AI Applications in Structural Engineering: Design Optimization, Structural Health Monitoring, and Risk Assessment

Course Description:

This course provides a comprehensive examination of artificial intelligence applications throughout the structural engineering lifecycle, from initial design and analysis through construction, monitoring, maintenance, and asset management. Foundational concepts are introduced to establish an understanding of machine learning, deep learning, optimization algorithms, and computer vision technologies relevant to structural engineering. The course explores AI-assisted structural analysis and design, including integration with finite element modeling, load prediction, material behavior modeling, and generative optimization of structural systems while maintaining compliance with applicable building codes and design standards.

Learning Objectives:

Upon successful completion of this course, the participant will be able to:

1. Define fundamental artificial intelligence concepts and terminology relevant to structural engineering applications, including machine learning, deep learning, optimization algorithms, and computer vision technologies.
2. Explain how artificial intelligence technologies are applied in structural analysis and design, including integration with finite element modeling, load prediction, material behavior modeling, and generative structural optimization while maintaining compliance with applicable codes and standards.
3. Identify machine learning applications used during construction and project execution, including progress monitoring, quality control, structural monitoring during erection, and safety analysis.
4. Describe the use of artificial intelligence in structural health monitoring, damage detection, deterioration prediction, and remaining service life estimation for buildings and infrastructure systems.
5. Explain how artificial intelligence supports infrastructure risk assessment, structural reliability analysis, lifecycle cost evaluation, and resilience planning under varying environmental and operational conditions.
6. Evaluate data quality requirements, validation procedures, uncertainty considerations, and reliability factors associated with AI-assisted structural engineering analyses.
7. Assess professional liability, ethical responsibilities, regulatory compliance requirements, and risk management considerations related to the use of artificial intelligence in structural engineering practice.
8. Apply best practices for implementing artificial intelligence technologies within structural engineering organizations, including technology selection, workflow integration, workforce training, governance frameworks, and pilot deployment strategies while maintaining engineering judgment and accountability.