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Boosting Project Performance: Engineering Automation Workflow Using Dynamo

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Description

Revit software is a powerful tool in the building information modeling (BIM) world that can be linked to Robot Structural Analysis software. Structural analysis requirements and constraints make this link difficult to manage and often result in time-consuming manual adjustments in models. This class will explain how you can use Dynamo for automated creation of intelligent models (including MEP design) and structural analysis models with one engineering data set input based on shaft structure and pumping station project example. The intelligent model output includes all necessary information, schedules with concrete, and mapped required reinforced steel quantities. The second model output uses the same unique geometry while considering all structural load cases and combinations required for detailed design. We’ll show you how this engineering workflow allows users—with a previously impossible agility— to react quickly to change, leaving more time to on focus on core engineering rather than model and deliverable generation.

Key Learnings

  • Design the workflow to automate the creation of Revit and Robot Structural Analysis models using Dynamo.
  • Learn how to implement automation workflow as part of engineering projects to reduce time-consuming tasks, risk of errors, or inaccuracies.
  • Learn about maximizing automation workflow to quickly adapt on last-minute changes.
  • Learn about advantages and disadvantages of developing advanced Dynamo script.

Speakers

  • Avatar for Wojciech Mleczko
    Wojciech Mleczko
    Civil Engineer interested in Bridge and Tunnel Structures with strong background of Building and Engineering Structures. Developing in Building Information Modelling and Structural Analysis.
  • Gary Furphy
    Gary Furphy is Director of Digital Engineering for Jacobs in the Middle East. Gary is a passionate leader, driven to transform operational approaches and drive adoption of technology to push boundaries to make project delivery more efficient and enjoyable. With over 25 years of global experience, having worked on a myriad of projects in both Europe and the Middle East, spanning sectors such as Pharmaceuticals and Rail. He had led the delivery of BIM and Asset Information on many projects. His holistic approach involves seamlessly connecting disparate solutions and methodologies to find the optimal approach for each unique need. Gary is passionate about fostering a culture of continuous learning and improvement, understanding the varying learning curves of individuals and encouraging an environment where knowledge and experiences are collaboratively shared and valued. Ultimately, Gary aspires to empower people to make better decisions through the application of digital technology.
  • Avatar for Emmanuel Lagardette
    Emmanuel Lagardette
    Emmanuel is an expert in structural analysis with more than 28 years’ experience in design technology and the AEC industries. In his current role, he leads a team of technical consultants solving complex problems to customers’ advanced requirements. As a technical consulting manager, Emmanuel combines his own broad technical background with a strategic business perspective to help customers develop holistic solutions.
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      Transcript

      WOJCEICH MLECZKO: Hello, everyone. This presentation is about developing of engineering automation workflow using Dynamo to improve product performance. We will cover aspects of package of scripts related to Revit and Robot Structural analysis. Furthermore, we will try to suggest potential improvement of post-processing tasks.

      EMMANUEL LAGARDETTE: First of all, we will give you a moment to read the Safe Harbor statement, which includes important information. Now, you have all read it, here is a brief introduction on Jacobs and who we are.

      [MUSIC PLAYING]

      WOJCEICH MLECZKO: My name is Wojceich Mleczko. I'm a Bridge and Tunnel Engineer representing Jacobs, and please meet my co-speakers.

      GARY FURPHY: My name is Gary Furphy. I'm a Digital Engineering Manager based out of Dubai in the Middle East.

      EMMANUEL LAGARDETTE: My name is Emmanuel Lagardette, Implementation Consultant within the Autodesk enterprise customer success organization. The agenda for this class is the following.

      After a short introduction including a brief on the shaft [INAUDIBLE], an overview of the project and the project team, we will review the typical challenge phase during the design phase of this kind of project, the solution we decided to implement helping to mitigate those challenges, and optimize project tasks.

      Then, we will have a look on the solution workflow more in detail, and as a conclusion, review and discuss the benefit of implementing this solution on the project. As an introduction, let's have a look with Gary on shaft [INAUDIBLE], the project itself, and the project team.

      GARY FURPHY: Thank you. You may ask yourself, why will you decide to automate an engineering solution around shafts? Well, shafts are actually everywhere. They're under our feet in pretty much every country in the world, and they have more similarities than differences.

      They're used for water supply, stormwater, drainage, hydroelectric, ventilation, and even in metros for emergency escape shafts. We believed we could improve outcomes by creating a solution that merged multiple processes across engineering, building information modeling, and analysis into one solution.

