See Through Walls: Adaptive Reuse Through Data, AI, and Circular Design

"Behind layers of plaster, paint, and finishes lies an intricate network of pipes, electrical conduits, beams, and other structural elements that make a building function and stand, yet remain invisible to the everyday eye. Within these layers, traces of different periods accumulate: replaced systems, improvised adaptations, and technical solutions that once responded to specific contexts and urgencies. In adaptive reuse, the greatest challenge often begins before construction even starts, which is understanding what lies within when little or no reliable documentation exists."

"By combining artificial intelligence, sensors, and predictive modeling, the teams are developing an experimental tool capable of "seeing through walls." This system uses multiple sources of information, such as laser scans, sensors, old floor plans, and geospatial data, to create intelligent three-dimensional models of existing buildings. From these models, AI can infer and predict what is not directly visible, including the position of pipes, the condition of materials, or the structural health of certain components."

"According to David Benjamin, Director of AEC Industry Futures at Autodesk, the goal is to offer architects and engineers "a kind of x-ray vision for buildings." As he explains, "Laser scans are great because they can tell you about all of the visible aspects of an existing building. But they can't tell you about the invisible aspects, including what is inside the walls and the condition of each material. If we can create a ki"

Hidden building layers contain pipes, conduits, beams, and accumulated traces of past systems that remain invisible yet affect functionality and structural health. Renovations often encounter unexpected conditions that increase cost, delay, and risk, creating barriers to adaptive reuse. Autodesk and Arcadis combine artificial intelligence, sensors, laser scans, old floor plans, and geospatial data to generate intelligent three-dimensional models that infer in-wall systems, material condition, and structural health. Predictive modeling across multiple data sources enables improved decision-making, cost estimation, and risk assessment where direct documentation is lacking during renovation projects.

Read at ArchDaily