9781032901206 - Structural Design and Optimization of Lifting Self-forming GFRP Elastic Gridshells based on Machine Learning di Kookalani, Soheila; Alavi, Hamidreza; Rahimian, Farz... (17 risultati)

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Condizione: As New. Unread book in perfect condition.

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Condizione: New. In.

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Hardcover. Condizione: Brand New. 240 pages. 9.18x6.12x9.21 inches. In Stock.

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Hardcover. Condizione: new. Hardcover. 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. 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). This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Hardcover. Condizione: new. Hardcover. 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. 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). This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

HRD. Condizione: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

HRD. Condizione: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Condizione: New. PRINT ON DEMAND.

Hardcover. Condizione: new. Hardcover. 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. 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). This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

Buch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 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).