A Kriging model enhanced integral method for reliability analysis

An Adaptive Two-Stage Framework for AK-MCS in Reliability Analysis

Comparative studies across six benchmark cases demonstrate that the Two-Stage AK-MCS method reduces the average number of function evaluations by 2.0% to 10.0% compared to the standard AK-MCS method.

Unifined reliability-based design optimization method of large-scale structures

Reliability-based design optimization (RBDO) is inherently complex and computationally intensive. This study aims to enhance the accuracy and efficiency of RBDO by integrating advanced probabilistic and optimization techniques. The proposed framework incorporates improved combination line sampling (iCLS), approximate B...

A hybrid inverse approach for estimating structural pavement parameters using the Differential Quadrature Method and a metaheuristic optimization algorithm

Research on the Application of Incremental Approximation Models in Hull Form Optimization Design

To address the problems in hull form optimization where an increase in design variables requires approximation models to be rebuilt from scratch and historical CFD samples are insufficiently utilized, this paper proposes an incremental approximation model construction method. Based on the additive decomposition propert...

Cross-term extreme learning machine for accurate reliability analysis

By adding a cross-term layer between the hidden and output layers, the model's expressive power is expanded through the nonlinear combination of hidden nodes, overcoming the limitation of traditional ELM relying on linear mapping of a single hidden layer.

Reliability Analysis of Unsaturated Soil Slopes Using Advanced Surrogate Models: A Comparative Study of PCE-MCS, K-MCS, and Gradient-Based Methods

Probabilistic stability analysis of soil slopes is essential for reliable geotechnical design due to pronounced uncertainties in soil strength and hydraulic parameters. Although crude Monte Carlo simulation (MCS) provides a robust benchmark, its prohibitive computational cost and the limitations of gradient-based m...