Sifeng Bi

Sifeng Bi
University of Strathclyde · Department of Mechanical and Aerospace Engineering

Doctor of Engineering

About

46
Publications
11,350
Reads
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376
Citations
Introduction
Dr. Sifeng Bi is currently an Alexander von Humboldt research fellow in the Institute for Risk and Reliability, Leibniz Universität Hannover, Germany. His research topics are uncertainty quantification, stochastic model updating and validation, especially in the application of vibroacoustics and complex structural dynamics. In particular, he focuses on probabilistic techniques such as advanced Monte Carlo simulation, approximate Bayesian computation, and global sensitivity analysis. His research also includes experimental modal analysis, noise and vibration controlling, decoupling of vibroacoustics, and damping identification with the consideration of uncertainties.
Additional affiliations
December 2019 - present
Beijing Institute of Technology
Position
  • Professor (Associate)
April 2017 - November 2019
Leibniz Universität Hannover
Position
  • Fellow
September 2015 - March 2017
Institut FEMTO-ST
Position
  • PostDoc Position

Publications

Publications (46)
Article
Full-text available
Test-analysis comparison metrics are mathematical functions that provide a quantitative measure of the agreement (or lack thereof) between numerical predictions and experimental measurements. While calibrating and validating models, the choice of a metric can significantly influence the outcome, yet the published research discussing the role of met...
Article
Full-text available
The Bhattacharyya distance is a stochastic measurement between two samples and taking into account their probability distributions. The objective of this work is to further generalize the application of the Bhattacharyya distance as a novel uncertainty quantification metric by developing an approximate Bayesian computation model updating framework,...
Article
Full-text available
The system identification technology is essentially an inverse procedure, starting from the experimentally measured response, to construct mass, stiffness, and damping matrices of the structure. However, the measurement inevitably contains uncertainties, which significantly impact the identified system characteristics, especially for damping terms....
Article
This paper is dedicated to exploring the NASA Langley Challenge on Optimization under Uncertainty by proposing a series of approaches for both forward and inverse treatment of uncertainty propagation and quantification. The primary effort is placed on the categorization of the subproblems as to be forward or inverse procedures, such that dedicated...
Article
One of the key challenges of uncertainty analysis in model updating is the lack of experimental data. The definition of an appropriate uncertainty quantification metric, which is capable of measuring as sufficient as possible information from the limited and sparse experimental data, is significant for the outcome of model updating. This work is de...
Conference Paper
Full-text available
Assessing seismic performance of existing structures after strong earthquake events is essential to identify potentially unsafe structures and to schedule repairs or retrofitting. For this purpose, a well calibrated model of the structure of interest should be established based on a very limited number of seismic response data, by considering not o...
Article
Full-text available
This work proposes a distribution-free stochastic model updating framework to calibrate the joint probabilistic distribution of the multivariate correlated parameters. In this framework, the marginal distributions are defined as the staircase density functions and the correlation structure is described by the Gaussian copula function. The first fou...
Article
As a classical technology, Model Updating has been developed for nearly 50 years to calibrate the parameters or the numerical model itself, such as to tune its prediction as close as possible to experimental measurements. Industries have benefitted from a more precise model, which further promotes the application of numerical simulation technologie...
Article
The reliable prediction of pedestrian-induced vibration is essential for vibration serviceability assessment and further vibration mitigation design of footbridges. The response of the footbridge is governed by not only the structure dynamic model but also the crowd-induced load, which naturally involves randomness and uncertainty. It is consequent...
Article
Full-text available
This work proposes a novel methodology to fulfil the challenging expectation in stochastic model updating to calibrate the probabilistic distributions of parameters without any assumption about the distribution formats. To achieve this task, an approximate Bayesian computation model updating framework is developed by employing staircase random vari...
Chapter
Full-text available
This chapter presents the technique route of model updating in the presence of imprecise probabilities. The emphasis is put on the inevitable uncertainties, in both numerical simulations and experimental measurements, leading the updating methodology to be significantly extended from deterministic sense to stochastic sense. This extension requires...
Article
The spatial correlation of a multidimensional force produced by different aircraft components is an important factor that affects the structural vibration of aircraft. To predict the structural response by external forces with a certain phase difference, determined by the spatial correlation of external forces, an analytical method based on structu...
Article
Full-text available
In this study, a two-step approximate Bayesian computation (ABC) updating framework using dynamic response data is developed. In this framework, the Euclidian and Bhattacharyya distances are utilized as uncertainty quantification (UQ) metrics to define approximate likelihood functions in the first and second steps, respectively. A new Bayesian infe...
Article
Full-text available
Uncertainty quantification metrics have a critical position in inverse problems for dynamic systems as they quantify the discrepancy between numerically predicted samples and collected observations. Such metric plays its role by rewarding the samples for which the norm of this discrepancy is small and penalizing the samples otherwise. In this paper...
Article
Full-text available
The Bhattacharyya distance has been developed as a comprehensive uncertainty quantification metric by capturing multiple uncertainty sources from both numerical predictions and experimental measurements. This work pursues a further investigation of the performance of the Bhattacharyya distance in different methodologies for stochastic model updatin...
Preprint
Full-text available
In practical engineering, the presence of dependent evidence is not rare due to various imperfections. Misuse of such information in reliability analysis will lead to conflicting or even erroneous results. In this paper, we propose a Bayesian reliability approach for complex systems with dependent life metrics. Notions such as explicit evidence and...
Article
Full-text available
