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BIM Automation: Generatively Designing Resilient Infrastructure with Revit

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说明

In this session, we'll explore how building information modeling (BIM) automation techniques have been used to create 15,000+ generative designs for critical water and power infrastructure projects using Revit software. You will learn which tools are available for design automation efforts, including generative algorithms and artificial intelligence, and how these techniques plus Revit are helping owners and engineers automatically assess the most climate-resilient infrastructure options in the preliminary design phase of projects. We'll showcase specific examples of how Revit is being used to serve underrepresented communities in Brazil that are most impacted by climate change—focusing on the automatic generation of embedded carbon calculation documentation, CAPEX/OPEX tables, 3D models, and more, for wastewater treatment plants and electrical substations.

主要学习内容

  • Learn how to implement best practices for automation in BIM workflows, including generative algorithms and artificial intelligence.
  • Learn the frameworks to properly apply generative design techniques to infrastructure projects using Revit.
  • Discover the benefits of using BIM automation for designing resilient infrastructure.
  • Learn how to create compelling business cases for Revit automation tools, especially to design infrastructure for climate constraints.

讲师

  • Adam Tank 的头像
    Adam Tank
    Adam has over 15 years of experience in the water & infrastructure industries with a focus on start-up innovation, software, and business development. As the Chief Customer Officer at Transcend he has responsibility for client success related to Transcend Design Generator (www.transcendinfra.com) and the automation of preliminary engineering activities. Most recently he served as the North America Smart Cities Director at Suez. He previously led, and sold, a robotics spin-out of the General Electric corporation which focused on cutting edge potable water pipe rehabilitation techniques. Prior to that he serves as GE Water’s Digital Water Leader, managing venture investments and creating software solutions for water distribution challenges. Earlier in his career Adam serves as an engineer in the CPG industry where he both lived and worked in Brazil, and led sanitation programs for General Mills’ largest yogurt plant in North America. Adam received his undergraduate degree in microbiology from Kansas State University and his M.B.A. from the University of Arizona. He is a foster dad, bio dad, outdoorsman, and avid reader and writer.
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Transcript

ADAM TANK: If you are here for the BIM automation, generatively designing resilient infrastructure with Revit class, you are in the right place. If not, I'm glad you're here. Maybe you'll stay, maybe you won't. But, in either case, we're going to have a ton of fun together. So let's go ahead and get started.

We are at an inflection point. There is a shift from manual tasks performed by humans to automated processes powered by machines that's transforming entire industries right now. And it's across all of them. No one is immune to this. If we think about healthcare with computer-led decision making, robotic surgeries, all the way to HR, where we have intelligent automated workflows, there is not a single person, a single job, that's being untouched by advances in automation.

Take a look at this picture of Wall Street from about 100 years ago. You see hundreds, if not thousands, of people screaming over one another, shouting with paper tickets, trying to make stock trades. If you now go to the floor, the trade floor, this is the view that you see. These humans that were once shouting are now behind the scenes, and the computers are now powering that trading. And in large-- in many cases, they're doing it somewhat automated.

Very similar situation if we look at hospitals. We used to have dozens of people in a room operating on one patient, some observing, some taking pictures, some learning. And now all you need is maybe two people in addition to a robot and automation, who are delivering better outcomes for the patient with far less effort, and energy, and time than we expended before.

One of my favorite videos showcasing the power of advances in technology is with F1 formula races. So I want you to take a gander at this video. I'll play it in its entirety. And then we'll talk about some of the differences that we see between the 1981 team and the 2019 team.

[VIDEO PLAYBACK]

[CAR SOUNDS]

[END PLAYBACK]

It is almost laughable how different these two teams are. The 1981 team clearly was struggling to get their car back in the race, where the 2019 team, with advances in technology and probably some extra skill from the team members, were able to get their car back into the race in mere seconds.

If we translate this to what's happening in the engineering industry, you look back in the 1980s, you probably would have seen a room that looks similar to this one. Dozens, if not hundreds, of people hovering over drafting tables, creating designs using pen and paper. Of course, there's been advances in technology and, to some degree, automation.

But my question is, are we really the Formula One team of the future? Have we truly embraced automation and generative design when it comes to engineering of critical infrastructure? I believe that the AEC and EPC world will be transformed by radical advances in BIM automation and specifically with generative design, which is what we're going to be talking about today.

