Soheila Kookalani – författare

Structural Design and Optimization of Lifting Self-forming GFRP Elastic Gridshells based on Machine Learning presents the algorithms of ML that can be used for the structural design and optimization of 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. The book will be of interest to researchers and academics interested in advanced design methods and ML technology in architecture, engineering and construction fields.

Structural Design and Optimization of Lifting Self-forming GFRP Elastic Gridshells based on Machine Learning presents the algorithms of ML that can be used for the structural design and optimization of 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. The 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 integrates of Building Information Modeling (BIM) and Decision Support Systems (DSS) in the field of building design, construction, and maintenance. The book explores how BIM and DSS technologies can be synergistically utilized to enhance performance, comfort, and maintenance efficiency in buildings.

With an emphasis on practical applications, the book provides a comprehensive overview of the latest advancements in BIM and DSS, including real-world case studies and implementation guidelines. The book features illustrations, tables, and examples that aid in understanding complex concepts and demonstrate the practical application of BIM and DSS in building projects.  

Readers will gain a deep understanding of how BIM and DSS can be integrated to optimize building design, streamline construction processes, and improve facility management and maintenance. The main benefit of reading this book is that it provides a valuable resource for professionals in the architecture, engineering, and construction industries who want to leverage the power of BIM and DSS to enhance their building projects. Additionally, the book explores how BIM and DSS can contribute to energy efficiency.