It is important to determine the soil-water characteristic curve (SWCC) for analyzing landslide seepage under varying hydrodynamic conditions. However, the SWCC exhibits high uncertainty due to the variability inherent in soil. To this end, a Bayesian updating framework based on the experimental data was developed to investigate the uncertainty of...
Presentation
Full-text available
Invited presentation given on CIVIL-COMP 2019, 16-19 September 2019 | Riva del Garda, near Lake Garda, Italy.
Article
Full-text available
A probabilistic model is proposed that uses observation data to estimate failure probabilities during excavations. The model integrates a Bayesian network and distanced-based Bayesian model updating. In the network, the movement of a retaining wall is selected as the indicator of failure, and the observed ground surface settlement is used to update...
Article
Full-text available
Non-intrusive Imprecise Stochastic Simulation (NISS) is a recently developed general methodological framework for efficiently propagating the imprecise probability models and for estimating the resultant failure probability functions and bounds. Due to the simplicity, high efficiency, stability and good convergence, it has been proved to be one of...
Article
Full-text available
The tendency of uncertainty analysis has promoted the transformation of sensitivity analysis from the deterministic sense to the stochastic sense. This work proposes a stochastic sensitivity analysis framework using the Bhattacharyya distance as a novel uncertainty quantification metric. The Bhattacharyya distance is utilised to provide a quantitat...
Conference Paper
Full-text available
Two challenges may exist in the reliability analysis of highly reliable structures in, e.g., aerospace engineering. The first one is that, the failure probability may be extremely small (typically, smaller than 1e-6), which commonly prevents us from generating accurate estimation with acceptable computational costs by using the available methods. T...
Data
Here are the Matlab functions to calculate the Bhattacharyya distance between two random sample sets. This tool is universal for problems with different numbers of outputs (i.e. multiple dimensional problems). Now, only the protected p-code is released. Feel free to contact the authors if you need the original M-file to customize to your own applic...
Data
Here is the Matlab tool to calculate the Bhattacharyya distance between two random sample sets. As elaborated in the paper, the calculation employs the Probability Mass Function (PMF) estimated from each sample sets. The Matlab tool is universal for different problems with different numbers of outputs (i.e. multiple dimensional problems). Now, on...
Data
Here is the Matlab tool to calculate the Bhattacharyya distance between two random sample sets. As elaborated in the paper, the calculation employs the Probability Mass Function (PMF) estimated from each sample sets. The Matlab tool is universal for problems with different numbers of outputs (i.e. multiple dimensional problems). Now, only the prot...
Data
If you have any questions on either methods or codes, please feel free to contact me at pengfeiwei@nwpu.edu.cn. This package provides Matlab codes of the "Non-intrusive imprecise stochastic simulation (NISS)" methodology framework for efficiently propagating the imprecise probability models. The codes are developed for the methods developed in the...
Data
If you have any questions on either methods or codes, please feel free to contact me at pengfeiwei@nwpu.edu.cn. This package provides Matlab codes of the "Non-intrusive imprecise stochastic simulation (NISS)" methodology framework for efficiently propagating the imprecise probability models. The codes are developed for the methods developed in the...
Article
Full-text available
(All the codes are attached. Please feel free to download and use them. If you have any question, don't hesitate to contact me.) Uncertainty propagation through the simulation models is critical for computational mechanics engineering to provide robust and reliable design in the presence of polymorphic uncertainty. This set of companion papers pres...
Conference Paper
Full-text available
This paper presents a system matrices identification approach directly from the real-time measured structural responses. Based on the experimental modal analysis, the identified system matrices are expected to represent the system behaviours as same as the experimentally measured ones. Due to the fact that the system matrices, i.e. the mass, stiffn...
Article
Full-text available
(All the codes are attached. Please feel free to download and use them. If you have any question, don't hesitate to contact me.) Structural reliability analysis for rare failure events in the presence of hybrid uncertainties is a challenging task drawing increasing attentions in both academic and engineering fields. Based on the new imprecise stoc...
Conference Paper
Full-text available
The typical model updating techniques mainly focus on calibrating the model parameters , e.g. Young's modulus and density, while being inefficient for the model form error due to the inevitable approximation and simplification during numerical modeling. In this paper, an integrated model updating approach for both uncertain material parameters and...
Cover Page
Full-text available
Special Issue on Uncertainty Management in Complex Multiphysics Structural Dynamics (SI Number: SI032B) ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering
Conference Paper
Full-text available
Uncertainty quantification metrics are critical in the campaign of stochastic model updating, by provide an elaborate measurement of the uncertainty in both simulations and experiments. In this work, the Bhattacharyya distance is proposed as a comprehensive model updating metric for two samples considering their probabilistic properties. The updati...
Conference Paper
Full-text available
To control the noise level of passenger compartment is one of the important design issues in fields such as automobile and manned aerospace. An uncoupled model of the acoustic part extracted from the coupled vibroacoustical system is significant for predicting and improving the vehicle noise performance. The objective of this work is to build mass,...
Article
Full-text available
This manuscript presents a stochastic model updating method, taking both uncertainties in models and variability in testing into account. The updated finite element (FE) models obtained through the proposed technique can aid in the analysis and design of structural systems. The authors developed a stochastic model updating method integrating distan...
Chapter
Uncertainty quantification metrics provide a quantitative measure of the agreement between predictions and observations. These metrics not only significantly influence the outcomes of model calibration but also provide a means of determining the desired level of fidelity for model validation. This manuscript evaluates the influence of these uncerta...
Article
Full-text available
Model validation of uncertain structures is a challenging research focus because of uncertainties involved in modeling, manufacturing processes, and measurement systems. A stochastic method employing Monte Carlo simulation (MCS) and hierarchical cluster analysis (HCA) is presented to give an accurate validation outcome with acceptable calculation c...

