The needs, movements and preferences of building occupants can’t be considered only after a building has been constructed. A building that has been designed for the end user doesn’t just happen. People-focused strategies need to be followed at every stage of the project, from design right through to construction and operation.
During the design phase, everyone from facility managers to the staff that will be using the building should be included in workshops so that designers and engineers understand their unique challenges, pain points, and daily tasks. Leveraging visualisation and community engagement, expanding the application of design models, and understanding the end user sentiment requires an agile design approach.
Visualisation also enables stakeholder engagement to be managed around the effect of massing and how a proposal fits into the existing environment and allows groups to come together to be bold and achieve a solution for all. Aurecon, for instance, used visualisation to explain building access and operation to the facilities management team for a building we utilised in the Oman Across Ages Museum.
Human centred design is enabled through collaborative design, rapid prototyping and optioneering. When the right stakeholders are brought in during the concept and design phase and you’re able to make ideas tangible and get quick feedback from the people that you are designing for, then the building designers and engineers can learn through producing.
Prototypes don’t have to be perfect from the get-go, they need to represent a concept that’s open to adjustments and optimisation. This type of optioneering lets you test ideas within a continuous feedback loop so that you can make sure you are on the right track. It’s essentially a practical, repeatable approach that will help us arrive at truly intelligent, human-centred buildings.
Emotionally intelligent buildings will force us to better leverage our design data and communicate them in different types of visualisation scenarios, especially with regards to daylighting, indoor environmental quality and acoustics.
Some examples of this include auralisation, where acoustic modelling is used to simulate noise to optimise building designs and influence sound masking within buildings, Computer Fluid Dynamics (CFD) analysis to understand the efficacy of a building’s façade and HVAC system, for example, as well as digitised wayfinding and pedestrian modelling solutions.
While these visualisation options could be deemed costly, it must be put into the context of the overall cost of buildings of the future to translate a better return on investment for owners and developers.
During the construction and operation phase, data insights and sensors can be used to gain valuable feedback and target specific problems. These rapid insights can be used to justify larger, more costly adjustments to the design.
When building systems are installed, we need to investigate ways that machine learning will eventually be able to take data derived from these systems and enable a building to manage itself.