      We engaged with Autodesk to initially create a solution for one project in the Middle East and to test and validate those assumptions before expanding globally. This presentation is that story.

      WOJCEICH MLECZKO: We'll focus on drainage network project as an example where we have faced 35 kilometers of tunnels, what was followed by 30 shaft structures, the same type with various depth, diameter, and [INAUDIBLE] thicknesses. And due to the number and repeatability of structures, it was decided to automate the design process.

      GARY FURPHY: So as I said, we engaged Autodesk in this, and this is the project team that we came up with for creating this solution. We had Wojciech, myself, and Wolfgang Angerer-- who unfortunately couldn't be here today-- who's Engineering Tunnel Director also based in the Middle East.

      From the Autodesk side, we had Emmanuel, who's presenting today, and Ian Bond, the Principal Consulting Project Manager. Now, let's review some of the challenges with Emmanuel-- or myself.

      OK. So on all projects, change management is critical to success. Change will happen. It's how it's handled that's important. In the Middle East, we often have the advantage that we're building new infrastructure and greenfield sites. Yet, at the same time, our construction programs are extremely compressed.

      For this [? strange ?] project in the Middle East, we have internal and external influences that affect the engineering and outcomes. The design process can be highly impacted by modifications that occur throughout the project. There are plenty of reasons for this change to occur.

      On the left hand side of the slide, you see examples of some of these, and there's a lot of internal factors that can-- from modifications from different disciplines. On the right hand side of the slide, you can see the external influences on the project. We have influences from the clients themselves, location of tunnels, location of shafts, of infalls and outfalls.

      On land development planning, particularly when you're building new cities, there's a lot going on at once, and you may be constrained by plots of land or building in those areas. And of course, we have the hydrology, and also then adjacent utilities or future utilities to consider as well.

      Each change will have an impact on multiple disciplines working on a project in parallel. Furthermore, on a drainage and stormwater network, a modification on one side of the project might have massive impact on a whole network in a domino effect.

      So let's talk a little bit about the actual project change management. So on the right hand side of this slide, you're seeing an example that affects three disciplines where we have a workflow with a start, an input definition modification. Then, we have production from each those disciplines and outputs and checking.

      What we can see is that for this process, each time a modification is required, it impacts the input data of one part of the project, and each discipline check could require additional changes or completely invalidate the previous work. Those additional changes must be communicated to all other disciplines that have to perform, then, their own check and analysis considering their discipline-specific constraints.

      And then this implies even more changes to the project design. All of this requires a lot of rework and models, deliverables, analysis, CFD. This will mechanically increase the delay in knowing each change's precise impact, and obviously, increasing the design time and the budget.

      So when we look at creating an engineering solution for shafts like this, there's a number of business drivers. Number one, productivity. We need to do more with less. We want to integrate. So on a large scale project, there can be many disciplines and hundreds of people. We want to reuse the same information for many purposes.

      We also want to standardize the way we work. And we recognize that with the shaft automation, we can apply this as a global solution, and then we can also increase quality and insurance and bring more value for our clients. So the savings don't only impact our side, they also impact our clients as well.

      And then through optimization of network design, we can have a reduction in embodied carbon. Now, let's look at the solutions approach with Emmanuel.

      EMMANUEL LAGARDETTE: The shaft design are often standardized and based on the same concept, mainly to facilitate and rationalize the maintenance and operations phase. Our shaft design are standardized.

      This means one easy way of mitigating the impact of the design changes is to standardize their input data, automate their BIM and structural analysis models, the quantification of their required reinforcements, the deliverables, drawing, schedules, or even analysis reports.

      This standardization and automation have been done using the following technologies. Revit [INAUDIBLE] BIM model, Robot Structural Analysis for their structural analysis model, also linked to the BIM model, BIM 360 Document Management, used as a common data environment for the whole project hosting model files, but also project documentation, Microsoft Excel to manage and standardize approach to aggregate shaft input data, but also to compute specific [INAUDIBLE] and transfer it into the structural analysis model.

      Last but not least, as it is the backbone of this solution, Dynamo for Revit to automate [INAUDIBLE] Dynamo graph the creation of the BIM model of the structural analysis model, the analysis report, the reinforced concrete quantities, and also the drawing and schedule production out of the BIM model.

      Here is the solution workflow describing the solution. First of all, we have the Excel file for grouping all the input data that engineer can complete or update easily, the Dynamo graph A1 to create the BIM model, the Dynamo graph A2 create the structural analysis model from the Excel file and the BIM model.

