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Plan for Space Programming

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说明

Space programming isn't a one-size-fits-all approach for DLR Group. Each project brings a different set of challenges with a different set of solutions. This presentation will discuss the different approaches that DLR Group uses for space programming in Revit software from campus planning to small-scale, tenant-fit projects. We'll provide actual project examples and talk about the challenges of the projects and the approaches that we used. You’ll see examples of custom software, paid subscriptions, and freely available resources as we talk about building, room, and FFE data.

主要学习内容

  • Learn methods of using Revit and supporting tools for multibuilding space programming projects.
  • Learn methods of using Revit and supporting tools for single-building space programming projects.
  • Learn methods of using Revit and supporting tools for renovation of space programming projects.
  • Learn strategies for managing data at varied scales of projects.

讲师

  • William Carney
    William Carney is the BIM director at BSA LifeStructures where he is responsible for overseeing the firm’s use and implementation of design technologies. He obtained his Bachelor of Science degree in architectural studies from Southern Illinois University Carbondale and his master’s degree in architecture from the NewSchool of Architecture and Design in San Diego. He is also an Author for Lynda.com and is actively involved with the St. Louis Revit User Group as one of its steering committee member.
  • Ju Hui Chia
    Ju-Hui Chia is a Design Technology Manage at DLR Group. Focus on Revit Development. She is passionate about design technology, and programming that enables a more efficient process.
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Transcript

BILL CARNEY: Hello, and welcome to Plan for Space Programming. I'm Bill Carney, design technology leader for DLR Group, where I get to lead a team of BIM, reality capture, computational design, and GIS specialists to help our design teams best utilize technology to design buildings. We're a group of technology enthusiasts that love what we do and love enabling good design through the use of tools.

I sit in our Minneapolis office, where I teach virtually for Washington University in my former home of St. Louis. I'm also a content author for LinkedIn Learning. And finally, I'm one of our three R&D studio leaders, where I help focus the lens of DLR group's use of funding for research and development for new and innovative technology that we can leverage for design or that we need to be aware of for its impact on design.

I love what I do. I do what I love. My co-presenter today is one of our rock stars from our DLR Group's design technology team, Ju-Hui Chia, who can introduce herself.

JU-HUI CHIA: Hello. I'm Ju-Hui Chia, design technology manager at DLR Group, where I mainly focus on automation programming and developing our own custom-ready roads. I sit in our Chicago office, and thanks, Bill, for having me here as a speaker today. And I'm really excited to share what we did for the company.

BILL CARNEY: DLR Group is a global integrated design firm specializing in architecture, engineering, interior design, and planning as our core disciplines. We have offices in most major US cities, as well as Shanghai and Dubai.

JU-HUI CHIA: We started in 1966 with Irv Dana, Bill Larson, and Jim Roubal and two architects and engineering in Omaha. Grew a firm out of their basement into the employee-owned design firm we are today.

BILL CARNEY: We practice in 10 primary sectors. The diversity in sectors and region are really why we're here today. Ju-Hui and I, we've been working across disciplines and sectors to develop new tools and processes, and this collection of sectors creates diversity and the types of buildings that we designed. So we could work the exact same way on every single project, but we do take a right-size approach to tools, and not every project type benefits from the same approach.

If you've ever used the Revit schedule key to fill out finish information, you've run into a scenario where it works great on a project with similar room types, but when you find a room type that requires slightly different similar information, you might find yourself doing stupid workarounds that continue using the schedule key.

I almost wanted to try and coin something like the three dimensions of programming and have a cube with all the options, but I thought it was a little cheesy. It does require a bit of a matrix to determine what tools and how to use them. So part of doing this presentation was for us to consolidate the many disparate tools and processes that we have across the firm into something we could guide a team on what to use from something like a project startup form or BIM execution plan.

So we could ask questions like, is it a renovation? Does this project have multiple levels or buildings? By adding just a few questions about the space program, we can suggest a few options for tools that will support the project best. So we do have a few more questions that we do ask in the third dimension.

So when we ask what additional information our teams have to start the project, we end up with a cube, and the 3-D's of programming! Sorry, I really can't help myself in using things when I present. It's all about my own entertainment. But in all seriousness, when we pair the building type, the working style, and find out what the team has for a starting point, so what is the site like?

Does it have a lot of topography? Do they have CAD? Do they have Revit? Do they have client standards, regulated standards for building program? All of these things help us pick out what we need to do. So Ju-Hui and I will talk about some of the projects and approaches that we've used around these terms and options.

So with that, let's set some expectations for the presentation. I find it's best to give an overview of where we'll go so you can know what to expect. So here's a road map to paint a very clear expectation. We're currently in the introduction phase of our presentation. So right here, right on time. Great work, Ju-Hui. Really, we'll talk about projects and details.

So as we move into the 3D's of programming, we're going to look at projects. We're going to talk about a lot of tools. It's not a linear start to finish, how we did the whole project, it's the random collection of tools that we've used. Then we'll give some future thoughts as far as where we see ourselves going at DLR Group, and we'll try to put a bow on it and wrap it up, and then maybe some Q&A.

Ju-Hui, do you want to take us home with the learning objectives?

JU-HUI CHIA: Of course. So for today's learning objectives, we would like to learn methods of using Revit and supporting tools for multi-building space programming projects. We also want to learn methods of using Revit and supporting tools for single-building space programming projects as well as the renovation of space programming projects.

