Structural Design and Optimization of Lifting Self-Forming Gfrp Elastic Gridshells Based on Machine Learning
暫譯: 基於機器學習的提升自成型GFRP彈性格網的結構設計與優化

Kookalani, Soheila, Alavi, Hamidreza, Rahimian, Farzad Pour

Structural Design and Optimization of Lifting Self-Forming Gfrp Elastic Gridshells Based on Machine Learning

相關主題

商品描述

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.

商品描述(中文翻譯)

《基於機器學習的提升自成型玻璃纖維增強聚合物（GFRP）彈性格網殼的結構設計與優化》介紹了可用於玻璃纖維增強聚合物（GFRP）彈性格網殼的結構設計與優化的機器學習（ML）算法，包括線性回歸、脊回歸、K最近鄰、決策樹、隨機森林、AdaBoost、XGBoost、人工神經網絡、支持向量機（SVM）及六種增強形式的SVM。書中還介紹了可解釋的機器學習方法，包括部分依賴圖、累積局部效應和SHaply加法解釋（SHAP）。此外，還提出了幾種開發機器學習算法的方法，包括K折交叉驗證（CV）、田口法、理想解相似性排序技術（TOPSIS）和多目標粒子群優化（MOPSO）。這些算法的實施旨在通過綜合的機器學習模型表示來改善格網殼結構的應用。本研究引入了使用機器學習方法進行形狀預測、形狀尋找、結構性能評估和形狀優化的創新框架，專注於提升自成型的GFRP彈性格網殼。本書將吸引對建築、工程和建設領域的先進設計方法及機器學習技術感興趣的研究人員和學者。

作者簡介

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 £2.4 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.

作者簡介(中文翻譯)

Soheila Kookalani 是劍橋大學工程系的研究助理。她的研究專注於可持續建築、循環經濟以及建築環境中的數位轉型。她專長於整合人工智慧、數位雙胞胎技術和自動化，以推動建築工程和管理的創新。她積極參與教學，並為數位雙胞胎模組的開發做出了貢獻，推進了這一快速發展領域的知識。她在高影響力期刊和國際會議上有著豐富的發表紀錄，反映了她在可持續建築、數位創新和循環經濟實踐方面的貢獻。她還是《智能與可持續建築環境期刊》的編輯委員會成員，為推進智能、數據驅動和環境負責任的建築方法的研究做出貢獻。此外，她還擔任多個知名期刊的審稿人，確保其領域內的研究發表具有嚴謹性和高品質。她致力於應對全球挑戰，持續探索新興技術和政策驅動的基礎設施韌性、循環設計和建築數位化解決方案。

Hamidreza Alavi 是劍橋大學工程系的研究與教學助理。他是高等教育學院的研究員（FHEA），在劍橋的課程開發和教學中扮演積極角色。他設計並教授有關建築資訊建模（BIM）和數位雙胞胎技術的模組，將現實世界的應用與先進的計算方法相結合。他的研究專注於數位雙胞胎、人工智慧（AI）和數據驅動的決策支持系統在基礎設施管理和建築自動化中的整合。此前，他曾擔任加泰羅尼亞理工大學（UPC）的副教授，領導基於BIM的設施管理和數位建築研究。他積極參與領導旨在推進建築環境數位化的國際研究項目。他的工作在高影響力期刊和會議上廣泛發表，並擔任《智能與可持續建築環境期刊》的編輯委員會成員。致力於整合研究與教育，他積極貢獻於為建築和基礎設施領域開發智能、可持續和技術驅動的解決方案。

Farzad Pour Rahimian 是提賽德大學計算、工程與數位技術學院的數位工程教授。他領導可持續工程中心和開放研究及產出質量小組。他是Q1《智能與可持續建築環境期刊》的主編，並擔任年度智能與可持續建築環境會議的榮譽主席。他的200多篇出版物獲得了5,000次引用，強調採用尖端技術以服務於淨零和可持續發展議程，包括能源政策、數據驅動的數位雙胞胎、需求響應優化、智能能源、循環建築和社會創新。Farzad在其博士研究期間指導了14位成功的學者，並是CSE的12位學者的導師。這一點通過持續強勁的研究紀錄得以證明，包括19個資助的行業主導研究項目和四個顧問項目（14個項目和4個顧問的主要研究者，總價值為240萬英鎊），資助來源包括H2021、InnovateUK、AHRC、ERDF、CSIC、SFC和Data Lab。他是國際建築理事會（CIB）和buildingSMART International的成員。

類似商品

最後瀏覽商品 (20)