      After those two steps, the structural engineers can perform their checks using Robot structural analysis, including the reinforced concrete design. If all design criteria are not met, engineers cannot take the shaft input data [? store in ?] Excel. The theme and structural analysis model can be updated until all design requirements are met.

      When all of those design requirements are met, the Dynamo graph actually generates automatically the analysis report from Robot structural analysis, the Dynamo graph A4, [INAUDIBLE] reinforcement quantities on each concrete component of the BIM model.

      The required reinforcement [INAUDIBLE] will be included in each shaft concrete component as a shared parameter in the Revit model. Finally, views, sheets, and [INAUDIBLE] of quantities will be generated automatically using the Dynamo graph A5.

      Let's now review the solution main principles. Those main principles are the following. All shaft project input data are stored in one unique Excel file. This file is a unique source of data used by [INAUDIBLE] tool easily manageable for any update or checking.

      The five automated tasks can be used independently. This ensures the flexibility of the workflow if needed in case of error. It is always possible to run one specific Dynamo graph to regenerate the BIM model [INAUDIBLE] report, or even to create an experimental structural analysis model.

      Thanks to being BIM 360, the common data environment used for this project, includes all project data as the input data [INAUDIBLE] but also the model and the deliverables. It [INAUDIBLE] project stakeholders to have a seamless access to the last update of the project data and control their versions.

      The automation tool has been developed in Dynamo for Revit based mainly on Python script, but also using the Visual Basic for application in Excel using straight versus compile plugins [INAUDIBLE] to adapt them very quickly to any potential changes from a project to another or even within the same project.

      Those four main principles help to reduce inconsistencies and risk of error during the design phase of the project while keeping the solution quite flexible. Let's now review the detailed workflow of the solution with Wojceich.

      WOJCEICH MLECZKO: The whole process of automation is controlled by the Excel file. Each row represents data for each structure, and each worksheet is associated with a particular data set, such as coordinates, element dimensions, soil parameters, loss load combinations.

      Starting from the general information source, coordinates, ground level, incoming and outgoing [? Thomas ?] diameter, angle from the north, and level with this data received [INAUDIBLE] of the structure. Next parameters are tunnel wall thickness and slope. The same approach is used for defining connections data.

      The next step is related to definition of elements parameters. This worksheet is connected with bottom chamber of the structure. You can define the diameter of the chamber, bottom slab, and top slab levels. They are calculated by the Excel.

      It's not a [? black box, ?] and all values can be controlled by the user engineer. What is also defined? Wall thicknesses, slab thicknesses, concrete grade for each element chosen from the list will be applied both in Revit and in Robot. Dimensions for access structures with openings are also defined in the Excel file.

      The same approach is used to define top chamber elements parameters. We can also define [INAUDIBLE] dimensions for each structural element and the soil layers by starting and ending level of each of them and groundwater level based on the weight. The ground pressure is calculated-- also the groundwater pressure

      Furthermore, we can define elasticity of the ground, which will be used for applying the supports. In this case, we can define up to five soil layers.

      Next logs defined in the Excel file are connected with the structural analyzer software such as [INAUDIBLE].

      For example, [INAUDIBLE] concrete for each chamber, temperature, including a maximum gradient, minimum gradient, heating, and cooling temperature. Shrinkage as an [INAUDIBLE].

      [INAUDIBLE] factor for further structural analysis. We can also define [INAUDIBLE] with proper label, nature, and then. The next advantage of this workflow is definition of load combinations for ultimate limit state, serviceability limits state, and others. By assigning proper load factor, we can adjust the calculation to understand that we want.

      As [INAUDIBLE] mentioned before, the whole workflow consists of a few Dynamo scripts. To create the BIM model, we need to open the first one related to Revit.

      The script is linked to the Excel file with input data. We only need to choose the number of substructure, which model we want to create for. Names of worksheets from where we need to extract data for this task are listed-- also variables, segregated, prepared for the programming process and geometric creation.

      Due to clarity, the transparency [INAUDIBLE] divided to sections where we cover aspects of [INAUDIBLE] concrete creation, incoming and outgoing tunnel openings, wall and slab types creation in Revit.

      Next parts are related to outline geometry in Dynamo. All these necessary point for further creation of elements in Revit. This task is done with the Python [INAUDIBLE]. We can find the defined functions to access Revit API.

