This course aims to equip PhD students in civil engineering and architecture with the knowledge of parametric design and structural optimization. It focuses on three key steps in structural optimization: 1. Design Vector Definition: Understanding the fundamental concept of design vectors. 2. Definition of an Objective Function: Formulating objective functions for optimization problems. 3. Formalization of Structural/Functional Constraints: Exploring the formalization of constraints in structural optimization. The course delves into the efficiency of memetic algorithms as a numerical approach for solving optimization problems, using MATLAB code. It also highlights the advantages of optimization through parametric design, utilizing Grasshopper for structural parametric design.

conoscenza base di analisi e progettazione strutturale conoscenza base di matlab

This course aims to equip PhD students in civil engineering and architecture with the knowledge of parametric design and structural optimization. It focuses on three key steps in structural optimization: 1. Design Vector Definition: Understanding the fundamental concept of design vectors. 2. Definition of an Objective Function: Formulating objective functions for optimization problems. 3. Formalization of Structural/Functional Constraints: Exploring the formalization of constraints in structural optimization. The course delves into the efficiency of memetic algorithms as a numerical approach for solving optimization problems, using MATLAB code. It also highlights the advantages of optimization through parametric design, utilizing Grasshopper for structural parametric design. the course structure is the following: 1. Introduction to Optimization Strategies (3 h) - Overview of optimization concepts in civil engineering and architecture. - Introduction to memetic algorithms for optimization strategies. 2. MATLAB Fundamentals for Structural Optimization (3 h) - Basic MATLAB programming for structural optimization. - Application of memetic algorithms using MATLAB code. 3. Grasshopper for Structural Parametric Design (4h) - Understanding Grasshopper as a visual programming language. - Integration of Grasshopper for parametric design in civil engineering. 4. Case Studies and Applications: (4h) - Real-world examples of parametric design and optimization in civil engineering and architecture. - Hands-on projects using memetic algorithms, MATLAB, and Grasshopper. 5. Advanced Topics (4h) - Exploring additional optimization techniques and tools. - General parameter control framework for evolutionary algorithms 6. Metaheuristics and Optimization in Practice (2h) - Understanding the role of metaheuristic algorithms in finding optimal solutions. - Application of metaheuristics in real cases 7. Integration of Genetic Algorithms for Conceptual Design (2h) - Using genetic algorithms for conceptual structural design in commercial buildings. - Parametric building performance simulation with genetic algorithms.

A distanza in modalità sincrona

Presentazione report scritto

P.D.2-2 - Settembre