Structural Design And Optimization Of Lifting Self Forming Gfrp Elastic Gridshells Based On Machine Learning
Download Structural Design And Optimization Of Lifting Self Forming Gfrp Elastic Gridshells Based On Machine Learning PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Structural Design And Optimization Of Lifting Self Forming Gfrp Elastic Gridshells Based On Machine Learning 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.
Structural Design and Optimization of Lifting Self-Forming Gfrp Elastic Gridshells Based on Machine Learning
This book aims to develop structural design and optimization methods of lifting self-forming Glass fiber reinforced polymer (GFRP) elastic gridshells based on machine learning (ML).
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.
Enhancing Interoperability and Automation of Construction Waste Quantification
Author: Subarna Sivashanmugam
language: en
Publisher: Taylor & Francis
Release Date: 2025-07-28
Enhancing Interoperability and Automation of Construction Waste Quantification outlines the construction waste quantification (CWQ) modelling that supports data-driven decision-making in the built environment. It presents how the functionalities of Building Information Modelling (BIM) and Semantic Web Technology are integrated to enhance the interoperability and automation of the CWQ process. The research in this book shapes the development of a semantic framework that supports the built environment in quantifying construction waste (CW) and informing optimal material choices from early design stages to minimise the quantity and diversity of waste generation. The book also demonstrates the application of the proposed framework using an ontology (PROduct CIRcularity Ontology) and a BIM-integrated digital tool (Building Waste Tool [BWT]). The PRODCIRO and BWT inform how data, standardisation, consistency, and granularity could streamline and automate the CWQ process. The book also presents the outputs of a test-case building used to validate the adaptability and accuracy of the framework. This book is a valuable resource for BIM and sustainability practitioners. It provides a comprehensive discussion on the significance of CW, its impacts on sustainability, advancements in CWQ, and data and information gaps within the existing CWQ practices. The solution proposed in the book will help the built environment to shift from reactive to proactive and preventive waste management.