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Recently, the topic of conversation in the construction industry is definitely the “introduction of AI.” Many sites will actively adopt intelligent solutions in line with smart construction trends for better management and efficiency.
However, when applying advanced AI to the field, the executive team said, “It's amazing, but after all, I have to take care of all the detailed checks myself?” It often leaves me with a sense of regret. If you introduced AI because you wanted to make good use of it, but it just doesn't work as smoothly as expected, and it feels like something out of place, why?
It's not a performance issue with AI itself; AI can properly understand our scene This is because there is no “context.” I'm going to talk about the 'digital infrastructure' that must be in place at our site in order to freely use the solution as desired.
In order for AI to produce proper results, a specific “context (Context)” is essential. The problem is that the context of a construction site is not in documents, but in the offline reality of “space, time, and state” itself, which changes every day by digging and framing the ground.
In order for AI to fully understand this scene, everything in the field must be infused with digital data. However, the actual data is thoroughly fragmented. “I wanted to build it like this” Plan, “It was actually built like this” Construction (AS-built), “I built this much, so ask for money.” Readiness (Progress) The data is scattered across different systems and staff drawers.
Painful rework breaks out due to differences between design drawings and actual construction locations, and exhausting disputes with partners every monthly settlement are also due to the fact that in the end, everyone is looking at different data. As long as field records are fragmented, even the best AI can only read fragmented documents from the past and cannot pinpoint the real context needed in the field right now.
The first thing we need to overcome this disconnection SSOT (Single Source of Truth) It's a construction.
SSOT is not a grandiose concept. It means aligning fragmented design, construction, and ready-made data in a single line on the same physical reference (XYZ coordinates). It groups together what we designed for a specific space, how it was actually constructed, and what the current progress is in a single standard. If a time axis is added to this, it is possible to clearly track time series how that space has changed.
Here, we need to change the way we look at 'digital twins' on construction sites. If digital twins up until now have remained an amazing “3D viewer” that displays design drawings in three dimensions, now they must evolve into a new operating system that accumulates what construction was carried out and what changed yesterday on a daily basis. The digital twin must be the foundation for a “world model (world model)” that AI can judge and execute. When the client, construction company, and partner companies all share this single reality data, the debate up until now turns into objective fact-checking.
Once PLAN, AS-BUILT, and PROGRESS are arranged on a single standard, the scope and depth of the AI introduced to help practitioners is completely different.
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In the past, there were many unclear disputes due to the number of dump trucks dispatched or sampling surveys. However, if the coordinates and quantity of the completed construction section are recorded as 3D data, the pain of manual counting disappears. Through DTM (numerical topographic model) technology with cm-scale errors, settlement disputes between partners are blocked at the source with objective figures.
The error between the drawing and the actual location of the site results in huge rework costs. By simply overlaying continuously updated drawings on top of the latest orthographic images or 3D models taken with a drone, it is possible to detect early construction risks that are difficult for humans to check as they walk around. It's about preventing rework that will get as big as a snowball before pouring concrete.
When construction data is linked to a design schedule, actual progress compared to plans can be intuitively visualized and the uncertainty of air delays can be eliminated.
There is only one main premise for all of these changes. It means that design, construction, and completion must be firmly linked on a single physical standard.
True competitiveness in the AI era doesn't end with adopting better inference models. It is completed by how well and accurately I can convey the “context” of a difficult scene that I know to AI.
At the construction site, the context is SSOT, which arranges design, construction, and ready-made data on a single standard. When this is in place in the field, AI can accurately read the current state of the field, and from then on, true automation and insight will have an explosive synergy. Modernizing a solution is the next story after the foundation has been laid.
Meisa is a solution that fills this empty context. Extensive and complex field data is unmanned via drones and satellites, and automatically converted into precise 3D models. On top of that, design, construction, and completion are integrated into a single platform, and it is a powerful foundation so that the AI you have introduced to the site can intelligently grasp the situation and function as expected.
If you want to freely and fully utilize the AI solutions you have introduced, please work with Meisa to establish an unshakable data foundation.
