Rooms of the Future: Understanding and Using Rooms as Vital Assets in Digital Twins

BOYD JOHNSON: Hello, and welcome to this industry talk for Rooms of the Future-- Understanding and Using Rooms as Vital Assets in Digital Twins. Just a brief safe harbor statement. You can pause this and read it if you'd like. I'm just going to move on.

So we'll just jump in here and get started. First, let me introduce myself. My name is Boyd Johnson. I'm a senior architect and BIM manager at AECOM. My work in AECOM's transportation business line out of our Denver office. My focus is primarily in the aviation market sector, providing digital twin services for airport clients all over the world.

And I've been with AECOM for about 10 years. I've been involved in parametric modeling and design and really digital twin type services all the way back to the early 2000 when Revit was kind of just coming out. In addition to my focus and our group's focus at AECOM, our digital delivery group at AECOM, we work throughout all different market sectors.

If you're familiar with AECOM, AECOM is a very large, probably one of the world's largest, AEC firms. And AECOM is involved in all aspects of the AEC industry, from design and construction to management and pretty much every market sector in there. And our work within digital delivery, digital twin services for our airport clients, has grown quite a bit. And we service multiple market sectors now, not just aviation clients. But primary focus is on aviation clients.

They are interesting clients. They are basically like a city. They need to be in operation 365 days a year, 24/7. And they've got a lot going on, mission critical lives at stake. We all know what airports are. So they're really a dynamic and interesting client and that we really enjoy working with.

Assisting me with my presentation, actually, at AU-- he's not here at this time-- is Derek Milz from Autodesk. Derek is a customer adoption specialist with Autodesk, specifically helping clients of Autodesk to adopt the Tandem platform, which we're going to talk about that towards the tail end of this presentation.

So let's get started into the actual topics. Like I said, this is an industry talk. And the primary focus is to share our collective experiences and thought leadership and the insights we have across multiple market sectors. In addition, really kind of want to push forward with new and innovative ways we can bring forth the greatest value out of digital twins, not only today, but into the future as this technology continues to evolve, as organizational silos are broken down and overall awareness of what digital twins can and what we want them to be.

I think the industry is still really trying to-- well, we're all kind of clamoring for, I think, new ways that we can use digital twin. I don't know how many times I'm going to say those words, but it'll probably be a lot. So it's very interesting. I think there's a lot of opportunities. And I really don't think we know quite what the end is. We'll talk a little bit about what the end or the final completion is. But I think there's just a lot of room in front of us to continue to explore what digital twins really are.

These topics, we're going to focus on three main objectives in this industry talk up on the screen, the significance of rooms within a digital twin environment, the importance of developing well thought out data schema in support of rooms in a digital twin. And finally, we're going to show some examples of how Autodesk Tandem can provide real time monitoring and analysis of the physical world kind of through the perspective of rooms.

Well, we can't talk about digital twin without showing some type of futuristic, mysterious, spacey, digitally glowy series of interconnected objects. So these are just some of the things that we always see. I think it's been pretty common for describing digital twin. These are maybe a little more, maybe a little darker than some. But it seems to be a very common, blue connected lines, glowy lines, and then, of course, blue glowing cubes with somebody always kind of gazing deep into this mysterious cube as if it could provide the secret of life. So Tandem person on the right. And then we know what's on the left, I would think.

So let's dig into the first objective here and what's important about rooms in a digital twin. So these images here 10,000 years ago. The only, probably, concern that we had as humans was where you were, staying safe, surviving, having food, getting food.

But it wasn't until really, as humans-- and this was probably 10,000, 12,000 years ago, until we actually began to understand agriculture and really be able to store up food is when we stop moving around. And at that point, we stopped or began to decrease being nomadic because we could stay in one place.

And so chances are if you're watching this, you're not nomadic. You're sitting in a room, you're sitting in a room or something that you understand the context of where you're at. Pretty much our world these days and the developed world exists within the context of a series of never ending locations, whether you're in a home, conference, an office, an airplane, whatever. Those are locations that we can actually put our finger on.