We will highlight in specific projects. Data strategies will be covered throughout each project example. We will try to highlight how we manage space-related information for the project. It's not all strategies, just some strategies.

BILL CARNEY: Well said.

JU-HUI CHIA: And now, the introduction are out of the way, let's get into the meat of presentation, our 3D space programming project examples.

BILL CARNEY: So we kind of just lumped workplace as a bunch of our building type scenarios. And this is where we have a whole building program. We have tenant bit projects. We have renovations. We have a single floor layout. These projects always seem to be fast, so once you start, you're stuck with the method that you have, and there isn't a lot of room for error.

What would fit in the space is the game that we play on these projects. If you like Tetris, you might like a tenant fit project. Many of our workplace clients send us these quick, small exercises to test what will fit in the space. You're basically making the space program for the project, and it's not overly designing anything but seeing what can reasonably fit. So that we can knock out some of the learning objectives, this isn't a whole building project. You're working within a boundary. The size of the boundary should guide your approach.

So common, for me, has always been a lot of health care projects, and this project, you'd have some of this to it, where it's a renovation in a small suite area. So in a size of an area like this, you usually only have one person working in it. So Revit design options do work, and they work well at this scale of a project. And this is about the size limit that I recommend for using design options.

When the scope is bigger, like the whole floor, I recommend saving the whole floor as a separate file. So if the file is already one giant model with multiple levels, what you can do is select all the things on a floor, make that a model group, and then turn that group into a link. And then if there is additional design options, you can copy that file and rename it.

The benefit of this is that often, on a floor plan of this size, you may have multiple people working on it. And even if it's just one additional person that's doing annotations and detailing, the ability to turn that into a central model allows for this. So this is more often a strategy for health care or higher education, but basically, anything with a campus will have a base plan file. And you have the existing buildings, you pull that file out, you do your project, and then you simply just bind it back into the project. And with all your new drawings combined, you have what the new existing building is, and then you wait for the next call and the next tenant bit project.

Specifically for this project, I know this isn't sexy, but it's a screenshot of an Excel calculator that had the rules for the spaces of the client. So we use this with some square footage estimates and number of people to create space program scenarios of what would fit inside of this building. When we were initially engaged in this project, we were given this list, and we did our typical thing and make a whole bunch of boxes. The team was immediately overwhelmed. They had to fit these boxes into small areas, and it was just too much.

So because these are quick burn projects, we did work with the team to really listen and hear what they were trying to do. And what we found is that for them, often, it was just having the pre-loaded room types is what they were needing because they had to let the room drive the calculation for the additional spaces. So they're able to just pick day office and array it along this line, and that should drive the number of additional huddle rooms and spaces that they need from this calculator.

So the designer can just copy their room along and then go ahead and change the type to a different type because they know what they need inside of the space, and they're just making it quickly fit. It works well enough. A tip from us is, if you have a whole bunch of boxes and you do have to lay them out, use an Align tool of some sort. There's a lot out there, and what I'm doing here is taking one of the boxes and aligning it into a corner, and that box represents an office. And then what I can do is use the Revit add-in to just select all the boxes and align them from a click of a button.

So here, I can click on the Add Ins tab, click Align Top. They all move. I didn't have to individually click each box to move it, and then I got them to condense with another click of a button.

So something that we did make with our test fit projects, so much of this is about just finding seat counts that can fit inside of an area. So we built a tool that allowed us to select a region, and that could be a room-- it could be a filled region, it could be a floor-- and then pick the furniture item, and then you can pick the pattern that you want the furniture to lay out in. So what they did is click the furniture, the desk and chair, and it laid it out in rows. But then we can pick a different furniture pattern and adjust that very quickly.

So this one, it's been pretty perfect for this style of project. We just serrated around the outside edges of the room. These videos show this a little more in detail. On the left is different patterns and groupings, and you can see we're selecting circles and chairs and different groups of furniture, so you can just work in layout like a little pod and lay that out in a space based on patterns. And then the video on the right, what we're doing is some manual override.

So we set it that you can set a detail line style that represents something like circulation. It could be primary circulation or secondary circulation. And you can give that a dimensional offset. And as you draw that over these spaces, it automatically adjusts the furniture inside of it. So you can really quickly layout FF&E items with some manual oversight.

Once you've done all of your layouts, the other part of this is they need to quickly create counts, and we found our teams were manually counting items inside of spaces. They were highlighting plans, so we did build a tool that you can just select the whole area, and it will give you counts as far as how many of these furniture items are by room or just the furniture family itself that you can populate into the calculator and drive your required box list.

So these projects often come with options, and when you have options, you need to compare them. Our workplace team created a benchmarking method called workplace elevated. So it's common metrics for workplace to categorize room types and look at them in the same way. Initially, this was something that required our designers to draw an area plan of every single one of these super quick burn test fit projects. It's really tedious, and then when they make updates they're chasing their changes with this and changing that all the time.

What you're seeing on the screen, we had built a tool that what it does is reads the room name and the bounding conditions and the size and guesses what that elevated category is. And then the user can go through a properties palette and click on each room and check to see that it's the correct type. And you may notice that some of the area boundary lines are on the inside of the wall, center of the wall, or the outside of the wall, and that's it's a modified BOMA calc based on the adjacent room type.

