Article
Engineering, Civil
Jia-Hua Yang, Heung-Fai Lam, Yong-Hui An
Summary: The paper proposes a new two-phase adaptive MCMC method to address the problem of determining the posterior probability density function (PDF) in Bayesian model updating. By using a parameter-space search algorithm and a weighted MCMC algorithm, samples in the regions of high probability can be generated adaptively without going through computationally demanding multiple levels.
ENGINEERING STRUCTURES
(2022)
Article
Engineering, Mechanical
Sifeng Bi, Michael Beer, Scott Cogan, John Mottershead
Summary: This paper introduces the theoretic framework of stochastic model updating, covering critical aspects such as model parameterisation, sensitivity analysis, surrogate modelling, test-analysis correlation, and parameter calibration. It emphasizes on uncertainty analysis and extends model updating to the stochastic domain by quantifying uncertainties and treating model parameters as random variables with imprecise probabilities. The paper elaborates on two key aspects, forward uncertainty propagation and inverse parameter calibration, and introduces techniques such as P-box propagation, statistical distance-based metrics, Markov chain Monte Carlo sampling, and Bayesian updating. The technical framework is demonstrated through solving challenges and benchmark testbeds, encouraging readers to reproduce the results and providing further directions for stochastic model updating with uncertainty treatment perspectives.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2023)
Article
Engineering, Mechanical
Adolphus Lye, Alice Cicirello, Edoardo Patelli
Summary: This tutorial paper reviews the use of advanced Monte Carlo sampling methods in Bayesian model updating for engineering applications, introducing different methods and comparing their performance. Three case studies demonstrate the advantages and limitations of these sampling techniques in parameter identification, posterior distribution sampling, and stochastic identification of model parameters.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2021)
Article
Engineering, Civil
Shubham Baisthakur, Arunasis Chakraborty
Summary: The study experimentally identifies the load rating of a steel truss bridge using an improved Bayesian model updating algorithm, sequentially updating the initial element model to match the bridge's static and dynamic characteristics. The updated model serves as a digital twin to predict load-carrying capacity and performance under proof or design load, incorporating in-situ conditions and aiding in risk reduction. The efficiency of the improved HMC-based algorithm is demonstrated with limited sensor data, showing potential for adoption in other existing bridges.
JOURNAL OF CIVIL STRUCTURAL HEALTH MONITORING
(2021)
Article
Computer Science, Artificial Intelligence
Pieter Van Molle, Tim Verbelen, Bert Vankeirsbilck, Jonas De Vylder, Bart Diricx, Tom Kimpe, Pieter Simoens, Bart Dhoedt
Summary: The paper highlights the limitations of conventional neural networks in capturing uncertainty and introduces Bayesian techniques such as Monte Carlo dropout. The authors propose a novel method based on the overlap of output distributions of different classes to better approximate inter-class output confusion. They demonstrate the advantages of their approach using benchmark datasets and skin lesion classification.
NEURAL COMPUTING & APPLICATIONS
(2021)
Article
Engineering, Industrial
Zeyu Wang, Abdollah Shafieezadeh
Summary: This paper presents a new approach to overcome the computational cost problem of Bayesian updating for complex computational models. It decomposes the updating problem into a set of sub-reliability problems with uncertain failure thresholds, enabling precise identification of intermediate failure thresholds and training of surrogate models. The proposed method reduces computational costs significantly while maintaining high accuracy.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2023)
Article
Engineering, Mechanical
Lisha Zhu, Xianzhen Huang, Cong Yuan, Zunling Du
Summary: This paper investigates how to integrate experimental or monitoring data into reliability analysis using Bayesian updating approach, establishing a theoretical model and inference method, and proposing a second-order reliability method. The validity of the proposed framework is demonstrated through numerical examples.
JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY
(2022)
Article
Geosciences, Multidisciplinary
Hongbo Zhao, Bingrui Chen, Shaojun Li, Zhen Li, Changxing Zhu
Summary: The study introduces a system of Bayesian inferences to update mechanical parameters of a rock tunnel model dynamically using monitored field data. The regular supplementation of monitoring data greatly enhances the performance of Bayesian inferences in determining the mechanical parameters of the surrounding rock mass in a tunnel model.
GEOSCIENCE FRONTIERS
(2021)
Article
Engineering, Industrial
Robert Millar, Hui Li, Jinglai Li
Summary: In many engineering systems, the performance or reliability is characterized by a scalar variable. The distribution of this variable is important for uncertainty quantification in various applications. Standard Monte Carlo simulations are often used but struggle to efficiently estimate the tail of the distribution. The Multicanonical Monte Carlo method provides an adaptive importance sampling scheme, where samples are drawn from a nonstandard importance sampling distribution using Markov chain Monte Carlo (MCMC). However, MCMC is inherently serial and difficult to parallelize. In this paper, we propose a new approach that uses the Sequential Monte Carlo sampler for parallel implementation and demonstrate its competitive performance with mathematical and practical examples.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2023)
Article
Construction & Building Technology
Dan Li, Jian Zhang
Summary: This paper explores a Bayesian approach with unscented transform for stochastic model updating, which selects a minimal set of sampling points to represent the PDF of model parameters effectively exploring the distribution of measurements and updating model parameters and associated uncertainties. Numerical simulation of a reinforced concrete beam and updating a real-world cable-stayed bridge FE model demonstrate that the proposed approach can achieve similar model updating performance as MCMC methods.