And the commercial kind of AEC industry, these are comprised of very large, complex campuses. They're very complicated. They're very diverse. And these types of large enterprises have a significant need in value investment regarding locations and understanding where things are. So that need for value today, more than before, really comes from BIM and GIS and connecting data.

And I think because of those systems and because of gene learning, AI-- and BIM's been around for 25 plus years now-- and everything that GIS does and asset management systems, definitely AI, we're beginning to be aware of the enormity of data and either how poorly it's organized or how we can begin to use it.

I think these systems that I mentioned, BIM and GIS, are definitely systems that definitely give us a taste of the potential. And that's really that kind of potential of digital twin that we might be able to achieve someday that makes us really shed some light. It's like [GROANS], our data is not as organized as it should be or it could be. Or there are many things that we have an inclination that are out there, like data points or understanding or assets and ability, that we just don't have-- we don't have insight. We don't even know where they are and their condition and so on and so forth.

And my perspective is primarily coming from airport perspective, where 50, 100s, 100s of buildings and multiple miles, square miles of area. And so assets are diverse and distant. You can't walk up to them, usually not easily. And so, again, there's a strong need there for understanding digital twins and utilizing digital twins.

So jump in here now in regards to the first topic and really the roles of rooms in a digital twin. So first topic really is kind of accurate spatial representation. Rooms within a digital twin should be detailed and accurate as possible, representing the spatial layout of a physical environment. Some of those obvious requirements would be dimensions, accurate dimensions, the geometry, spatial relationships, allowing for digital engagement by users to visualize how different areas are organized.

This level of accuracy is really key for various other applications that we'll talk about. We all know the saying location, location, location, it's very important. But we do have to understand what that location is to really be able to define it.

Some of those examples would be building management systems, understanding space utilization or the simulation of the physical world in the digital world. The first, when I first made the comment, it says it should be accurate. I want to just make a note that the image there on the bottom represents LiDAR. And I think we all kind of have an understanding of LiDAR and the significant value that it provides in capturing the physical world and getting it digital with tremendous amount of accuracy.

But again, coming from an airport perspective, you can't just go out there. And it's financially probably cost prohibitive to go out there and scan every single building on a large airport. It's just not possible. And so we have to find innovative ways to begin to, how can we-- we don't have endless amounts of money. Does that mean we can't stand up a digital twin?

No. I think there are many ways, and we work with many of our clients, to stand up those digital twins with what they have. And that top image is one of our airport clients. And you can kind of see. It's probably a 1950s JPEG of a floor plan. And we did we took that and we can stand it up pretty quick into a BIM, at least at a kind of basic level.

And so those are just, I think, important things to keep in mind as it relates to I want to do a digital twin. Do I have to do everything, and I have to capture everything? No, you don't. There are methods and things that you can incrementally grow in a maturity level with digital twin.

So rooms provide context for systems and assets. So these rooms form this contextual framework within assets, things that are very important within digital twin, such as any type of machinery or HVAC systems or sensors, how they're placed. Without these defined areas, defined rooms, it would be difficult to track or to monitor assets or behavioral relationships to their surroundings, understanding what's going on in those spaces.

An example would be when a room when a room sensor goes off, where is it located, doing specific environmental conditions, whether it's fire or heat or CO2 levels and things like that. We always want to understand where things are within the context that they are.

So another one is enhanced simulation and mining. So many digital twin applications allow for the simulation of scenarios like energy usage, foot traffic, or emergency situations. They're dependent on just well-defined spaces.

That image up there at the top is actually a runway. You could say that's not a room. But again, the world that we live in, room is not just something confined by walls. We put rooms or spaces, identify locations out on the horizontal airfield, and same around other buildings, not just at airports. They, too, are important areas that we want to identify. So they may not have walls, but they're just as important.

So accurate rooms layouts enable for better scenario modeling, such as understanding how air flow behaves in a ventilation system, how people might evacuate a building, or how energy consumption can be optimized across different spaces. We'll talk about that briefly towards the end when we get into Tandem.

So the other one is obviously, I think-- it's fairly obvious-- facility management operations. For a smart building, rooms and spaces within a digital twin allow facility manager to monitor and optimize operations in real time. And this includes controlling HVAC, lighting, security systems. These are often mapped to specific rooms or to zones, or cluster of zones.

