Artificial Intelligence and BIM
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The document outlines the role of artificial intelligence (AI) in the architecture, engineering, and construction (AEC) industry, emphasizing the need for cross-discipline integration and the evolution of design processes through AI. It discusses various maturity levels of AI implementation in design, ranging from collaborative design to fully autonomous systems, and provides a roadmap for organizations to transition towards advanced parametric and generative design capabilities. Additionally, it offers actionable steps for implementing AI in design management, problem definition, and solution development to enhance overall productivity and efficiency.
Artificial Intelligence and BIM
- 1. AI & BIM AI in the AEC industry February 2020
- 2. Why should we care about AI?
- 3. Our Path Forward To move towards AI we need to think differently from the past, in the future software will be an integral role in developing solutions. We need to get better at cross discipline integration for non- traditional approaches. Algorithms will begin to replace repetitive functions, before the more advanced decision making. AI maturity will complement existing skills, changing the dynamic between Designers, Engineers and Programmers. Multi-skilled staff will be in high in-demand. Engineer Drafter Engineer DrafterDesigner AI 80s 90s 00s 10s Source(s): Modified from an original concept by Craig Lamont (2016) 20s 30sEngineerProgrammerAI Engineer Designer Engineer Designer Drafter ProgrammerAI Engineer
- 4. Parametric Design Iterative / Codified / Rules based / Parameterised / Constraints based Benefits Can generate a large number of options Simplifies future design Automation Cost reduction Dynamic
- 5. Generative Design We’re able to deliver both parametric and generative design. Here we’ve applied it to RET design.
- 7. How mature are you? Level 0 Level 1 Level 2 Level 3 Level 4 Level 5 Collaborative design Parametric design for single functions. Parametric design for multiple disciplines. Automated design for a single discipline. Automated Verification for a single discipline Fully autonomous design and verification for multiple disciplines, but a limited set of knowledge domains. Fully autonomous design, acting like a human across a wide range of knowledge domains. You’re living here You might holiday here You could live here tomorrow You could live here in 6 months You need to be here in 1~2 years You need to be here in 3~5 years All competitors live here Some competitors live here Some competitors live here Some competitors are holidaying here Competitors are researching a holiday here Competitors are dreaming of living here Copyright: Alex Ferguson (2018)
- 8. What can you do today? Level 0 Level 1 Level 2 Level 3 Level 4 Level 5 Collaborative design Parametric design for single functions. Parametric design for multiple disciplines. Automated design for a single discipline. Automated Verification for a single discipline Fully autonomous design and verification for multiple disciplines, but a limited set of knowledge domains. Fully autonomous design, acting like a human across a wide range of knowledge domains. Encourage teams to look beyond traditional delivery approaches towards Level 1. Generate awareness of the developing toolset Invest in parametric design capabilities. Strategically hire individuals with this expertise. Leverage existing expertise to solve repetitive tasks. Implement design management processes that require the extensive use of parametric design to deliver outcomes. Invest in fast- tracking research and development of automated design solutions alongside industry expertise. Implement design management processes that require use of automated design to deliver outcomes. Invest in research and development into the use of AI for design development Copyright: Alex Ferguson (2018)
- 9. What could you do today? Level 0 Level 1 Level 2 Level 3 Level 4 Level 5 Collaborative design Parametric design for single functions. Parametric design for multiple disciplines. Automated design for a single discipline. Automated Verification for a single discipline Fully autonomous design and verification for multiple disciplines, but a limited set of knowledge domains. Fully autonomous design, acting like a human across a wide range of knowledge domains. Encourage teams to look beyond traditional delivery approaches towards Level 1. Generate awareness of the developing toolset Invest in parametric design capabilities. Strategically hire individuals with this expertise. Leverage existing expertise to solve repetitive tasks. Implement design management processes that require the extensive use of parametric design to deliver outcomes. Invest in fast- tracking research and development of automated design solutions alongside industry expertise. Implement design management processes that require use of automated design to deliver outcomes. Invest in research and development into the use of AI for design development Copyright: Alex Ferguson (2018)
- 10. Get Started Problem Definition •Understanding the design intent and desired outcomes •Review existing processes for production and opportunities for parameterisation •Identification of the problem to be solved through parameterisation •Estimate the cost, time and resources to create a solution in traditional ways (benchmark) Define Parameters •Definition of the problem in terms of the key inputs and variables that are to be manipulated •Confirmation of the intended inputs and outputs of the solution •Confirmation of the extent of control or level of automation to be applied to the solution •Confirmation of any constraints Define Architecture •Identify suitable software platforms and capabilities •Develop a concept of the parametric solution •Agree a high-level definition of the standards, rules and frameworks to be applied •Plan for and agree the cost, time, resources to be used. •Confirm parametric modelling equates to positive value (i.e. compared to benchmarks) Develop Parametric Solutions •Commence development of the parametric model •Review of the model at each stage for each discipline to validate approach •Independent verification of the model outputs for given inputs •Document the solution in code as required •Document information required to operate the model Solve the Problem •Use the model to generate outputs with client input •Review model outputs with the client •Confirm design objectives are being met •Generate final design deliverables •Record details of the problem solution Finalisation •Archive the project •Extract and document IP created which may be reused in future projects •Publish reusable modules and code to knowledge management system •Document lessons learned and publish to knowledge management system
- 11. Alex Ferguson Director of Digital Projects
Editor's Notes
- #3: You can see the strength and speed of development of AI in software like AlphaGo, which, in the span of 6-9 months, went from being unable to beat a reasonably good Go player to beating the previous world champion 4-5, the current world champion, then beating everyone while playing simultaneously! Then there was AlphaZero, which shortly after, crushed AlphaGo 100 to Zero and AlphaGo just learnt by playing itself, and it can play basically any game in which you input the rules. It is literally, read the rules, play the game and be superhuman It is wishful thinking to believe an AI is not going to replace us. It is capable of vastly more than almost anyone knows. Source(s): Elon Musk (2018) AlphaGo – better than a human in less time to train, better than an algorithm which brute forces to check every solution, 2000 years of history of “this is how the game is best played” to determine strategy and AlphaGo plays moves that humans believed to result in certain defeat, only to win. It has mastered the art. OpenAI – 5 vs 5 competitive gameplay decision making, long term and short term memory, how might this improve our ability to manage risk, identifying strategies for delivering projects in adversarial environments. BIM 360 IQ – about implementing AI / machine learning algorithms on top of data collected during construction. Dynamo / Grasshopper – enabling iterative design – effectively creating search heuristics to find optimal designs that meet desired criteria AI & Architecture – Sketch a building and have it designed for you, input a space constraint and have an AI generate a suitable layout based on your space usage requirements Generative Design – Automatically lay out multi-discipline MEP services in a service corridor Question.. How many lines of code do you think it takes to do the last task?