STRUCTURAL CONTROL & HEALTH MONITORING
(2022)
Article
Engineering, Geological
Liang Han, Lin Wang, Wengang Zhang, Zhixiong Chen
Summary: This study presented a database of UCS from four sites in the Bukit Timah Granite formation in Singapore, and used Bayesian method and MCMC algorithm to quantitatively evaluate the uncertainties of statistical characteristics of UCS. The results showed significant statistical uncertainties of the three statistical characteristics of BTG rocks, which somewhat rely on the selection of basic parameters and autocorrelation function classes.
GEORISK-ASSESSMENT AND MANAGEMENT OF RISK FOR ENGINEERED SYSTEMS AND GEOHAZARDS
(2022)
Article
Engineering, Chemical
Yota Yamamoto, Tomoyuki Yajima, Yoshiaki Kawajiri
Summary: A sequential Monte Carlo (SMC) parameter estimation method was developed for chromatographic processes to rigorously estimate parameter uncertainty, showing higher efficiency compared to existing methods and reducing time and effort for experimental data analysis.
CHEMICAL ENGINEERING RESEARCH & DESIGN
(2021)
Article
Engineering, Civil
Chen Fang, Hong-Jun Liu, Heung-Fai Lam, Mujib Olamide Adeagbo, Hua-Yi Peng
Summary: The Bayesian model updating framework is a reliable method for constructing high-fidelity finite element models and provides a practical solution for efficient model updating of large-scale civil engineering structures. The framework was applied to update the finite element model of the Ting Kau Bridge in Hong Kong using measured modal parameters. The results demonstrate that the framework accurately updated the finite element model and can facilitate structural health monitoring of large-scale civil engineering structures.
ENGINEERING STRUCTURES
(2022)
Article
Computer Science, Interdisciplinary Applications
Mario Miguel Valero, Lluis Jofre, Ricardo Torres
Summary: Predictions of wildfire behavior are often uncertain, with modeling uncertainties largely unquantified in the literature due to computing constraints. However, new multifidelity techniques show promise in overcoming these limitations, as demonstrated in this study's exploration of their applicability to wildland fire spread prediction. The study achieved notable speedups in performance compared to standard methods, allowing for the quantification of uncertainties and sensitivity analysis in a cost-effective manner.
ENVIRONMENTAL MODELLING & SOFTWARE
(2021)
Article
Engineering, Marine
Pengfei Xu, Jianyun Chen, Jing Li, Shuli Fan, Qiang Xu
Summary: Digital twins are considered as promising tools for offshore wind turbines in real-time updating, optimized design, intelligent operation, and maintenance. This paper proposes a Bayesian updating framework based on Transitional Markov Chain Monte Carlo (TMCMC) to construct the desired virtual model of monopile offshore wind turbines to address this difficult problem. The proposed method utilizes measurements from physical entity monitoring and finite element model data to estimate the posterior distribution of uncertain parameters. The results demonstrate the effectiveness and robustness of the Bayesian updating method in updating the finite element model and handling measurement noise.
Article
Computer Science, Interdisciplinary Applications
Jinwoo Jang, Yong Yang, Andrew W. Smyth, Dave Cavalcanti, Rohit Kumar
JOURNAL OF COMPUTING IN CIVIL ENGINEERING
(2017)
Article
Engineering, Mechanical
Jinwoo Jang, Andrew W. Smyth
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2017)
Article
Engineering, Mechanical
Daniel T. Bartilson, Jinwoo Jang, Andrew W. Smyth
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2019)
Article
Construction & Building Technology
Jinwoo Jang, Andrew W. Smyth
STRUCTURAL CONTROL & HEALTH MONITORING
(2020)
Article
Construction & Building Technology
Daniel T. Bartilson, Jinwoo Jang, Andrew W. Smyth
STRUCTURAL CONTROL & HEALTH MONITORING
(2020)
Article
Engineering, Mechanical
Daniel T. Bartilson, Jinwoo Jang, Andrew W. Smyth
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2020)
Article
Engineering, Civil
Patrick Alrassy, Jinwoo Jang, Andrew W. Smyth
Summary: This paper presents a novel map-matching algorithm that integrates in-vehicle data with trajectory data to improve efficiency and accuracy of the algorithm. The algorithm combines probabilistic and weight-based frameworks, with adaptive features to reflect variations in GPS noise levels. It has been validated to be robust, with an accuracy of 97.45%, especially in scenarios where map data are denser and GPS noise is higher.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Chemistry, Analytical
Ali Hashemi, Jinwoo Jang, Shahrokh Hosseini-Hashemi
Summary: A smart active vibration control system with piezoelectric actuators and a linear quadratic regulator controller is proposed for controlling the transverse deflections of wind turbine blades. By mapping the blade to an Euler-Bernoulli beam and solving analytical vibration solutions, the system accurately predicts the blade's vibration and dynamic responses. The system effectively suppresses vibration peaks and controls the maximum flap-wise displacement of the blade's tip, demonstrating its significant performance.
Proceedings Paper
Engineering, Electrical & Electronic
Jinwoo Jang, Andrew W. Smyth, Yong Yang, Dave Cavalcanti
2015 IEEE CONFERENCE ON COMPUTER COMMUNICATIONS WORKSHOPS (INFOCOM WKSHPS)
(2015)