But I want to pose the question, why will this happen, and why is it so important? I'll call your attention to a really interesting article that was published in Palo Alto online in August 22 of this year, 2023. Palo Alto is eager to expand the footprint of their wastewater treatment plant. They know they have to make it a bit bigger in order to handle more volumes of wastewater and some other future scenarios.

But the challenge they have is that it's unbelievably cramped in Palo Alto and real estate is unbelievably expensive. So if you look at this graphic, the city is trying to piece together, do we buy more acres to the south? Do we pass a new measure to buy land to the east? Do we maybe use the existing site footprint better in a way that we don't have to buy more real estate?

Ultimately, the utility leaders and the public works directors are scratching their heads because nobody has a crystal ball. Nobody can predict what they need to do in order to figure out where they should put the next upgrade, especially when they're space-constrained and when new regulations for wastewater are literally coming down the pipe.

Karen North, their assistant public works director, says that, "I view this as an opportunity to future-proof our facility for hopefully 50 years." In other words, she wants to be able to evaluate options as early on in this process as possible to create a more resilient facility given the constraints of today but also the constraints 50 years from now.

The challenge with that is that the way that we do design in this industry is incredibly manual, cumbersome, and oftentimes repetitive. And we certainly see that happening in the civil and architectural space.

So if Karen is to kick off this project to see what options she has available to her, she's going to launch this project, and then an engineering firm will start with their process engineering team to look at the process of treating wastewater. They'll do their work and hand that to the mechanical team, who then will hand that to their controls and instrumentation team, who then hand that to their civil and architectural team, who finally then submit it to the client.

Karen will review, and she'll probably say, you know what? There might be some other things we should consider here. Can you maybe look at a new scenario in regards to potential rainfall? Can you look at a new scenario in regards to population growth? And this process starts all the way back at the process engineering team.

There are some advances in technology that these firms are using today. Designs may be completed manually in AutoCAD. And there's an occasional use of Revit families to help speed up this process. Independent of those though, this process still takes hundreds or thousands of hours with human touch in every one of these engineering disciplines. And if someone like Karen needs a change, it sometimes requires complete revisions of all of the design work that the engineering firms had done.

What if there was a better way? What if we could use advances in automation, and specifically generative design in Revit, to give Karen the options that she's looking for early on in this process, so she can ultimately build the most resilient wastewater plant of the future?

There is. And that's what I want to show you, is that these tools are being used today on real-world projects to help utilities deliver more resilient, more sustainable, and more innovative critical infrastructure very, very early on in the processes. So let's look at the case of a US water utility that were in a very similar situation to Karen, looking at their options to upgrade an existing 24 million gallon-a-day wastewater treatment plant.

What they wanted to do was have the engineering consultants look at three scenarios. What will the future effluent requirements be? In other words, how stringent do we need to treat this wastewater? What if the population increases based on our growth patterns and potentially other businesses moving in? And what is to happen if we have historic floods, like North Texas has recently been having, what impact does that have on our treatment plant, not only today, but 20, 30, 50 even 100 years from now?

But, of course, to do this manually, as we just discussed, it takes an unbelievable amount of budget and time that neither the asset owner nor the engineering firm has. If we think about why this is so impossible to do, and why we don't have the time and budget to do it, this utility was particularly interested in a couple of different use cases.

In this example, they wanted to see multiple wastewater treatment technologies. They wanted to see scenarios for upgrading for future effluent requirements. They had to meet new more stringent phosphorus limits in the wastewater. And they knew that they would have varying population growth models.

And maybe most interestingly, when it comes to resiliency, they wanted to ensure the facility would be able to address the impacts of climate change, such as more volatile weather patterns and the increased frequency and intensity of extreme weather events. This screams resiliency. And it screams opportunity to use new advances in technologies.

In fact, as part of this master plan update, they requested the engineering consultants to provide 10x the number of scenarios. In other words, they started with those three and then they then asked for 30 scenarios to be evaluated. I'll show you the text here, and I'll give you about 30 seconds to read this, and then we'll talk about it.

If you're an engineering consultant, your job probably has hit the floor. You would never be expected to do this traditionally using manual design methods because it simply wouldn't be possible. This work is typically left for conceptual design or detailed design phase and certainly not in the submission stage.

