Training course on Advanced Computational Design for Structures

Training Course on Advanced Computational Design for Structures

Introduction

The increasing complexity and ambitious scope of modern architectural and structural projects are continuously pushing the boundaries of traditional design and analysis methodologies. In response, the seamless integration of advanced computational design techniques has become an indispensable imperative, empowering engineers to explore vast design spaces, rigorously optimize performance, and precisely realize highly intricate geometries that were previously deemed unfeasible. This advanced approach to computational design for structures transcends basic finite element analysis, strategically leveraging sophisticated parametric modeling, innovative generative design, and precise algorithmic methodologies to create structures that are not only more efficient and sustainable but also exceptionally resilient, responding intelligently to a myriad of complex performance criteria.

Training Course on Advanced Computational Design for Structures is meticulously designed to provide participants with the practical application of cutting-edge computational tools and methodologies specifically for the design and rigorous optimization of complex structural systems. The curriculum will encompass a deep understanding of advanced parametric modeling for intricate structural forms, mastery of generative design algorithms for performance-driven optimization (e.g., topology optimization, sophisticated form-finding), and practical implementation of scripting and visual programming languages (e.g., Python, Grasshopper) for customized workflows. Furthermore, the course will focus on integrating advanced structural analysis software with dynamic design environments. Through a balanced blend of robust theoretical foundations, extensive hands-on exercises, and project-based learning, this course will comprehensively prepare participants to innovate groundbreaking structural solutions, significantly enhance design efficiency, and actively contribute to the development of the next generation of resilient and high-performing buildings and critical infrastructure.

Course Objectives

Upon completion of this course, participants will be able to:

1. Analyze the fundamental concepts of advanced computational design and its paradigm shift in structural engineering.
2. Comprehend the principles of parametric modeling and its application to complex structural geometries.
3. Master various generative design algorithms for performance-driven structural optimization (e.g., topology, shape).
4. Develop expertise in scripting and visual programming languages (e.g., Python, Grasshopper) for structural workflows.
5. Formulate strategies for integrating computational design tools with advanced structural analysis software.
6. Understand the critical role of data-driven design, simulation, and multi-objective optimization in structural engineering.
7. Implement robust approaches to form-finding techniques for lightweight and efficient structural systems.
8. Explore key strategies for structural performance assessment under various loading conditions and constraints.
9. Apply methodologies for designing adaptive and responsive structures using computational methods.
10. Understand the importance of material considerations, fabrication constraints, and constructability in computational design.
11. Develop preliminary skills in utilizing specialized software platforms for advanced structural computational design.
12. Design and optimize a complex structural system using advanced computational design methodologies.
13. Examine global best practices and future trends in computational structural engineering, AI, and robotics in construction.

Target Audience

This course is ideal for professionals in structural engineering, architecture, and related design fields:

1. Structural Engineers: Seeking advanced skills in computational design and optimization.
2. Architects & Designers: Interested in form-finding and performance-driven structural integration.
3. Computational Designers: Applying algorithmic design to structural challenges.
4. Researchers & Academics: Exploring cutting-edge methods in structural engineering.
5. BIM/Parametric Modeling Specialists: Expanding expertise into structural analysis and optimization.
6. Construction Technologists: Understanding digital fabrication and complex geometry realization.
7. Software Developers: Creating custom tools for structural design automation.
8. Project Managers: Overseeing innovative and complex structural projects.

