Work

An intelligent reliability-based design optimization of complex engineering structures using manifold learning-enhanced surrogate modeling

Year: 2025

Type: article

Abstract: With the increasing integration of computer science and engineering optimization, the capabilities of advanced machine learning in data processing and surrogate modeling for complex simulations have provided a transformative development direction for enhancing the computational performance of reliability-based design optimization (RBDO). However, when addressing highly nonlinear and complex problems, machine learning models often encounter challenges arising from the trade-off between modeling accuracy and computational efficiency during surrogate simulation analysis. Particularly in high-dimensional physical simulations, traditional machine learning approaches struggle to construct precise surrogate models, thereby limiting their effectiveness within the RBDO framework. Manifold learning represents an effective dimensionality reduction technique. To address these challenges, this study proposes a manifold learning-integrated surrogate modeling methodology along with a novel RBDO framework. The approach employs active dimensionality reduction to enhance the adaptability of advanced surrogate models across diverse complex engineering structures, complemented by sophisticated metaheuristic algorithms to improve optimization accuracy and efficiency. The proposed reliability-based design optimization method consists of three sequential stages: (1) developing high-fidelity surrogate models through manifold learning to replace complex engineering simulations; (2) conducting inverse reliability analysis incorporating the advanced surrogate model to transform uncertainty optimization problems into deterministic ones; and (3) solving for optimal design solutions using state-of-the-art metaheuristic algorithms. Experimental results demonstrate that the proposed RBDO framework reduces computational time by ∼68% compared to conventional design methods, validating the effectiveness, and superiority of this optimization approach for addressing complex engineering problems.

Source: Advances in Mechanical Engineering

DOI: 10.1177/16878132251395799

Authors: Hang Zhou, Yimin Shen, Song Chen, Xiaoping Jing

Institution: Chengdu Technological University

Language: English

Topic: Probabilistic and Robust Engineering Design

Subfield: Statistics, Probability and Uncertainty

Field: Decision Sciences

Domain: Social Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $2,650