And these facility managers can remotely manage these spaces, adjusting settings and respond to anomalies or things that are going on in the spaces. Utilization, so understanding what's going on in a space. And this is not so much a utilization of maybe, I'm an architect and how do we want to use our space? A little bit more on the IoT side, kind of live information of how spaces are being utilized.

So digital twins allow us to track these rooms and spaces and how they're utilized. This is kind of a crucial sector, crucial in sectors like real estate or office management or higher education, where we really need to optimize the spaces and how they're being used.

They have the potential to provide a significant cost savings by analyzing that occupancy data and how the organizations are deciding to use them or potentially relocate, provide additional energy saving measures, potentially even downsizing, obviously, things like post COVID. We're seeing a lot of that.

Safety and emergency response, it's vital. A digital twin, accurate information-- Somebody's having a heart attack. You need to get somewhere. You need to understand, emergency responders need to understand where they need to go. So obviously, then in addition, fire evacuation routes, any type of hazardous situation, just that management emergency response.

Knowing how spaces are connected allow responders to act more efficiently by planning safe evacuation routes, detecting the affected areas. Resiliency is another piece in there, as well, just of understanding how can we respond to those kind of unplanned situations?

So obvious things, I think interactive use, I mean, phones have been around for 15 years now, I think, just under-- smartphones have been around for 15, 16 years. Indoor navigation is something I think we're still kind of striving for. There are some connectivity issues inside, at least deep inside of a building where you may not be able to get a good signal. There are systems that are being utilized, being kind of brought up. Part of that is LiDAR and BIM and other systems that are allowing indoor navigation to become more real.

So it's immersive interactive experience with rooms and spaces, virtual tours, AR/VR. We know about those things for quite some time now, collaborative design, collaborative training, these kind of virtual things that we've known for maybe five plus years now with the invention of that technology.

Just really enhance the user engagement. Individuals be able to interact more with the space from a simulated environment. And then this last one, data integration and real time monitoring, this is where we're trying to get to, I think, a little bit more on the autonomous side. We're really trying to get real time data from IoT devices, which can be associated with specific areas and facility.

We'll talk about that at the end here within Tandem. Definitely be able to monitor environmental conditions, temperature, humidity, lighting, occupancy, kind of room by room basis, optimizing conditions, best for comfort or for just how the spaces are being utilized.

So how do we begin to organize something that is chaotic, potentially scary, or unknown? And I say unknown is like-- I talked about an airport, there are times that who owns what building and who's in that building, how long they've been there? And so just really how to take something really large and what appears to be maybe somewhat unmanageable and begin to get it organized, and primarily from a data perspective.

So a well thought out data schema is essential for creating an effective digital twin, especially for large organizations with complex systems, data schema, or a data architecture really defines how data is structured, stored, and accessed through a digital twin. And we really want to try to focus on how we're doing it at the beginning, try to consider as many things as possible, but knowing that this will continue to evolve over time.

In the context of digital twins, the concept of rooms and spaces, they play a really crucial role as they're kind of a virtual representation of the physical environment. In essence, the representation room in spaces forms kind of a backbone of many of the applications that are used in digital twin. It's either the rooms or the spaces are very vital, as well as some type of the data schema classification system and things like that.

Having this system in place allows us to be reliant on the system, be reliant on the data, and the overall kind of digital twin as it continues to mature. So the first kind of logical thought of if you're in a chaotic environment or a really large environment, that there's just so much data out there that you just don't know what to do, it's like, we got to get organized. And that makes sense.

This doesn't necessarily mean that it's one person getting organized. In a large organization, they're very siloed. You have a lot of departments and things like that where it's not really feasible for one person to make all these decisions. There's a lot of interests that may align and interests that may not align. And how those are managed and considered as you begin to think about a data scheme or standing up a digital twin, those are pretty important.

So getting organized, what does that mean? How do you even begin? So there's a couple of things that we'll talk about here in regards to what are some key considerations when you're beginning to stand up a digital twin and thinking about, really, a data schema?