There are a couple of things that stood out to me. One was that 30 conceptual design scenario. Another one was that they wanted the engineering firms to use conceptual design software that automated a number of these engineering discipline calculations, including the generation of a 3D BIM model, producing the editable outputs, the native files, in both Autodesk AutoCAD and Autodesk Revit.

So you might be wondering the question, why 30 options? Well, we talk about resiliency and we talk about wanting to be able to deliver better outcomes to these asset owners. In the case of the Texas wastewater treatment plant, they had the three scenarios that they wanted evaluated.

But it's not just those three scenarios that we might want to evaluate to create a future-proof wastewater treatment plant. We may also want to look at different variations of each of these options. So on the technology side, we have not only the way you treat the wastewater but maybe how the wastewater treatment plant is operated.

And, of course, the site footprint, just like in Palo Alto, you want to see, what if we put our new assets here? What if we use our existing treatment assets? What if we can't buy the land? And what if we need to repurpose or we need to demolish and build new? Same thing with temperature and same thing with the wastewater.

And you even branch out further from there. So it's not just three individual options, it's a permutation of all of these together. Because every time you change one, you have to do those calculations all over again, which, again, simply wouldn't be possible using existing traditional design methods.

So how does this actually work? What does it mean to automate BIM modeling? What does it mean to generatively design wastewater treatment plants and critical infrastructure? What we see here is software in the cloud actually instructing Revit to arrange rooms inside of buildings on its own.

So this is not a human designer who's laying out these blueprints, this is the computer actually saying, based on the constraints you've given me, here's where I think these new assets should go. Here's how big they should be. And here's how those assets should, in fact, perform. You can see us manipulating that 3D view right now. Of course, this doesn't look like a Revit file, but it very will soon.

Once you have these buildings and you know how big they are, software now can actually manipulate these buildings and arrange them on a site. So, historically, engineers would take cut-out pieces of paper and move them manually on a blueprint. And now, software is doing that effectively automated.

Once you have that, once you have the buildings laid out on site, you know where they're going to be, you can actually instruct Revit to create the full 3D BIM model with all of the associated elements. So, in this case, the concrete volumes, the quantities, the thicknesses, fill volumes, takeoffs, everything that we know and love about Revit, you have all that data available to you.

And, of course, now that we have the BIM model, we can also take that data and translate it into documents, like a civil boq or a [INAUDIBLE] unbelievably helpful when you're costing these projects early on and you're evaluating this range of scenarios as early as the submission phase for these projects.

So if we took a look at what happened with this particular treatment plant, the bottom half of the screen where all of these assets are is the existing treatment works. They didn't want to touch this. The wastewater utility didn't want to touch these. And they wanted to use this-- we'll call it whitespace at the top-- to see what new assets do we need and what space will they occupy?

So the software that we used generatively designed the new assets. And it did it all in Revit. So here we have the floor plan or the site footprint. And we had told the software the constraints around the existing assets. And by pressing go and just a couple hours later, the software generatively designed all the new assets required, their sizes, their shapes, and then laid them out on the site.

So if we look at what that looks like from a BIM point of view, we now have the existing assets to the bottom half of the screen in gray. And we have the new assets on top. And, of course, once you have this BIM model, you can do some really cool visualization work.

So, in this case, we've taken the 3D BIM model in Revit. We've overlaid it on a site marking. And now the utilities have a much better view of exactly what this treatment plant will look like in 50 years. And you've been able to evaluate a full range of potential scenarios as part of the project.

So if we think about that previous process, where we had the project launched all the way through to submission to the client, the multiple iterations, all of the engineering handoffs, we now have a much more streamlined process where we have the software doing the conceptual design work utilizing Revit, the outputs are then taken-- dozens of outputs are taken and handed to the client after buy-in from project partners.

And you ultimately are able to evaluate a much wider range of scenarios, much wider range of resiliency, and you save a ton of time and energy. So we combine multiple decisions from engineering fields. We explore a wider range of scenarios. And, ultimately, the owner is much better off, building the plant that they know they need to last for decades and decades into the future.

At the end of this process, many of the firms that bid on this work did include conceptual designs in its bid package which, again, is typically not possible using manual methods. The winning EPC firm collaborated to produce these 30 or more alternatives.

And, now, the utility is using similar techniques on other master-planning updates. So we've seen the success of generative design and Revit that provides the outcome that utilities are looking for.