So this really sped up the process for them, and then they could take it into additional tools like Power BI and calculate out other metrics like staff per collaboration areas, the number of workstations, total staff. It's a way for them to compare one design to another with metrics and actually, make some data-driven decisions.

All in all, workplace stuff has been very successful for us. We're able to help teams speed up what they were doing. The tools we've been building continue on into other tools. The workplace elevated is actually morphing into the way that we're doing life safety. Nothing is perfect, but it's helping the teams that it can continue to improve.

The one area that I would love to improve is something like that calculator having a way that it's not so client specific that we could write to really easily. And then a very weird thing for us is that these small-scale projects tend to show a lot of details, so they push the limits for what we can do graphically between boxes and rooms with furniture inside of them.

So with that, Ju-Hui, do you want to take the next project?

JU-HUI CHIA: Sure. Thanks, Bill. So the next project I would like to share is a J+C civic project, roughly 110,000 square feet, two stories. In this project, we used the imaginary clarity space programming tool to help the design team. With the typical deliverables folder criteria document process, we would have normally done within five months. But for this project, we were able to do in a matter of weeks, as we were asked by our client to have our preliminary criteria document to help them with their procurement process.

We had our basic departments laid out, and then we were able to use this process to populate the two floors of the building with even more than we expected, followed deliverable furniture and equipment items, because we were able to lay out the spaces so quickly and have them be on the square footage targets.

We started from taking the program list from an Excel sheet, which has the type of the room department component number, area, et cetera, and then we use imaginary space programming tool to import the programs, then populate all the spaces in Revit. We will have a short video to show the whole process in a few minutes.

But the idea is to quickly populate all the programs with the square footage targets, and the design team can easily lay out their space, depends on their relationships, then convert them to actual walls and rooms in Revit.

So the team also used KRT for room data sheets, which can grab all the data from Revit, including the amounts of the FAV items. Since all the data was tied into the rooms in Revit, they did not have to constantly go back to the room data sheets. They could just look at the room's properties and know that, hey, this space is high security level, and this one is a little bit lower, so this made their process much more streamlined. The challenge here was that the team was hoping that they could edit the data sheets graphically, but since these were in a beta version, they were OK with them.

So this is the interface of the KRT space programming tool. As you can see here, these are older programs we imported from the Excel sheet, which have all the data for the room types. And then in here, we can pick what furniture or equipment we will have for this room type and set up some color coding for the department.

Then we can go into Revit to pair the KRT field to Revit parameters and payload levels and set up the family folder location, where we stored all the families. Then we generate all the spaces by clicking the spaces from generic model. We can either Create all at once or only select a portion of them.

Then after that, we got all our space boxes in Revit, and they can be shown as bubble diagrams or solid lines with the department's colors, and they have text on them. So the design team don't need to reference their program list back and forth. Because they have all the target square footage on the end of program data in the model.

So they can quickly put them into the desired location. Then we have ability to convert them to actual walls or rooms by keeping all the data. Then we can even check if they have the proper FAVE items placed in the room. If not, we will know, and we can place them by using the FAVE button. And then we could either praise them or at once, which will place all the missing families in the center of the room, then we can check them to the correct location, or we could praise them individually.

BILL CARNEY: One thing we have been trying similar to the workplace metric is the tool called Space Geometric. So you can analyze the spaces very quickly in a spatial syntax sort of way. It does some things great, but we did find our limitations with this as a justice and civic project, where a space may be limited privacy and visible in areas by partial walls or doors that are half doors.

So those are things that we have challenges if we're trying to analyze visibility in two dimensions. But otherwise, it's a really good tool for us of trying to introduce more evidence-based design calculations. We think it's a slam dunk for our health care teams, and we've been finding it helpful in other locations.

JU-HUI CHIA: So we were using KRT space programming in beta, so we didn't have our glitches, but we had enough bigger plans for how to do this process from other tools that we were able to make what we needed on the fly when things didn't work, while driving software development. We had a big project with repeatable information and fairly static data to the point where the team just needed something to help them manage requirements.

So this was perfect for that, being beta, every hurdle we met, we seem to have a solution for with the exception of live editing the graphic version of the room data sheets. We have been exploring spatial metrics for our justice and civic projects and think there will be a fit, but run into a lot of challenges. It's worth mentioning that our justice and civic team has aspirations of working out of a common database for their projects. We will touch on it more, but we list that as room for improvement. Our data was still heavily developed in Excel and used KRT to connect to Revit and audit ourselves, but can improve.

So next Bill is going to show a core project in Alaska.

BILL CARNEY: I don't know if cool is the word. I always say this is one of the weird ones. So this was one of our planning projects, but normally planning, you're looking at very large scale, but they were getting down to the granular level of the programmatic rooms of each one of these buildings. So University of Alaska's master plan, we're looking at several buildings on several campuses across Alaska. Lots of topography.

So the team, they wanted everything. They wanted to generate building masses, stacking diagrams, sight maps and floor plans that could all dynamically reflect information to make graphics for the master plan report. What we had to start with is CAD plans of each building. So we did use a Dynamo script that placed room bounding lines on all the CAD lines and then placed a room at the text of the room tag that it could spot inside of each room.

