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.
Reihe
Sprache
Verlagsort
Verlagsgruppe
Zielgruppe
Für höhere Schule und Studium
Für Beruf und Forschung
Academic and Postgraduate
Produkt-Hinweis
Fadenheftung
Gewebe-Einband
Illustrationen
175 s/w Abbildungen, 9 s/w Photographien bzw. Rasterbilder, 166 s/w Zeichnungen, 49 s/w Tabellen
49 Tables, black and white; 166 Line drawings, black and white; 9 Halftones, black and white; 175 Illustrations, black and white
Maße
Höhe: 234 mm
Breite: 156 mm
Dicke: 14 mm
Gewicht
ISBN-13
978-1-032-90120-6 (9781032901206)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Klassifikation
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 of Catalonia (UPC), where he led research in BIM-based facility management and digital construction. He has been actively involved in leading international research projects aimed at advancing digitalization in the built environment. His work has been widely published in high-impact journals and conferences, and he serves as an editorial board member for the Journal of Smart and Sustainable Built Environment. Dedicated to integrating research and education, he actively contributes to developing intelligent, sustainable, and technology-driven solutions for the construction and infrastructure sectors.
Farzad ?Pour Rahimian is a Professor of Digital Engineering at the School of Computing, Engineering and Digital Technologies at Teesside University. He leads the Centre for Sustainable Engineering, the Open Research and Output Quality group. He is the editor-in-chief of the Q1 Journal of Smart and Sustainable Built Environment and honorary chair of Annual Smart and Sustainable Built Environment Conferences. He has 5,000 citations for more than 200 publications with a strong emphasis on adopting cutting-edge technologies to serve the net zero and sustainability agenda, including energy policies, data-driven digital twins, demand response optimization, smart energies, circular construction, and social innovation. Farzad supervised 14 successful academics during their PhD study as their director of studies and is the mentor of 12 academics at CSE. This is demonstrated through a sustained strong research record that includes 19 funded industry-led research projects and four consultancies (principal investigator in 14 projects and 4 consultancies with an overall value of GBP2.4 m) from the H2021, InnovateUK, AHRC, ERDF, CSIC, SFC, and Data Lab. He is a member of the International Council for Building (CIB) and buildingSMART International.
Autor*in
Teesside University, UK
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