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Projects

Projects (4)
Project
Structural dynamic reliability focuses on the problem of structural reliability under stochastic dynamic excitation. In engineering, dynamic loads exist widely, including seismic loads, wind loads, vehicle loads, etc. Thus, structural dynamic reliability analysis plays an irreplaceable role in structural uncertainty analysis. In general, dynamic reliability analysis uses the theoretical tool of stochastic processes. The advances in probability analysis of stochastic process promote the development of dynamic reliability analysis. The main methods for dynamic reliability analysis include moment methods, spectral methods, Monte Carlo methods, probability density methods, and so on. In recent years, great progress have been made regarding the above dynamic reliability analysis methods, and a lot of research work has been done in their applications in engineering. From a broader perspective, most engineering phenomena or problems are dynamic. Thus, generalized dynamic reliability analysis should contain all the engineering problems related to time-dependent process, such as the structural safety assessment considering structural performance deterioration process. This Special Issue aims to gather contributions presenting the most recent advances on structural dynamic reliability theory and its engineering applications. https://www.mdpi.com/journal/applsci/special_issues/MW076LE2OC
Project
The aim of the project is the modal correlation between experimental data and finite element models. The fully integrated optimization in PERMAS allows the tuning of geometric and physical model parameters of the underlying finite element model.
Project
The Bhattacharyya distance is a stochastic measurement between two random samples and taking into account their probability distributions. The objective of this work is to further generalize the application of the Bhattacharyya distance as a novel Uncertainty Quantification (UQ) metric in the campaign of Verification and Validation (V&V) of the numerical models. The emphasis in this project is to design the Bhattacharyya distance as a universal tool, which can be conveniently embedded in the frameworks such as stochastic model updating, sensitivity analysis, etc.