So scalability and flexibility. The large organizations have pretty detailed systems, assets, and operations. They're very expansive, complex. And they're often siloed, very territorial, egos, fear of change, and understanding this is the way I do it. And there's always the case of the right hand doesn't know what the left hand is doing and vice versa. That is very common throughout large organizations.

And so a digital twin pretty much flies in the face of that. And it doesn't really-- those silos kind of need to be broken down. And there's definitely a lot of change management that is involved in this. And developing a well detailed, well-structured data schema will help to ensure that digital twin can scale as that organization continues to grow and potentially break down those silos, provides flexibility to incorporate new systems and assets or new facilities without overhauling the entire data architecture to adapt with change.

So the data integrity, you definitely need to consider a robust kind of data schema. It helps to enforce that integrity, ensures that information across that digital twin is accurate and consistent and reliable so that you can trust it. If people begin to lose trust in the data, then there's problems.

So a lot of the things that we do for our clients, we spend a lot of time with developing standards and making sure that we've vetted all stakeholders and standing up a digital twin data schema. And then there's enforcement, enforcement and validation. So validating that the data stays compliant. And if it doesn't, you enforce it in some way to get it back in compliance.

You've got multiple departments contributing to data, to the digital twin. And the integrity of that data is critical to maintain the credibility and reliability of that digital twin, that output, and the desire to continue to go back and back to it to gain information.

So it's not just one system. It's not just BIM. It's not just GIS. It's not just asset management. As I said earlier, we just don't know where this thing's going yet. There are many different things that I don't think we-- we don't know quite how we're going to leverage yet. But the industry will continue to, I think, gravitate towards these rich data sets and want to leverage them and to analyze them and to generate simulations, potentially even begin to function autonomously. That's something we'll talk about in a little bit.

Interoperability between these systems. Large organizations often operate a wide range of systems from an enterprise resource planning system to building management, IoT, and asset management systems. And the well-designed schema ensures that this digital twin will integrate and communicate with all these various systems regardless of where they sit. And obviously we've got different authoring applications or just really manufacturers of software, whether it's Autodesk or Esri or IBM.

You really have to understand how to promote that interoperability to enable data exchanges between the twin and the organizations existing in that infrastructure and be able to make sure that you can view that data, regardless of where you're coming from, whether it's from the BIM side or the GIS side.

So three key pieces there, I think. At least at this stage, there are many others-- would be BIM, GIS, and asset management systems. There are many others. A lot of the other systems that I'm thinking about are lease management, space management, things like that. They usually leverage some of these other systems, those kind of primary systems.

But they all need to be able to connect. So another key piece is that efficient data storage and retrieval. So in a digital twin, the volume of the data generated by a mature digital twin with a lot of IoT sensors and equipment and other systems can be pretty significant.

So a thoughtful design in data schema ensures that the data storage is optimized, kind of trying to prevent bottlenecks, efficient indexing, partitioning and schema designs, allow for faster data retrieval, which is crucial for real time monitoring, analytics, and decision making.

We're not going into that at great detail. That's not really the focus here. This is primarily focused on the rooms, the physical rooms and how they're identified in a digital twin.

But that's important. Real time data processing and analytics, definitely moving towards that autonomous kind of digital twin, really lies in the ability to provide real time insights and predictive analytics, to be able to process live streams of data as it comes in, to be able to monitor systems in real time and to make data driven decisions as these systems beginning to mature. And with a good data schema, it allows that system, AI, to begin to run simulations and then to take even a step further and begin to potentially run autonomously.

I think the end all be all is potentially Terminator-like, I think, where the systems could be autonomous because they run simulation. They understand. They get live streams of data. They can run simulations. They've gone through the machine learning process, and, therefore, then we can have them make decisions. So there's a lot of value in there, but we still have a long way to go.

So what does that mean for a data schema, primarily as it relates to rooms? But there are many factors to be considered. But we're just going to focus on the rooms. So the data schema for rooms needs to be a strong foundation, ensuring that it can meet the demands of a large organization with diverse needs.

