Professional Practice
What are the legal ramifications of utilizing AI tools for structural analysis and design?
The use of AI in structural analysis and design does not change the traditional definition of responsible charge. Engineers must evaluate and validate AI-led closed-form designs and machine learning (ML) results just as they would any other black-box solution.
Is it ethical for a licensed engineer to base design on AI, given its potential inaccuracies? What ethical challenges arise when AI is used without the supervision of a professional engineer?
AI should not replace the judgment of the engineer. AI-generated designs must comply with all relevant building codes and meet safety and performance requirements. Licensed engineers are ultimately responsible for the integrity of their designs. Although AI can complement an engineer’s expertise, there may be limitations such as potential inaccuracies and biases. Thus, the engineer remains the final authority to ensure public safety and compliance.
Are there any current regulations regarding the use of AI by professional engineers?
Currently, there are no specific regulations exclusively addressing the use of AI by professional engineers. However, engineers must adhere to existing professional standards and guidelines, ensuring that AI-based designs comply with all relevant codes and regulations.
How do you foresee professional liability being affected by AI usage in design and analysis?
If AI contributes to a design error, omission, or failure, it is comparable to mistakes made by interns or junior engineers. Engineers will need to develop robust oversight mechanisms, maintain professional judgment over AI recommendations, and collaborate with legal experts to adjust contracts and liability frameworks as AI becomes more integrated into the field. Licensing boards will need to adapt their training and regulatory frameworks accordingly. It is anticipated that there will be lawsuits stemming from inadequate and unsafe designs due to AI use.
Education and Training
What steps should companies take to prepare for AI integration?
The approach to AI integration varies depending on the size of the firm. Small firms may focus on being consumers of AI tools, while larger firms might consider developing AI solutions. Key steps include utilizing available resources such as AI policy guides and getting started guides, and ensuring staff are familiar with software engineering fundamentals. Aligning AI integration with the company’s business strategy and client needs is crucial.
Where can companies find AI training?
Recommended resources include Massive Open Online Courses (MOOCs) such as Coursera, DeepLearning.AI, MIT Open CourseWare, LinkedIn Learning, and platforms like Kaggle. Open-source platforms for research papers, such as Arxiv, are also beneficial for staying updated with current research.
How can AI be integrated into engineering education to benefit future engineers?
AI can be integrated into engineering education by incorporating AI-related courses and projects into the curriculum, encouraging students to participate in AI competitions and research, and providing hands-on experience with AI tools and software. Collaboration with industry professionals and exposure to real-world AI applications can also enhance learning.
How can a small/medium-sized firm harness AI without it being an overly burdensome cost?
Small to medium-sized firms can benefit from being consumers of AI tools rather than developers. Choosing the right tools for the business and understanding both the technology and the business use case is essential. Innovations like using Large Language Models (LLMs) for coding and data analysis can improve workflow. Privacy considerations must also be taken into account when using AI.
Workflow, Collaboration & Technology
What are some of the easiest AI applications that structural engineers can start implementing in their work?
Engineers can begin with AI applications such as ChatGPT or Claude for summarizing technical articles and writing quick code. Microsoft Copilot, an add-on for Office 365, is a user-friendly tool that integrates with various Office apps for tasks like creating presentations from notes.
How can AI be used to create structural engineering reports?
AI can assist in generating reports by systematically organizing project information from site visits to analysis and design. Although no comprehensive tool exists yet, simpler versions like ARIO (Automated Reconnaissance Image Organizer) can organize photos using metadata and image recognition.
How can AI assist in structural design beyond administrative tasks like writing letters/reports using GPT?
AI can help with complex design problems such as optimizing elevation levels for belt trusses, adaptive reuse of structures, and producing multiple design alternatives based on specified constraints. AI can also provide near real-time structural analyses and strategies for climate change adaptation.
How is AI being used currently in structural engineering? Is there any engineering software that has implemented AI to assist with analysis or design?
AI currently plays a supportive role, complementing engineers’ work rather than replacing critical human oversight and judgment. Use cases include cloud platforms reducing tedious work during construction administration stages, AI-driven optimization studies, real-time health monitoring, and machine learning applications in wind engineering. Some AI-based connectors are available for software like Revit, although mainstream engineering software has limited AI integration.
What structural design AI tools exist on the market today?
Notable startups in this field include Asterisk (TT core), AREN (health monitoring), Augmenta (automated design for electrical systems), Hypar (automated design), UpCodes (smarter building code search), and Genia (automation for single-family homes).
Answers as of October 2024.