Chen Ding

• Master of Engineering
• PhD Student at Leibniz Universität Hannover

Network reliability analysis remains a challenge due to the increasing size and complexity of networks. This paper presents a novel sampling method and an active learning method for efficient and accurate network reliability estimation under node failure and edge failure scenarios. The proposed sampling method adopts Monte Carlo technique to sample...

Network reliability analysis remains a challenge due to the increasing size and complexity of networks. This paper presents a novel sampling method and an active learning method for efficient and accurate network reliability estimation under node failure and edge failure scenarios. The proposed sampling method adopts Monte Carlo technique to sample...

In engineering analysis, propagating parametric probability boxes (p-boxes) remains a challenge since a computationally expensive nested solution scheme is involved. To tackle this challenge, this paper proposes a novel optimization-integration method to propagate parametric p-boxes, mainly focusing on estimating the lower and upper bounds of struc...

This paper aims at approximating the bounds of the static response of structures with interval uncertainties. Such task is often challenging due to the large number of computationally intensive response evaluations required. To address this challenge, we propose an efficient non-intrusive method, namely, parallel Bayesian interval optimization (PBI...

First-passage probability estimation of high-dimensional nonlinear stochastic dynamic systems is a significant task to be solved in many science and engineering fields, but remains still an open challenge. The present paper develops a novel approach, termed ‘fractional moments-based mixture distribution’, to address such challenge. This approach is...

First-passage probability estimation of high-dimensional nonlinear stochastic systems is a significant task to be solved in many science and engineering fields, but remains still an open challenge. The present paper develops a novel approach, termed 'fractional moments-based mixture distribution', to address such challenge. This approach is impleme...

First-passage probability estimation of stochastic dynamic systems is an important but still challenging problem in various science and engineering fields. This paper proposes a novel parametric approach, termed 'fractional moments-based mixture distribution' (FMs-MD), to address this challenge. Such method is based on capturing the extreme value d...

In this paper, an adaptive Hermite distribution model with probability weighted moments (PWMs) is proposed for evaluating the extreme-value distribution (EVD) of response, which serves as the basis of seismic reliability analysis of complex non-linear structures under random seismic excitations. From the perspective of EVD, the problem formulation...

Statistical moments estimation of the performance function with the aim of balancing accuracy and efficiency still remains a challenge for moment-based structural reliability analysis. In this paper, an improved adaptive bivariate dimension-reduction method in terms of vectors (i-VBDRM) is proposed for efficient statistical moments and reliability...

Evaluating the statistical moments of performance functions which aims at keeping the tradeoff of accuracy and efficiency is still a challenge in structure reliability analysis. This paper proposes several modified bivariate dimension reduction methods (BDRM), based on two-dimensional and one-dimensional Gauss-Hermite quadrature. Compared to the or...