Preserving the future by modelling the present
The redevelopment of Melbourne’s State Library of Victoria began by digitising this significant historical building. Three-dimensional laser scanning was deployed to produce a 3D virtual point cloud model, which was subsequently used as a reference to produce a ‘digital twin’. This elemental model was then converted into intelligent building components within Revit, enabling the team to progress design and documentation packages in the preferred parametric modelling application.
Paris’ own Notre-Dame was digitised in a similar fashion back in its prime – a decision that holds enormous weight after the recent fire. In the wake of the fire, new design proposals are referencing the digitised digital twin, helping architects to make alterations and compute multiple solutions for the rebuild with far greater ease.
The more data you input, the more ML improves its predictive abilities. Successful AI iterations will therefore result from larger, more comprehensive datasets. If we start digitising our built environment, we’re not only preserving architectural feats for the future, but training the AI in the process too.
How we see the role of the architect evolving
For designers with deeply held convictions that architecture is ‘the mother art’ (quoting Frank Lloyd Wright), rest assured that the aesthetic and qualitative nature of architecture can absolutely co-exist with the more functional aspects of computation. In fact, the most memorable and successful infrastructure is likely to be a marriage of beautifully curated design at the hands of artists, paired with an enjoyable user experience led by computation.
Until the next major technological breakthrough, machines will only be able to solve direct design problems and generate design suggestions for a human to take and develop further. For example, ML could forecast student numbers and room utilisation to quickly generate the briefing requirements for a university campus’ location, size and functional requirements. But the machine can’t prioritise these design considerations on its own, nor respond to human-centred considerations – architecture’s philosophical criteria, such as respect for client values and needs, response to place and context, enacting environmental ethics, or influencing mood through aesthetics.
There’s an opportunity for our design culture to evolve from being relatively uninformed and reactive, to becoming highly informed and predictive, but we need to act now.
Five steps to join the era of industry 4.0
When 3D modelling tool Revit first arrived on the scene, Australian architects were some of the world’s earliest adopters, with local users outnumbering Europe and the US combined. As a country, we’re often ahead of the curve, and should be building the same culture of adoption with AI. Here are five ways to get involved, whether you’re a CEO, designer or software engineer:
Gain computational knowledge
Delve into new research, or partner with universities to leverage evolving student skillsets – today’s architecture graduates are intimately acquainted with the technology that some of us had to start from scratch to learn. We’re now seeing companies invest in technology innovation specialists with software engineering expertise.
Network within the computational design community
There are several Computational Design meet-up groups across Australia and conferences (such as Para Guru) where you can discover the latest innovations, and find opportunities to collaborate when the time comes for implementation.
Get quick, easy wins and build on that success
Build an internal working group and start testing available computational tools for your current projects. Just as ML requires more data to improve its predictive abilities, we need more experience and experimentation to improve our relationship with new tech.
Implement a regular technology review on projects
Solutions evolve so fast that you may be able to integrate automation later down the line on a project that today can only be executed manually. Incorporating computation into the conversation at every touch point is key.
Involve other sectors
As we integrate the knowledge of separate building disciplines into the AI, we can lower barriers of entry into the profession, gain insight about the underlying data in the rich tapestry of infrastructure around us and reduce latency of communication and chance of error at earlier stages.