And then that room, we picked up what the text was and assigned the room name and room number to that room. And then we could use that to make the color fill plan. On this project, we also use Dynamo to read the CAD site map and take the boundaries of the buildings, and we use that to make our masses that the team used to convey their master plan. But what we'll show here is where we've evolved to with that script from all the complexities that we've learned. Ju-Hui, do you want to explain this one? Because you can do it way better than I can.

JU-HUI CHIA: Sure. So we build up these tools in a way that reads the building outlines in CAD, group and clean up duplicated or redundant lines and sort them to make sure all the buildings are enclosed and are closed loop. So then we can take the curved loop to create extrusion for each methods. And the exclusions will allow the person to set a parameter for building height and extrude the mass to represent the building height.

Then you can split the mess into floors and plan case message to assign materials and split up to represent how the building is being used or in the case of planning should be used. So Bill, you want to tell us what other cool stuff we did for the project?

BILL CARNEY: Absolutely. Although I was just watching because I'm so amazed by this every time it runs. With the topography, we had to take into account views across this site. So there were things that we need to take into consideration that a flat site in Florida is not as easy to analyze. So we did need to actually deal with the topography and look at it in ways, so that pushed us into InfraWorks to be able to select the site, look at what features are there. A lot of it was coastal, but some of the sites were just flat out on the side of a mountain.

So we exported the InfraWorks model to FBX and we're DLR Group. We're a global design firm. We care about our graphics, so we took it into Twinmotion. And so here you can see the InfraWorks model exported in the Twin motion and then the Revit model brought in there.

What I love about this is we build these masses on a floor plan or on a building level inside of Revit, and it doesn't exactly match the topography when you're going building the building like this. And so what Twinmotion does is lets me select that individual component of the model and move it, but it keeps its relation. And I can also reassign materials, so I can graphically convey some additional information inside of this and really polish up a presentation.

And what you're seeing in the background, that actually picked up the map of the FBX, and it's pretty good. But there, I could apply some better-looking water that can move and give some additional information or put in things like boats and trees. You can even override the map, and there you're seeing buildings. It actually swaps out the InfraWorks buildings with better-looking bad buildings. So I think it's fantastic, and it's a good way for us to tell the story, combining all of these different data types.

Speaking of combining data types, a benefit of converting the CAD rooms is that we can export TopoJSON files that can be read in Power BI. So at a campus level, it's pretty easy to see these things in context. So what you're seeing with these bubbles, this is one of my favorite things. It's called a network diagram.

And so it will show interconnections between hierarchical information. So something like building department and room, I can see how these spaces relate to each other, and I can click and compare building A to Building B. I can see it graphically in the floor plan, and I can see how the spaces are similar or where my overlaps are between buildings. So it's a really good way for me to make informed decision about the building that I may not be able to see in just looking at it in a spreadsheet.

So this project was great in that we were working with our team to build out a workflow, and we really put it all together finally. We'd run into enough complexity with the need to analyze space at a portfolio, campus, building, and individual room level, that we were able to iron out all of our wrinkles and dynamo and eventually build a button that Ju-Hui showed, which was amazing.

We automated image exports. It's something that I throw out at every project ever as just a possibility of something we could do. And it always falls under that seeing is believing and having not seen it, the teams are never interested in, and we finally had a project that set that up. A big challenge with that, though, is that we had automated images exporting, and the idea is that you annotate your image inside of Revit and clarity export the image, and then it updates inside of your InDesign file.

The problem is the team did their annotations in InDesign, so they still were updating their information in two places. We didn't really reap those benefits. Topography in Revit was a challenge. There not much needs to be said other than I am excited for the future. Our planners, they tend to pull down data and work out scenarios in Excel because it's quick and easy.

This is a challenge that I just throw out for everybody to be aware. It's hard to keep track of when you do this, and make sure that, which is the correct current version. Or if they don't pull down all of the information like the building key, that may be like the building code name that you're tying it back to Power BI with, these things aren't impossible to overcome, but they're challenging.

All in all, this was one of my favorite projects from a tool exploration perspective, but now, Ju-Hui is going to tell us about my favorite project.

JU-HUI CHIA: Awesome So the next project is an educational for CRT master plan project, which is in Illinois' second largest district, U46. They have at least 55 plus buildings, which means 55 plus models, absolutely. And the materials, all we have are a bunch of the components for the projects. Before, the design team had to open individual CAD files, export them to Adobe Illustrator to color code them, and identify undersized rooms and capacities.

Additionally, they manually calculate the capacity and label the rooms by use. This would take approximately 7 hours per building in the manual process. With the automation tools, it took about 1 and 5 hours to get you to a place where they can use the prints. Then these models can be used for all the five phases of the master plan efforts.

We all have a couple of videos showing a series of scripts we made and run for this project. With these amounts of the models, KRT was a huge help and need because we let the test machine run or a bunch of the scripts and all the 55 models.

Something to note about this project, K-12 is public, so we provide a lot of information that needs to be digested by the public. We had CAD files and needed to create diagrams, that list of each floor of each building. So the first video is showing us how to copy and then all the models. We had an Excel file that list all the score names, and we created a starter file, let Dynamo copy and rename all the files based on this name list. Then we used KRT to help batch uploading all the models and to bring this to you.