In this case, though, we only barely scratched the surface. As we talked about, the utility wanted to evaluate these three to 30 scenarios. But we still have so many more possibilities available to us, which is exciting. It's the options for us to look at what resiliency truly means in the case of wastewater treatment planning.

Let's take that crystal ball concept and give these utilities the resilience that they need in order to build the infrastructure that our humanity, that our country, that the world needs to thrive. We can go from this limited set of design options and potential scenarios for evaluation-- in the case of the Texas treatment plant, those three-- and we can do upwards of 30 or even more.

The only constraint on our ability to assess and design the resilient infrastructure we need is going to be from the imagination of the engineer, not the technology, not the time, and not the budget.

How do we know this works? Our firm, Transcend, and the users of our tools, have designed over 15,000 of these types of infrastructure to date. And what's interesting is that we work across multiple customer segments, not just the engineering consultants and the asset owners, so folks like BRK Ambiental in Brazil, or Scottish Water in the UK, Black & Veatch in the States, Stanley Consultants in the States.

But we also work with the technology suppliers that bring this equipment into these projects to also provide resiliency and more innovation. So we enable all the ecosystem players to think holistically about what resiliency means in order to come up with the best potential outcome in the planning process.

We not only do this in water and wastewater treatment plants, as you've seen today, but we also work in the power industry, where we automate the design of electrical substations using a very similar process in our generative design platform.

And, finally, there are over a hundred of us working to build the tools of the future that our engineers need in order to design these resilient infrastructures. And we have our own set of talented and unbelievably passionate engineers and computer programmers that sit side by side to ensure that this platform delivers real, secure, trusted data, so that these asset owners can make the best decisions for themselves.

We work internationally, US and Hungary and, of course, have also had a wide range of experiences in multiple different sizes of companies, not just our little startup.

So help me usher in this era of automation into the engineering design industry. We cannot possibly do all of this work manually. Automation is the key to resilient infrastructure. And with your help, I am confident that we can bring in a new era of resiliency and sustainability. Thank you.

______
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我们通过 Typepad Stats 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。. Typepad Stats 隐私政策
Geo Targetly
我们使用 Geo Targetly 将网站访问者引导至最合适的网页并/或根据他们的位置提供量身定制的内容。 Geo Targetly 使用网站访问者的 IP 地址确定访问者设备的大致位置。 这有助于确保访问者以其(最有可能的)本地语言浏览内容。Geo Targetly 隐私政策
SpeedCurve
我们使用 SpeedCurve 来监控和衡量您的网站体验的性能,具体因素为网页加载时间以及后续元素(如图像、脚本和文本)的响应能力。SpeedCurve 隐私政策
Qualified
Qualified is the Autodesk Live Chat agent platform. This platform provides services to allow our customers to communicate in real-time with Autodesk support. We may collect unique ID for specific browser sessions during a chat. Qualified Privacy Policy

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改善您的体验 – 使我们能够为您展示与您相关的内容

Google Optimize
我们通过 Google Optimize 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。. Google Optimize 隐私政策
ClickTale
我们通过 ClickTale 更好地了解您可能会在站点的哪些方面遇到困难。我们通过会话记录来帮助了解您与站点的交互方式,包括页面上的各种元素。将隐藏可能会识别个人身份的信息,而不会收集此信息。. ClickTale 隐私政策
OneSignal
我们通过 OneSignal 在 OneSignal 提供支持的站点上投放数字广告。根据 OneSignal 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 OneSignal 收集的与您相关的数据相整合。我们利用发送给 OneSignal 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。. OneSignal 隐私政策
Optimizely
我们通过 Optimizely 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。. Optimizely 隐私政策
Amplitude
我们通过 Amplitude 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。. Amplitude 隐私政策
Snowplow
我们通过 Snowplow 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。. Snowplow 隐私政策
UserVoice
我们通过 UserVoice 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。. UserVoice 隐私政策
Clearbit
Clearbit 允许实时数据扩充,为客户提供个性化且相关的体验。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。Clearbit 隐私政策
YouTube
YouTube 是一个视频共享平台,允许用户在我们的网站上查看和共享嵌入视频。YouTube 提供关于视频性能的观看指标。 YouTube 隐私政策

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定制您的广告 – 允许我们为您提供针对性的广告

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。