Course Duration: 10 Days

Course Modules

• Module 1: Introduction to Computational Design in Structural Engineering
• Define computational design and its evolution in the context of structures.
• Discuss the limitations of traditional design methods for complex geometries and performance goals.
• Understand the paradigm shift brought by parametric, generative, and algorithmic design.
• Explore the benefits: efficiency, optimization, complexity handling, innovation.
• Identify key software ecosystems and tools for computational structural design.
• Module 2: Parametric Modeling for Structural Forms
• Comprehend the principles of parametric modeling and its application to structural elements.
• Learn about establishing geometric relationships and design variables for structural forms.
• Master techniques for creating adaptive and flexible structural models using visual programming (e.g., Grasshopper).
• Discuss the use of data trees and lists for managing complex structural hierarchies.
• Apply parametric modeling to generate various structural configurations for exploration.
• Module 3: Advanced Structural Analysis Integration
• Develop expertise in integrating parametric design environments with structural analysis software (e.g., Rhino.Inside.Revit/SAP2000).
• Learn about seamless data exchange and synchronization between design and analysis models.
• Master techniques for setting up load cases, boundary conditions, and material properties computationally.
• Discuss automated meshing strategies for complex geometries.
• Apply integrated workflows for rapid iterative analysis of parametric structural models.
• Module 4: Generative Design and Form-Finding
• Formulate strategies for generative design in structural engineering.
• Understand the concept of form-finding for lightweight and efficient structures (e.g., cable nets, shells).
• Explore techniques for using physics-based simulations for equilibrium and minimal surface generation.
• Discuss the application of evolutionary algorithms for exploring design variations.
• Apply generative methods to optimize structural shapes based on performance criteria.
• Module 5: Topology Optimization for Structures
• Understand the critical role of topology optimization in minimizing material usage and maximizing stiffness.
• Implement robust approaches to setting up topology optimization problems: design space, loads, constraints.
• Explore techniques for interpreting and refining topology optimized geometries for constructability.
• Discuss the use of density-based and level-set methods in topology optimization.
• Apply topology optimization to generate novel and efficient structural forms.
• Module 6: Scripting for Structural Design and Automation (Python)
• Apply methodologies for scripting structural design workflows using Python.
• Master techniques for interacting with structural analysis software APIs (e.g., SAP2000 API, ETABS API).
• Understand the use of Python libraries for geometry manipulation, data processing, and visualization.
• Discuss automated parameter variations, data extraction, and report generation.
• Explore object-oriented programming principles for developing custom structural tools.
• Module 7: Multi-Objective Optimization and Performance-Driven Design
• Explore key strategies for multi-objective structural optimization.
• Learn about defining conflicting objectives (e.g., minimizing weight vs. maximizing stiffness).
• Discuss multi-criteria decision-making and Pareto front analysis for optimal solutions.
• Understand the use of optimization algorithms (e.g., NSGA-II, genetic algorithms) for complex problems.
• Examine case studies of performance-driven design for various structural systems.
• Module 8: Advanced Data Management and Visualization
• Develop expertise in managing large datasets generated by computational design workflows.
• Learn about databases and data structures for storing structural analysis results and design variants.
• Master techniques for advanced data visualization of structural performance, stresses, and displacements.
• Discuss interactive dashboards and web-based platforms for design exploration.
• Apply data visualization tools to communicate complex structural insights effectively.
• Module 9: Material-Driven Design and Digital Fabrication
• Formulate strategies for material-driven computational design.
• Understand the impact of material properties on structural form and performance.
• Explore techniques for designing for digital fabrication (e.g., robotic fabrication, 3D printing).
• Discuss the optimization of material usage and waste reduction through computational methods.
• Apply design-to-fabrication workflows for complex structural components.
• Module 10: Adaptive and Responsive Structures
• Understand the critical role of computational design in developing adaptive and responsive structures.
• Implement robust approaches to designing structures that react to environmental changes (e.g., wind, seismic).
• Explore techniques for integrating sensors and control systems into structural design.
• Discuss the use of real-time data for structural performance monitoring and active control.
• Examine case studies of kinetic architecture and smart structural systems.
• Module 11: Artificial Intelligence (AI) in Structural Engineering
• Apply methodologies for leveraging AI and Machine Learning (ML) in structural design and analysis.
• Master techniques for using ML for surrogate modeling, predicting structural behavior, and anomaly detection.
• Understand the application of neural networks for pattern recognition in structural data.
• Discuss the potential of AI for automating design decisions and optimizing design iterations.
• Explore the ethical considerations and limitations of AI in critical structural applications.
• Module 12: Future Trends and Industry Integration
• Examine global best practices and innovative projects in advanced computational structural design.
• Develop preliminary skills in assessing emerging technologies and their impact on structural engineering.
• Discuss the integration of computational design with Building Information Modeling (BIM) and digital twins.
• Explore the role of virtual reality (VR) and augmented reality (AR) in design review and collaboration.
• Design a strategic roadmap for adopting and scaling advanced computational design within an engineering firm.

Training Methodology

• Interactive Workshops: Facilitated discussions, group exercises, and problem-solving activities.
• Case Studies: Real-world examples to illustrate successful community-based surveillance practices.
• Role-Playing and Simulations: Practice engaging communities in surveillance activities.
• Expert Presentations: Insights from experienced public health professionals and community leaders.
• Group Projects: Collaborative development of community surveillance plans.
• Action Planning: Development of personalized action plans for implementing community-based surveillance.
• Digital Tools and Resources: Utilization of online platforms for collaboration and learning.
• Peer-to-Peer Learning: Sharing experiences and insights on community engagement.
• Post-Training Support: Access to online forums, mentorship, and continued learning resources.

Register as a group from 3 participants for a Discount

Send us an email: info@datastatresearch.org or call +254724527104

Certification

Upon successful completion of this training, participants will be issued with a globally recognized certificate.

Tailor-Made Course

We also offer tailor-made courses based on your needs.

Key Notes

• Participants must be conversant in English.
• Upon completion of training, participants will receive an Authorized Training Certificate.
• The course duration is flexible and can be modified to fit any number of days.
• Course fee includes facilitation, training materials, 2 coffee breaks, buffet lunch, and a Certificate upon successful completion.
• One-year post-training support, consultation, and coaching provided after the course.
• Payment should be made at least a week before the training commencement to DATASTAT CONSULTANCY LTD account, as indicated in the invoice, to enable better preparation.