And the second video is showing us how we set up the Dynamo script to link all the CAD files from Autodesk Desktop Connector onto their corresponding levels and buildings based on the CAD file name and the models name. Once we have the script set up correctly, we use KRT machines to run the rest of the models.

The last one will be the one we generate rooms based on the CAD background. Seems the design team needed to quickly calculate all the problem areas and use these to do their research. So we built up another Dynamo script that we grab the data from, some specific CAD layers, walls, doors, windows and room text to grid the room separation lines and rooms. Then it sets their room names and numbers based on the room text in the CAD background.

BILL CARNEY: So because a school is woven into the fabric of a community, our planning teams offer a huge level of technology and data analysis support through GAF for our teams to really look at the context of that building as part of the neighborhoods and communities that they actually reside in. So in these master-planning projects, they compare this information with our space program level information to make informed decisions beyond just the cost of repair for an individual building, but which site is going to have the biggest impact made throughout the entire school district.

Power BI let their teams connect the BIM room information, this GIS information, and additional district requirements, and so much more. What's nice about the program is that our designers can learn this quickly. This actually was the first dashboard made by somebody learning on this project. So they never made this, and they put this together. It was great. This one, made from one of our more advanced Power BI users, they were looking at broader data. You can see there's acoustic, lighting, air quality. But you can go in and you can pick the different building types. You can see the information that you need to across these buildings and really help to connect the dots in a meaningful way.

So although this project later in our presentation, it was actually one of our earlier successes as a team. Ju-Hui is too modest to take all the praise on this, but she was the rock star. This was one of the early successes of the DLR Group design technology teams for automation. So I have this phrase of build the myth where you want to have some events that are known for what your team's superpowers are.

But really, myths are fake, and legends are built from something real, and this is one of those projects that Ju-Hui really became a legend of automation because the amount of time she was able to save for these teams. When there's 55 plus building sites, that team was going to manually set up Revit models for and trace to make color-fill plans. That's a lot of time that you can take a risk trying to automate something that you haven't done before.

So even at one hour per building to set up a model and trace one to three floors, that's a real low guess for time saving. But that would still give you more than a week's worth of time to write script, where you would be better off spending all your time figuring out if you can do this without being better off doing it manually.

So another benefit of this project is that it also brought a larger group of the firm into using Power BI just from seeing the examples of what we could do and the learning on the project. The only real area for improvement by the end of this project was that Dynamo was just fast enough that you could manually run some things on it, but it was low enough that it would require a lot of extra steps.

We had to run several different scripts within it, and you could start to see the difference in performance between Dynamo and C#, so we've since programmed in our Revit add ins to handle that portion of work, but it's still fantastic. I can't say enough great about this project.

So another one, the FIU project, this was an interesting one in that we got an existing site with detailed Revit models on it. And the team wanted us to create their typical massing block deliverables, so we needed to dumb down data, and we use Dynamo to basically shrink wrap the buildings to make masses.

So we had three different sites, and they looked like this, and we wanted to turn them into this. So how we did that to dumb down BIM, we took Dynamo and we would select the linked model, and we would find the bounding box around the geometry of that model. And then once we had the bounding box, we could take the bottom face of it and draw room separation lines from that.

And then once we had the room separation line, we could place a room inside of the box outside of the link and so we put it in a corner. And because the link was room bounding, all of a sudden, the room is going around the Revit model. So we shrunk wrapped that Revit model.

Then with Dynamo, we could read the geometry of the room, and we could take the geometry of the bounding box cube and select the inverse. And so from that inverse, we could actually just find what the shrink wrap masses are and convert that into masses. Then we use CPC's spreadsheet link to let the team set data about the masses, like number of levels. We added the floors, the spaces inside of it, the building number, all the information that they needed to connect to. So they could start analyzing the information. We could run scripts to copy the levels out of the links and put it into the mass and then set the building levels at those same masses so they could start to slice and dice the buildings by department percentages and things like that.

One of the hardest things was the overhang. So spotting something like this, there's lots of reasons this is hard. The biggest one is that these sorts of features are inconsistently modeled. Sometimes they're room bounding, sometimes they're not, but where Dynamo was fantastic is that you could visually see the geometry of what the computer has seen.

And so we could look at it, and whenever we saw a weird shape mass that came out, we could look at the geometry in Dynamo and fix our script so that we could actually put the right shape inside of there. I had mentioned that we made levels inside of these masses, and we copied them out of the linked models so they were named the way they were named. And Power BI was really fantastic for us overriding level names and associating the first floor across all of these models so that we could look at these spaces in an even way.

This project was similar to the Alaska project, but it was a little different in that we got to connect more information because we had more. We didn't do a site map because they were pretty large scale, and it didn't work out as well. But we were able to start to look at business unit, department, when the buildings were built, and we made a very big hierarchy that we could drill down into and see all these spaces across three different campuses graphically and just numerically and answer questions as we're trying to do a master plan.

So we did also do the network diagram on this, and you can see it's pretty legible right here. And what was cool is you could filter through and look at connections of things. And we connect it by campus, building, business unit, department, and room use. So you can really see how these spaces are interrelated to each other, and you could compare individual business units and see the trail. But when you do this, it's horrible to look at, and the team runs away. So you have to be strategic in what you show them.

