Initial load factor adjustment through genetic algorithm for the generalized displacement control method: Implementation on non-rigid origami analysis

Abstract

The nonlinear analysis of structural systems relies on effective nonlinear solution schemes, known as nonlinear solvers or path-following methods. This paper explores the challenge of determining the initial load factor in such analyses, with a particular focus on complex origami structures. This study presents a novel approach that leverages genetic algorithms (GA) to efficiently determine the initial load factor for the generalized displacement method. Building upon the concept of stiffness parameters, our method offers a systematic solution. In this work, GA serves as a tool to discover the appropriate initial load factor based on changes in the stiffness parameter, providing a more rational and user-friendly approach. To demonstrate the practical applicability of our approach, we apply it to a range of complex origami problems, including the folding of Miura-ori, folding of an egg-box origami, bending of an egg-box origami, and folding of an arc Miura-ori. These examples, along with a truss example, encompass highly nonlinear behaviors, including limit points, extreme stiffness fluctuations, snap-through, and snap-back. Our results showcase the effectiveness of our method in accurately tuning the initial load factor to capture and track intricate nonlinear behaviors. Furthermore, it offers the flexibility to fine-tune the distribution of points along the path and manage the resolution of the depicted equilibrium path. This versatility underscores its potential applications in fields such as deployable structures and complex large deformation structural analysis.