Modernizing the Architectural Profession: A model for a decentralized democratic governance

(Preview) - Updated December, 19, 2024

Current workflow:? Project-centric models, where Building Information Modeling (BIM) tools like Revit are used to create isolated, site-specific models

Proposed workflow: Work on a “module,” which represents a segment of the city - and access multi-level information including city services such as water. Allow to see proposed work, etc - see information in real-time and have predictive analysis. Current workflow:? Steady-state model - with limited optimization and simulation capacity.?

Proposed workflow: dynamic modeling - using live-climate data enables simulation in current and predictive weathers. (See, current building codes use a sort of 100 year storm to design for our buildings which then are plugged in to design. So you are designing for the worst storm in 100 years - and again, like how our mind works, our design is based on the strongest storm from the past 100 years.) The proposed model uses artificial intelligence and weather - , and allows for predictive analysis too - simulating changing climates.?

Economies of scale and scope:?

The integration of artificial intelligence directly into the power grid offers potential for the development of a digital twin to capture both economies of scale (lower costs as usage grows) and economies of scope (efficiencies from multifunctional systems). The proposed framework will require in-person assessments of hundreds of thousands, potentially even millions, of buildings. This extensive effort highlights the scale of data collection necessary to create an accurate and comprehensive digital twin of the city, enabling real-time simulation and AI-driven predictive analysis. Currently, many infrastructure projects operate in silos, focusing on isolated goals without integrating broader data needs. By coordinating efforts and capturing additional information—such as adding structural data during energy performance assessments, we can significantly enhance efficiency and reduce costs. A high-level coordination framework ensures holistic data collection, maximizes resource use, and eliminates redundancies, unlocking substantial savings and operational improvements.Key layers of information include structural information, energy performance data, water and plumbing data, safety and compliance, occupancy and usage, maintenance and wear, advanced parameters (acoustics, air quality, thermal performance), and environmental and surrounding data. Additional technology such as LiDAR (Light Detection and Ranging) technology is incredibly versatile and essential for capturing spatial and geometric data, such as topography and elevations, as well as building features, including facades and complex geometries. It can also detect surface features such as cracks, deformities, or surface wear on buildings and infrastructure and provides? information on vegetation analysis, surface water bodies, and solar exposure patterns for solar energy optimization and shading design. Such a multi-layered Digital Twin Framework with structural, environmental, and functional data enables dynamic, real-time responsiveness and the integration of city-wide and building-level simulations. Similar to the modular and scalable framework proposed by the AI-driven communication protocol to integrate architecture directly into the power grid, the digital model can be built using layers of information. For example, zoning by-laws can be translated into the model by identifying existing zones and integrating their respective codes, enabling automated checks in real time. These layers act as a coordination mechanism, allowing specific aspects of the digital model—such as zoning, energy performance, or structural details—to be easily identified, updated, or analyzed independently.

By structuring the model with layers, information is not only organized systematically but also becomes inherently modular. This allows for dynamic updates and edits without disrupting the entire system, enabling stakeholders to focus on specific elements while maintaining overall coherence. Consider again the economies of scale and scope in which the new digital infrastructure to modernize the AEC industry also provides a path towards integration of artificial intelligence directly into the power grid which itself acts as a foundation for an artificial intelligence driven climate emergency and disaster management tool. Paradigm shift in the Architecture, Engineering, and Construction (AEC) industries. The current workflow in the industry typically revolves around project-centric models, where Building Information Modeling (BIM) tools like Revit are used to create isolated, site-specific models. Collaboration is limited to the stakeholders directly involved in the project, with minimal integration into a larger, interconnected urban context. This siloed approach can lead to inefficiencies, redundancy, and a lack of alignment with broader city-wide infrastructure or planning goals. An important step towards addressing Canada’s severe housing crisis requires creating efficiencies in the industry’s workflow. In contrast - the proposed model allows architects, engineers, and planners to work directly on a module that represents a specific segment of the city within the digital twin. This module is interconnected with the broader urban system, allowing for seamless integration and collaboration. By working within this dynamic, city-scale framework, professionals can address zoning, infrastructure, and urban planning holistically, while real-time updates and shared data ensure alignment across all stakeholders. This approach not only enhances efficiency but also fosters a unified vision for urban development. By embedding processes such as code compliance checks, zoning approvals, and structural validations into the digital twin, the system automates and streamlines workflows. This approach not only accelerates approvals and improves accuracy but also fosters collaboration across disciplines, ensuring a seamless integration of design, regulation, and urban planning into a unified digital ecosystem.The proposed can enable smart-contract, and further automate the industries.?

The United Federation of Corporations (Federation) will fund the development of transformational infrastructure and a paradigm shift in urban management, completely revolutionizing the workflow. Here, instead of architects and construction professionals using standalone software like Revit or AutoCAD to create individual models of buildings or projects, the design team works directly with the corresponding module of the city.

By framing the digital twin as a disaster management tool first, it gains foundational funding and justification. However, its secondary capabilities—smart contracting, zoning automation, energy modeling—unlock immense cost savings, accelerated timelines, and smarter urban governance. Further - in Canada alone - we estimate the AEC industry spent 1-2% of the total AEC industry revenue on software licensing for modeling, rendering and other project management tools costing around 2 to 5 billion. AEC firms also spend upwards to 15% of their budget on modeling softwares essential to the industry. From here - a grassroot driven revolution connecting universities, research centres, and firms across the world to collaboratively build the new workflows and workspaces will allow the professions to adapt to the 21st century, both reducing cost for itself, and the city while accelerating efficiencies.?

It's also critical to note the development of a digital twin represents a new digital infrastructure to revolutionize the profession, but also - such a provider provides a platform to re-think how we communicate, collaborate towards better design.??

Mathematical Foundation:?

While the existing workflow such as Revit for the designer is steady-state, with limited simulation abilities, the proposed digital infrastructure is dynamic, enabling long-term simulation using artificial intelligence and predictive modeling.

Further, the Federation proposes integrating the Navier-Stokes equations as the core computational standard and using similar BIM language models such as Industry Foundation Class. Similar to the framework presented in the AI-Driven Communication Protocol, we apply it to the city by dividing the city into larger modules (blocks, districts). These modules communicate with each other to account for broader phenomena like air and water flow or traffic dynamics. Further, within these district-level modules, there is Building-Level Discretization, where the building itself represents a module. Each building module undergoes further discretization, including rooms, walls, windows, HVAC systems, etc. Even smaller modules are integrated—for example, the wall has multiple layers. Each module is bounded by boundary conditions that communicate with neighboring modules: a building interacts with the city’s external airflows, ground conditions, and neighboring buildings.

The Navier-Stokes equations are ideal because of their universal applicability and ability to govern the motions and interactions of fluid substances, including air and water, making them perfect for modeling weather dynamics, airflow inside and outside of buildings, and heat transfer and ventilation within structures. While Navier-Stokes equations act as the foundational communication standard, other specialized equations (like solid mechanics) are layered or overlaid as needed for specific scenarios.

Consider how a storm is simulated to understand its impact on an urban area. The Navier-Stokes equations model wind flow and storm dynamics at the city level, outputting localized pressures and velocities on buildings. Structural analysis equations then simulate stress, deformation, and potential damage to building components.

Further, the Federation recommends employing finite forward differencing (or explicit methods), where the solution at the next time step is computed explicitly from the known information at the current time step. The proposed hierarchical discretization provides flexibility for each module to operate on its own timeline, providing significant adaptability.