This was a Power BI dashboard not used for this project, but it is another application of power BI that we've been using of analyzing time series data. And so this was culture and performing arts, and they were looking at theater spaces. And what they wanted to do is tag spaces, and you can see it in these colors by basically, noisy and not noisy. And then they're looking at events inside of the same theater to make sure that we don't have a loud event happening in the same area next to a not loud event, so that they're impacting each other or vise versa.

So what you're seeing in this awesomely dynamic graphic is called the Time Storyteller, and it's a custom visual that you can download. And what I love about it is you can pick things like today and adjust whether you're looking at this linearly, radially, individually by one theater or three theaters, and you can dynamically look at this for patterns that your eyes can spot. I've completely fallen in love with this because on the fly, I can look at really complex data that I can't understand in Excel.

Each Revit model had its own level. I think the most common question we answered on the project was, how do I schedule everything on the first floor across the building? Power BI made this really easy for us to take our schedules, and even though first floor wasn't named consistently, we could override that in Power BI and start to show the information as the same. So we did have a lot of trouble making those shrink-wrapped buildings, especially in buildings with courtyards or that were modeled weirdly.

We found that some of the links had one building in it, and some had many. And being able to see that allowed us to do it. And from a personal point of view, it always saddens me to dumb down BIM, but there is a point. We're getting a graphic output, but really, our designers are getting to work consistently in the way that they're trained to work. So they just jump in and know what to do.

Not every designer is a power Revit user, so I try to take my BIM utopian hat off and make sure that we're doing something that helps the designers. A huge benefit in shrink wrapping the link is that it made a campus of pretty heavy buildings go from a terribly slow Revit model to a functioning usable file that the team could graphically convey what was really a math problem in Excel.

So some future thoughts of what lies ahead for DLR Group. This is an example of a seeding tool that we used for our culture and performing arts teams. It was born out of a Grasshopper script that one of our designers built, and we had some requests to stabilize it a little more. The idea is, if you have a cube and you need to fit a theater inside of it and the size of the theater, the focal points, that sort of thing, how many seats can fit inside of the space, length and width, as well as height.

So the tool will generate the balconies. It allows the design team, though, to have some manual oversight. So they can adjust and move the balconies, they can copy them, but it's always making sure that the seats have the correct view to the stage and are laid out well. You can see the chairs have different widths, and so I learned a lot. All the colored chairs that you're seeing there are different widths, and they use different sized chairs to evenly spread the aisles as you have different number of seats that can fit as the theater tapers down to the stage.

We also allowed for manual override of something like those aisles, where you could just graphically change them, and then what it's doing is giving you the number of seats and outputting them into this calculator that is going to drive the requirement of back of house spaces. So that's one of the things that we're trying to do is, how do you do the free form high design and then push that out to calculate the back of house spaces that are programmatically required.

The other big R&D project is machine learning for space programming. So when I started DLR Group, there were several requests for R&D of an urban design tool, building level boxes tool, room level furniture and equipment tool, a data warehouse, pre and post occupancy evaluation, and data science to figure it out. So I put this way overly complicated diagram together to explain how the urban tool should feed requirements of what should be at a site into a building, and then those requirements should know what things go inside of the room. And you should feed this into a data warehouse and use machine learning to start to better understand your buildings and inject that from the planning stages in audit and on the design stage as a suggestion.

So then the next year, we had gone through and we framed up what these tools could be and started building. And so I had to put together another terrible diagram. I'm notorious at over animated diagrams. And this one, we had this K-12 calculator that we've been working on. It's going to be rolling out at the end of the year. What it does is calculates the number of spaces that are required for a school and pushes it into clarity. And then clarity takes that and we can go to Rhino or Revit and design inside of them.

And it's at these points that we want to inject machine learning into an audit on that list of rooms coming from the calculator or anything else that's getting uploaded and then in the design process. So I put together yet another terribly complicated diagram to try to explain what this new process would be like. But basically, today, or yesterday, we had a planner that had their own knowledge in Excel for a project, and it was pretty siloed to their circle of friends.

So now, because we have this data warehouse, we are looking at projects across the firm and getting better averages to make better decisions on projects. So we're right here, and we're right on the edge of what I'm hoping to come back and tell everybody about the amazing ways that machine learning is impacting our design process. So we did an R&D project last year, and we wanted to set out to answer three questions, can we learn from our past projects, can we make suggestions in space programming, and can we predict placement of furniture?

And it turns out that we could figure this out. Revit users don't fill out all the room parameters, and they don't call things the same way, and they like to abbreviate to make a name fit on a floor plan. So we have these incomplete lists, but with a list of the furniture items inside of a room, could we guess what the rest of those remaining things were And it turns out that we could do this with machine learning by reclassifying and using text analysis on these items.

And so there's some cool data science stuff that we got into, and these are awesome charts and diagrams that we were looking at-- offices and bathrooms-- to see if we could spot common trends. And what you're seeing here is that we found that upper cabinet and base cabinet show up by each other. No kidding. These were things, we were spotting the patterns, which is great.

And then what we did is, we have this tool. It's called Smart Copy, and I showed the W benchmarking tool. It's using machine learning. This one is not. It's using heavy computation. But what it's doing is let you lay out a room, and then it reads the center of the room, the door, and features like windows and the general shape of it and tries to take a best guess of what that orientation should be.

