Probabilistic Optimisation Of Composite Structures Machine Learning For Design Optimisation
Download Probabilistic Optimisation Of Composite Structures Machine Learning For Design Optimisation PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Probabilistic Optimisation Of Composite Structures Machine Learning For Design Optimisation book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages.
Probabilistic Optimisation Of Composite Structures: Machine Learning For Design Optimisation
Author: Kwangkyu Alex Yoo
language: en
Publisher: World Scientific
Release Date: 2025-03-17
This book introduces an innovative approach to multi-fidelity probabilistic optimisation for aircraft composite structures, addressing the challenge of balancing reliability with computational cost. Probabilistic optimisation pursues statistically reliable and robust solutions by accounting for uncertainties in data, such as material properties and geometry tolerances. Traditional approaches using high-fidelity models, though accurate, are computationally expensive and time-consuming, especially when using complex methods such as Monte Carlo simulations and gradient calculations.For the first time, the proposed multi-fidelity method combines high- and low-fidelity models, enabling high-fidelity models to focus on specific areas of the design space, while low-fidelity models explore the entire space. Machine learning technologies, such as artificial neural networks and nonlinear autoregressive Gaussian processes, fill information gaps between different fidelity models, enhancing model accuracy. The multi-fidelity probabilistic optimisation framework is demonstrated through the reliability-based and robust design problems of aircraft composite structures under a thermo-mechanical environment, showing acceptable accuracy and reductions in computational time.
Structural Design and Optimization of Lifting Self-forming GFRP Elastic Gridshells based on Machine Learning
Author: Soheila Kookalani
language: en
Publisher: Taylor & Francis
Release Date: 2025-08-26
Structural Design and Optimization of Lifting Self-forming GFRP Elastic Gridshells Based on Machine Learning presents the algorithms of machine learning (ML) that can be used for the structural design and optimization of glass fiber reinforced polymer (GFRP) elastic gridshells, including linear regression, ridge regression, K-nearest neighbors, decision tree, random forest, AdaBoost, XGBoost, artificial neural network, support vector machine (SVM), and six enhanced forms of SVM. It also introduces interpretable ML approaches, including partial dependence plot, accumulated local effects, and SHaply additive exPlanations (SHAP). Also, several methods for developing ML algorithms, including K-fold cross-validation (CV), Taguchi, a technique for order preference by similarity to ideal solution (TOPSIS), and multi-objective particle swarm optimization (MOPSO), are proposed. These algorithms are implemented to improve the applications of gridshell structures using a comprehensive representation of ML models. This research introduces novel frameworks for shape prediction, form-finding, structural performance assessment, and shape optimization of lifting self-forming GFRP elastic gridshells using ML methods. This book will be of interest to researchers and academics interested in advanced design methods and ML technology in architecture, engineering, and construction fields.
Machine Learning Aided Analysis, Design, and Additive Manufacturing of Functionally Graded Porous Composite Structures
Functionally Graded Porous Structures: Applied Methods in Mechanical Performance Evaluation, Machine Learning Aided Analysis, and Additive Manufacturing presents a state-of-the-art review of the latest advances and cutting-edge technologies in this important research field. The book is divided into three key sections. The first section begins with an introduction to functionally graded porous structures and details the effects of graded porosities on bending, buckling, and vibration behaviours within the framework of Timoshenko beam theory, and first-order shear deformable plate theory. The second section is focused on the usage of machine learning techniques for smart structural analysis of porous components as an evolution from traditional engineering, methods. The third section focuses on additive manufacturing of structures with graded porosities for end-user applications. The book follows a clear path from design and analysis to fabrication and applications. Readers will find extensive knowledge and examples of functionally graded porous structures that are suitable for innovative research and market needs, with applications relevant to a diverse range of industrial fields, including mechanical, structural, aerospace, energy, and biomedical engineering. - Provides a comprehensive picture of novel porous materials and advanced lightweight structural technologies that are applicable to a diverse range of industrial sectors - Updated with the most recent advances in the field of porous structures - Goes beyond traditional structural aspects and covers novel evaluation strategies, machine learning aided analysis, and additive manufacturing - Covers weight management strategies for structural components to achieve multifunctional purposes - Addresses key issues in the design of lightweight structures, offering significant environmental benefits