linear fitting in order to achieve the required accuracy. The paper establishes probabilistic time-dependent models for surface chloride concentration and chloride diffusion coefficient, and discusses the key factors affecting the accuracy of the models.
Article
Engineering, Civil
Ziqi Wang
Summary: This study addresses the fundamental limitation of equivalent linearization methods in nonlinear random vibration analysis, proposing a method to construct an estimator that converges on the nonlinear system solution using a limited number of nonlinear system simulations and optimizing the equivalent linear system to approach the nonlinear system solution quickly, especially for rare event probabilities.
Article
Engineering, Civil
Min Li, Srinivasan Arunachalam, Seymour M. J. Spence
Summary: This paper presents a multi-fidelity approach for computing small failure probabilities in engineering systems. By integrating information from different levels of model fidelity, the required number of high-fidelity model runs is reduced while maintaining accuracy in estimating failure probabilities.
Article
Engineering, Civil
Aritra Chatterjee, Trisha Chakravorty, Baidurya Bhattacharya
Summary: This paper presents a methodology to determine the system reliability of commonly used steel moment connections that have been designed according to current element based procedures. A general expression is derived to modify element resistance factors and meet specified system reliability targets.
Article
Engineering, Civil
Yuanqin Tao, Kok-Kwang Phoon, Honglei Sun, Jianye Ching
Summary: This study derives theoretical and approximate variance reduction functions (VRFs) for a potential inclined slip line in a spatially variable soil. The study investigates one-dimensional (1D) VRFs and proposes approximate VRFs for the one-dimensional Whittle-Mate 'rn (WM) model and the one-dimensional cosine Whittle-Mate 'rn (CosWM) model. The paper also derives theoretical scales of fluctuation and VRFs for commonly used two-dimensional autocorrelation models and proposes general approximations for the VRF over an inclined line.
Article
Engineering, Civil
Amir H. Khodabakhsh, Seid H. Pourtakdoust
Summary: The Fokker-Plank-Kolmogorov (FPK) equation is a crucial model for understanding and improving the performance of engineering systems. However, its solution is challenging due to its high dimensionality. This study introduces FPK-DP Net, a physics-informed network that can effectively solve high-dimensional FPK equations and demonstrates its applicability and accuracy through numerical implementations on benchmark problems.
Article
Engineering, Civil
Wouter Jan Klerk, Vera van Bergeijk, Wim Kanning, Rogier Wolfert, Matthijs Kok
Summary: This paper examines the reliability of flood defence systems under shock-based degradation and compares different maintenance concepts. The results show that the current maintenance concept fails to meet the reliability requirements for revetment failure. Additional inspections and targeted interventions can significantly reduce total cost and improve the robustness of flood defence systems.
Article
Engineering, Civil
Xuejing Wang, Xin Ruan, Joan R. Casas, Mingyang Zhang
Summary: This paper proposes a probabilistic Gaussian mixture model for simulating heavy vehicle scenarios on long-span bridges under free-flow conditions. The study utilizes a non-stationary Poisson process to simulate the uneven occurrence of heavy vehicles in different lanes, considering the correlation of gross vehicle weights within close range. The results show that the correlation and stationarity of vehicle distribution location significantly affect the structural responses.
Article
Engineering, Civil
Jianhua Xian, Ziqi Wang
Summary: This study presents an importance sampling formulation based on adaptively relaxing parameters, providing a unified framework for various existing variance reduction techniques and laying the foundation for creating new importance sampling strategies. It proposes two importance sampling strategies for low-dimensional and high-dimensional problems, which are crucial for fragility analysis in performance-based engineering.
Article
Engineering, Civil
Chi Xu, Jun Chen, Jie Li
Summary: This study proposes a new algorithm to determine the probability distributions of the live load duration and compares the results with Monte Carlo simulation. The algorithm allows the exact determination of design live loads based on a predefined exceeding probability, providing guidance for engineering design.
Article
Engineering, Civil
Hongyuan Guo, You Dong, Emilio Bastidas-Arteaga
Summary: This paper presents a general reliability assessment framework for RC structures based on a Mixed Bayesian network, taking into account environmental parameters, chloride transport, and concrete crack inspection. The case study reveals that early detection of large cracks may lead to an overestimation of failure probability by about 500%.
Article
Engineering, Civil
Changle Peng, Cheng Chen, Tong Guo, Weijie Xu
Summary: Reliability Analysis (RA) is critical in structural design and performance evaluation. This study proposes a novel learning function, SEUR, for surrogate model-assisted RA to improve efficiency and accuracy. The SEUR function is demonstrated to be more effective and efficient in dealing with nonlinear problems, small probabilities, and complex limit states.