So it could be mirrored, it could be a rectangle, and it goes and places those. And so here, we can stop having the conversation of, is it a super group or a model group or a superfamily or whatever we want to do, because we're just managing the data. So with this sort of process, because we can start to spot common rooms and the common things inside of rooms and the relationship between those things, what we want to do is start to have it that you can select the room, know what type it is, and push in the commonly suggested furniture items.

And this is a really bad proof of concept. You can see that the stuff laid out not great, but what we wanted to do is see, could we take the machine learning information about a room and then push it into the room and copy it around just like we did with that Smart Copy, and we were able to. So it's only a proof of concept, but we are going to continue on this research this fall.

So let's tie a bow on this thing and wrap this up. Just to recap, if it's part of a floor, use a design option. If it's the whole floor, save it out as a link. If you have a space program, space planning blocks are great. If you have to create a program, use a type-based block with room sizes into it. It helps you out, and you can place things, and you can use calculators behind to generate your program. Beyond that, use data analysis, computational tools, and help figure out what you need inside your building, and automate everything you reasonably should.

So our approach throughout. This is a bit of a chest and stairs. So we've had a long-term strategy, which I showed with those R&D diagrams. We look out, but what we do is keep a close eye on adoption, and we don't try to do it all at once. We've worked with our teams for constant impactful improvement on each project, and we make each move with the strategy of chess and the incremental climb of a stair step by step.

So with that, thank you for listening. We really appreciate you listening to this course. If you have questions that you want to follow up, you can connect with myself or Ju-Hui on LinkedIn. We'd be more than happy to talk to you.