It needs to promote scalability, data accuracy, efficient processing of data, data coming in and out, security, and making sure that data is secure and the integration of all these kind of legacy systems. We can't just say we're going to go get all the newest and shiny things. We need to integrate with the existing systems. So kind of investing in this data architecture from the beginning, it allows organizations to begin to potentially unlock that potential of digital twin to make better decisions.

So typically, we typically employ a kind of hierarchy system, hierarchical structure of data, which is pretty self-explanatory. But I'll just go through it really quick. Using a large campus with multiple buildings, it's broken down into fairly logical systems of maybe zones, North, South, East, West, zone A, B, C, 1, 2, 3, 4, whatever zones or clusters and then buildings and then levels and then rooms and how those are done in there.

And then many other types of attributes that can go in there. It's all data. We want to just associate, once we have that location, that kind of a location ID, which we'll talk about here in just a second, you can begin to attach other attributes.

So just to reiterate this so you can see it. The data schema needs to not just be from one perspective. It can't just be from the BIM perspective or from the GIS perspective. Multiple stakeholders need to be involved in order to break down those silos and begin to potentially really generate some value of the data schema.

So just kind of jumping into it, as it primarily relates to rooms, we look at a higher level here. This is a data schema that, like I said, breaks it down as like a facility, or it could be a zone or a campus, but then a facility. There could be other types of systems or positions or, like I said, levels or things like that, that begin to break down the system.

So you get down to what's on there is number 8, that asset location. That's really a concatenation of a series of data points that combine facility system, position, location ID, all these various pieces that you, as an organization, kind of collectively come up with. But that asset location is a concatenation of all those data points that almost instantly allow you to understand exactly where that space is.

And with room type and potentially some other attributes that you may include in that concatenation, you can gain some information. So room type is typically like restroom, office, corridor, mechanical space, so on and so forth. Those are things that are pretty high level, almost rudimentary, at the, we'll say, at the BIM level.

But a lot of times these things may already exist in GIS. And they need to be integrated into BIM or vice versa. They may be sitting in some other system, might be in Excel or something like that. This goes back to those silos and making sure you've got a broad range of stakeholder buy in to begin to develop a system like this.

Because more often than not, there's a system in place. It may not be perfect, but there may be a pretty strong need to honor that system in some way to make sure that you don't lose that data. Or someone may just feel like that's what they need to do.

So the next level down beyond that kind of asset location is design authored. And so these might be kind of specific things that are maybe somewhat associated directly to BIM. But these are things that, usually if we're designing a facility, that these are attributes or parameters that the architecture engineers would include in the model. And we typically make sure that these are always, they're validated. And if they're not included, we enforce compliance.

Because we really want, again, to stand up this system. And when it goes downstream into O&M, we want to make sure all that data is there. Field capture is kind of on the construction side. So designer or engineer thinks, I'm specking out this-- he or she is specking out this pump or an air handling unit. But the actual model manufacturer, serial number, warranty, install date, so on and so forth, that's not known until it's actually in the field and installed by the contractor.

And so at that point, we want that data. We want to make sure that data is associated. So we all these parameters or attributes are associated as the data continues to mature, continues to grow. And then it's shared across those systems, GIS or BIM, other asset management systems, usually through a unique identifier.

Then we go into another, getting deeper into the hierarchy system, we get into some things that may be the owner may include in there. The owner may capture data points and things like that and associate that to the assets. All these attributes all get combined together and all get associated to assets.

We've worked for a lot of airports all over the world. We've had some airports that have said, I want 200 attributes or hundreds of parameters associated to assets. But for the most part, this list right here, 25 to maybe 50 on the very high end, is really the best sweet spot that we have found for attribute data for assets. Anything more, you're probably potentially getting into the analysis paralysis kind of realm to where you probably won't use all that data. That said, digital twin world, more data we can associate, the more data, we can pull and extrapolate, maybe the more we can do with it.

The last piece here we're going to just talk about Tandem and how standing up that data schema and the importance of rooms, how we can-- and because we established a location and a hierarchy for all these rooms and identification of these rooms-- we then can see how this can benefit us and understanding from a more of a direct digital twin perspective of what are some of the things that we can do because we've set up this structure. So definitely integrating live data into a digital twin has the ability to transform a representation into a dynamic, informative, system, providing real time insights for a building management system.

