Structural Design and Optimization of Lifting Self-forming GFRP Elastic Gridshells based on Machine Learning

1. Auflage 2025
ISBN: 978-1-032-90120-6
Verlag: Taylor & Francis Ltd

Buch, Englisch, 226 Seiten, Format (B × H): 156 mm x 234 mm, Gewicht: 580 g

Reihe: Digital Frontiers in Buildings and Infrastructure

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.

Zielgruppe

Academic and Postgraduate

Autoren/Hrsg.

Rahimian, Farzad Pour

Kookalani, Soheila

Alavi, Hamidreza

Fachgebiete

• Geisteswissenschaften Architektur Architektur: Allgemeines
• Technische Wissenschaften Bauingenieurwesen Baukonstruktion, Baufachmaterialien
• Technische Wissenschaften Bauingenieurwesen Bauingenieurwesen
• Technische Wissenschaften Technik Allgemein Technik: Allgemeines
• Mathematik | Informatik EDV | Informatik Professionelle Anwendung Computersimulation & Modelle, 3-D Graphik
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen
• Mathematik | Informatik EDV | Informatik Technische Informatik
• Interdisziplinäres Wissenschaften Wissenschaften: Allgemeines Enzyklopädien, Nachschlagewerke, Wörterbücher
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken

Inhaltsverzeichnis

Chapter 1 Introduction of GFRP Elastic Gridshell Structures and Machine Learning Chapter 2 A Review of GFRP Elastic Gridshell Structures and Machine Learning Algorithms Chapter 3 Shape Prediction of Slender Bars Based on Discrete Elements Chapter 4 Shape Prediction of GFRP Elastic Gridshells During Lifting Construction Chapter 5 Form-Finding of GFRP Elastic Gridshells During Lifting Construction Process Chapter 6 Structural Performance Assessment of GFRP Elastic Gridshells Chapter 7 Structural Optimization of GFRP Elastic Gridshells Chapter 8 Conclusions and Recommendations for Structural Design and Optimizations of Gridshell Structures

Über Autor(innen)

Soheila Kookalani is a Research Associate in the Department of Engineering at the University of Cambridge. Her research focuses on sustainable construction, circular economy, and digital transformation in the built environment. She specializes in integrating artificial intelligence, digital twin technologies, and automation to drive innovation in construction engineering and management. She plays an active role in teaching and has contributed to the development of digital twin modules, advancing knowledge in this rapidly evolving field. She has a strong track record of publications in high-impact journals and international conferences, reflecting her contributions to sustainable construction, digital innovation, and circular economy practices. She is also an editorial board member of the Journal of Smart and Sustainable Built Environment, where she contributes to advancing research in smart, data-driven, and environmentally responsible construction methods. In addition, she serves as a reviewer for several esteemed journals, ensuring rigorous and high-quality research dissemination in her field. Committed to addressing global challenges, she continues to explore emerging technologies and policy-driven solutions for infrastructure resilience, circular design, and the digitalization of construction.

Hamidreza Alavi is a Research and Teaching Associate in the Department of Engineering at the University of Cambridge. He is a Fellow of the Higher Education Academy (FHEA) and plays an active role in curriculum development and teaching at Cambridge. He designs and delivers modules on Building Information Modeling (BIM) and digital twin technologies, integrating real-world applications with advanced computational methods. His research focuses on the integration of digital twins, artificial intelligence (AI), and data-driven decision-support systems for infrastructure management and construction automation. Previously, he was an Associate Professor at the Polytechnic University o