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Adobe Analytics
我们通过 Adobe Analytics 收集与您在我们站点中的活动相关的数据。这可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID、您的 Autodesk ID。我们使用此数据来衡量我们站点的性能并评估联机体验的难易程度,以便我们改进相关功能。此外,我们还将使用高级分析方法来优化电子邮件体验、客户支持体验和销售体验。. Adobe Analytics 隐私政策
Google Analytics (Web Analytics)
我们通过 Google Analytics (Web Analytics) 收集与您在我们站点中的活动相关的数据。这可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。我们使用此数据来衡量我们站点的性能并评估联机体验的难易程度,以便我们改进相关功能。此外,我们还将使用高级分析方法来优化电子邮件体验、客户支持体验和销售体验。. Google Analytics (Web Analytics) 隐私政策
AdWords
我们通过 AdWords 在 AdWords 提供支持的站点上投放数字广告。根据 AdWords 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 AdWords 收集的与您相关的数据相整合。我们利用发送给 AdWords 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. AdWords 隐私政策
Marketo
我们通过 Marketo 更及时地向您发送相关电子邮件内容。为此,我们收集与以下各项相关的数据:您的网络活动,您对我们所发送电子邮件的响应。收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID、电子邮件打开率、单击的链接等。我们可能会将此数据与从其他信息源收集的数据相整合,以根据高级分析处理方法向您提供改进的销售体验或客户服务体验以及更相关的内容。. Marketo 隐私政策
Doubleclick
我们通过 Doubleclick 在 Doubleclick 提供支持的站点上投放数字广告。根据 Doubleclick 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 Doubleclick 收集的与您相关的数据相整合。我们利用发送给 Doubleclick 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. Doubleclick 隐私政策
HubSpot
我们通过 HubSpot 更及时地向您发送相关电子邮件内容。为此,我们收集与以下各项相关的数据:您的网络活动,您对我们所发送电子邮件的响应。收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID、电子邮件打开率、单击的链接等。. HubSpot 隐私政策
Twitter
我们通过 Twitter 在 Twitter 提供支持的站点上投放数字广告。根据 Twitter 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 Twitter 收集的与您相关的数据相整合。我们利用发送给 Twitter 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. Twitter 隐私政策
Facebook
我们通过 Facebook 在 Facebook 提供支持的站点上投放数字广告。根据 Facebook 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 Facebook 收集的与您相关的数据相整合。我们利用发送给 Facebook 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. Facebook 隐私政策
LinkedIn
我们通过 LinkedIn 在 LinkedIn 提供支持的站点上投放数字广告。根据 LinkedIn 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 LinkedIn 收集的与您相关的数据相整合。我们利用发送给 LinkedIn 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. LinkedIn 隐私政策
Yahoo! Japan
我们通过 Yahoo! Japan 在 Yahoo! Japan 提供支持的站点上投放数字广告。根据 Yahoo! Japan 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 Yahoo! Japan 收集的与您相关的数据相整合。我们利用发送给 Yahoo! Japan 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. Yahoo! Japan 隐私政策
Naver
我们通过 Naver 在 Naver 提供支持的站点上投放数字广告。根据 Naver 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 Naver 收集的与您相关的数据相整合。我们利用发送给 Naver 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. Naver 隐私政策
Quantcast
我们通过 Quantcast 在 Quantcast 提供支持的站点上投放数字广告。根据 Quantcast 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 Quantcast 收集的与您相关的数据相整合。我们利用发送给 Quantcast 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. Quantcast 隐私政策
Call Tracking
我们通过 Call Tracking 为推广活动提供专属的电话号码。从而,使您可以更快地联系我们的支持人员并帮助我们更精确地评估我们的表现。我们可能会通过提供的电话号码收集与您在站点中的活动相关的数据。. Call Tracking 隐私政策
Wunderkind
我们通过 Wunderkind 在 Wunderkind 提供支持的站点上投放数字广告。根据 Wunderkind 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 Wunderkind 收集的与您相关的数据相整合。我们利用发送给 Wunderkind 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. Wunderkind 隐私政策
ADC Media
我们通过 ADC Media 在 ADC Media 提供支持的站点上投放数字广告。根据 ADC Media 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 ADC Media 收集的与您相关的数据相整合。我们利用发送给 ADC Media 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. ADC Media 隐私政策
AgrantSEM
我们通过 AgrantSEM 在 AgrantSEM 提供支持的站点上投放数字广告。根据 AgrantSEM 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 AgrantSEM 收集的与您相关的数据相整合。我们利用发送给 AgrantSEM 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. AgrantSEM 隐私政策
Bidtellect
我们通过 Bidtellect 在 Bidtellect 提供支持的站点上投放数字广告。根据 Bidtellect 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 Bidtellect 收集的与您相关的数据相整合。我们利用发送给 Bidtellect 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. Bidtellect 隐私政策
Bing
我们通过 Bing 在 Bing 提供支持的站点上投放数字广告。根据 Bing 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 Bing 收集的与您相关的数据相整合。我们利用发送给 Bing 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. Bing 隐私政策
G2Crowd
我们通过 G2Crowd 在 G2Crowd 提供支持的站点上投放数字广告。根据 G2Crowd 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 G2Crowd 收集的与您相关的数据相整合。我们利用发送给 G2Crowd 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. G2Crowd 隐私政策
NMPI Display
我们通过 NMPI Display 在 NMPI Display 提供支持的站点上投放数字广告。根据 NMPI Display 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 NMPI Display 收集的与您相关的数据相整合。我们利用发送给 NMPI Display 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. NMPI Display 隐私政策
VK
我们通过 VK 在 VK 提供支持的站点上投放数字广告。根据 VK 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 VK 收集的与您相关的数据相整合。我们利用发送给 VK 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. VK 隐私政策
Adobe Target
我们通过 Adobe Target 测试站点上的新功能并自定义您对这些功能的体验。为此,我们将收集与您在站点中的活动相关的数据。此数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID、您的 Autodesk ID 等。根据功能测试,您可能会体验不同版本的站点;或者,根据访问者属性,您可能会查看个性化内容。. Adobe Target 隐私政策
Google Analytics (Advertising)
我们通过 Google Analytics (Advertising) 在 Google Analytics (Advertising) 提供支持的站点上投放数字广告。根据 Google Analytics (Advertising) 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 Google Analytics (Advertising) 收集的与您相关的数据相整合。我们利用发送给 Google Analytics (Advertising) 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. Google Analytics (Advertising) 隐私政策
Trendkite
我们通过 Trendkite 在 Trendkite 提供支持的站点上投放数字广告。根据 Trendkite 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 Trendkite 收集的与您相关的数据相整合。我们利用发送给 Trendkite 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. Trendkite 隐私政策
Hotjar
我们通过 Hotjar 在 Hotjar 提供支持的站点上投放数字广告。根据 Hotjar 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 Hotjar 收集的与您相关的数据相整合。我们利用发送给 Hotjar 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. Hotjar 隐私政策
6 Sense
我们通过 6 Sense 在 6 Sense 提供支持的站点上投放数字广告。根据 6 Sense 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 6 Sense 收集的与您相关的数据相整合。我们利用发送给 6 Sense 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. 6 Sense 隐私政策
Terminus
我们通过 Terminus 在 Terminus 提供支持的站点上投放数字广告。根据 Terminus 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 Terminus 收集的与您相关的数据相整合。我们利用发送给 Terminus 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. Terminus 隐私政策
StackAdapt
我们通过 StackAdapt 在 StackAdapt 提供支持的站点上投放数字广告。根据 StackAdapt 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 StackAdapt 收集的与您相关的数据相整合。我们利用发送给 StackAdapt 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. StackAdapt 隐私政策
The Trade Desk
我们通过 The Trade Desk 在 The Trade Desk 提供支持的站点上投放数字广告。根据 The Trade Desk 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 The Trade Desk 收集的与您相关的数据相整合。我们利用发送给 The Trade Desk 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. The Trade Desk 隐私政策
RollWorks
We use RollWorks to deploy digital advertising on sites supported by RollWorks. Ads are based on both RollWorks data and behavioral data that we collect while you’re on our sites. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. This information may be combined with data that RollWorks has collected from you. We use the data that we provide to RollWorks to better customize your digital advertising experience and present you with more relevant ads. RollWorks Privacy Policy

是否确定要简化联机体验?

我们希望您能够从我们这里获得良好体验。对于上一屏幕中的类别,如果选择“是”,我们将收集并使用您的数据以自定义您的体验并为您构建更好的应用程序。您可以访问我们的“隐私声明”,根据需要更改您的设置。

个性化您的体验,选择由您来做。

我们重视隐私权。我们收集的数据可以帮助我们了解您对我们产品的使用情况、您可能感兴趣的信息以及我们可以在哪些方面做出改善以使您与 Autodesk 的沟通更为顺畅。

我们是否可以收集并使用您的数据,从而为您打造个性化的体验?

通过管理您在此站点的隐私设置来了解个性化体验的好处,或访问我们的隐私声明详细了解您的可用选项。