We can gain clear understanding of how different levels or rooms or spaces that are adjacent or are interacting and how they're performing. This live integration allows us to create intuitive visuals. Like what's on the screen here is a heat map highlighting areas that require attention and facilitate data-driven decisions to enhance that building efficiency. So it allows that data to come to life by using things like IoT.

So an example of this scenario would be you could visualize and trace how if you turned off a piece of equipment, let's say an air handling unit, how it might impact not only the space and the conditions of that space, but other systems. Other systems are potentially going to have to work harder. And having that kind of live feedback data points within the system allows you to understand that. So those this level of insight allows for proactive decision making, enabling teams to quickly assess potential ripple effects and optimize equipment performance across the entire facility.

The live data being stored and visualized instead of performing just kind of a trend analysis, you can begin to explore, visually explore, these heat maps by replaying what happened earlier today or yesterday or last weekend. So we can understand a variety of data points and how they all come together and it created a certain condition, and begin to learn from that and be reactive in a positive way. So looking back at historical data gives teams, facility managers, owners, operators powerful tools to understand past events and to be able to make informed decisions to improve future performance.

We could also begin to segment or cluster parts of the facility into zones. So take an airport, for an example. By strategically scheduling preventive maintenance in a specific zone or a cluster of rooms, we can optimize operations. Instead of dispatching the maintenance team across the entire facility without a visual or a graphic map, we can be more mindful by planning efficient routes, making sure we've got the right equipment rented and with us for each area, and really just ultimately saving time and resources being to plan those preventive maintenance.

A good example of this is an administrative building at an airport, San Diego airport. By integrating with a standard maintenance database, we can begin to visually display the status of preventive maintenance tasks, whether they are complete, whether they're deferred, or not yet even started. In addition, we can include the expected equipment value, which is kind of shown in that white box down there in the lower right.

And by leveraging this value or by extracting this value and bringing kind of a visual aspect of it, we can total those assets within, let's say, a room, or a series of rooms or spaces, providing facility teams with valuable insights on where to prioritize their efforts, especially focusing on higher value assets and their specific locations.

So these are individual assets, color coded based on value and the preventive maintenance. So we're looking at multiple data points and kind of bringing to the surface the important information that we want, color coding them and doing various things within Tandem.

But then we can, again, I kind go back all the way back to the nomadic piece and that we exist in rooms, that we exist in locations for the most part. And these assets exist in locations. And if an asset needs to be worked on, chances are someone needs to walk to a room, get to a room, open a door, get in there, and access that room. So it really kind of focuses the importance of rooms, that those assets are a child to the room that is a parent, so that parent child relationship.

So by applying what was in the previous screen of preventive maintenance that needs to be done and value of assets, and so if that preventive maintenance is not done and there's a value of that asset is really high, that's kind of a concerning area. And we can apply that to the room.

So I think in this visual here, it's hard to tell where those assets are, on what level. And we can, then, take that data that we got from those assets and apply them to the rooms and say that if it's red, so here, the red areas represent high value assets that have not undergone preventive maintenance. This is not a standard, out-of-the box functionality with Tandem.

But with some integration capabilities and an innovative approach, these are things that we can use Tandem for-- really impactful, relative for facility operations. From these types of things we can begin to look at, I think-- Yeah, so red area, need to get in there. Need to perform preventive maintenance.

But what happens if it's occupied? When is it occupied? So that's another layer of data that we want to integrate, so being able to analyze the data between systems, so a room booking system or an occupancy sensors. There was no connection between preventive maintenance and occupancy of a room. So Robin is a booking system that we can feed into Tandem, so provide Tandem information that what Robin is providing about room capacity or photos or real time booking status.

By correlating this with occupancy data from systems like VergeSense, we're able to uncover valuable insights, are people using the rooms without scheduling them? Are they booking appropriate sized rooms? Is one person using a really large room? Why are certain rooms being used more frequently than others? This cross system analysis and connecting data opens up a lot of new opportunities for optimizing space and utilizing efficiency.

That is all. Thank you for listening to the presentation.