      Through hundreds of lines of code, we set up the complete Revit model with thicknesses, materials, beam parameters if they are predefined. This approach is used for structural elements like walls and slabs.

      Now, let's run the script. With one click after a few seconds, we have precise model of the structure. Each element is marked.

      You can be sure about correctness of the elements, which could be very difficult to create with precision like this, such as incoming and outgoing tunnel openings, exactly defined diameter level and slope, shape of the branching concrete with slopes in each direction, parallel and perpendicular to the tunnel.

      Creation of this element in classic, manual way would take many hours, and this range of accuracy would be difficult to achieve. The important part of the Revit [? rated ?] script is creating the analytical model with Python script assigning spring supports to panels, which reflects the cooperation of the structure with the ground.

      We are also adjusting connections between the panels, including openings, which are placed in line with center of the walls.

      The BIM model can be exported to ACIS file format and saved on BIM 360 platform for CFD analysis. This procedure allows real time feedback delivered by hydraulic engineers. Furthermore, simplifies tracking of any changes and reduces the risk of inaccuracies.

      After receiving any comments, it's easy-- just with one click-- to remodel the structure before moving forward to structural analysis stage.

      To create a Robot structural analysis model [INAUDIBLE] before Revit model, we need to open a second Dynamo script.

      The approach is similar. Script is linked to Excel with input data. We [INAUDIBLE] need to choose a number of shafts structure, which model you want to create for. Names of worksheets from where we need to extract data for this task are listed.

      Name of the elements from Revit are mapped with the panels, which we will create in Robot. And the main node in Dynamo responsible for the whole task is Python [INAUDIBLE]. We are using Python programming to access software API, and the script covers creation of panels for each structural element, such as walls and slabs with definition of spring supports where it's needed.

      Creation of load cases, load combinations, which are predefined in the Excel file, and finally, application of all loads.

      Let's run the script. And for design reasons, the model has been divided into two parts, bottom and top chamber. Now, we can see the model of the bottom chamber. Additional script is responsible for the second part of the structure.

      And in the model, we have spring supports related to the soil layers, element's weaknesses correlated with Revit model, and [INAUDIBLE] the structure dimensions defined in the Excel file.

      The load cases-- we've applied the loads like ground pressure and the static load.

      Most of them are very time consuming during [INAUDIBLE] process of modeling. And we've got ready load combinations with load factors predefined in the Excel file.

      One of the important parts of this workflow is seismic load application. In case of underground structure like this, we are following [INAUDIBLE] approach. We are defining direction of the load as the angle from the north, then basing on seismic parameters [INAUDIBLE] ground movement is calculated.

      The load assigned the Excel to previously defined load cases in Robot structural analysis. And after clicking the button, the load is generated in the software, the application developed in VBA in Excel.

      The movement is calculated-- actually, recalculated to [INAUDIBLE] forces taking into account spring support elasticity. It takes a while because we are now recalculating and generating 1,000 of nodal forces. Doing this task manually, in my point of view, is impossible.

      And now, after choosing [INAUDIBLE] where the seismic load was assigned, we can see the nodal forces applied in the center of finite elements with the value changing along the height of the structure.

      With the load combinations, there are included two load cases, assuming the angle difference of 90 degrees. And this solution is very useful when changing the meshing dimensions. When decreasing or increasing finite elements, nodal forces disappear, and we end the application again.

      To automate the process of production of the structural analysis report, there is another script in the package [INAUDIBLE] defined template, such as 3D views with annotated panel thicknesses, mesh, [INAUDIBLE] load cases and load combinations, [INAUDIBLE] forces maps, and forces envelopes.

      We can reduce time-- therefore, costs, of course-- from 12 hours to one hour. [INAUDIBLE] it's very important to maintain the consistency of the present results for all structures.

      To insure the flow of information between the BIM model and structural analysis model, we develop the script which extracts the required reinforcement area on the center of finite element and the area. The next step calculates the volume for each panel and finally calculates the rate of reinforcement and creates shared parameter intervals to host this information for each element corresponding to a panel in Robot.

      The last part of the workflow is drawing production. You can produce, with one click, 3D views, plan views, and cross-sections, place them on the sheets, and drawings prepared this way are ready to annotate and print. And now let's focus on the conclusion with Gary.

      GARY FURPHY: Thank you, Wojceich, for that fascinating look into how we've developed this solution. Now, let's look at the investment in creating a solution like this. And note that in our case, the development has not stopped. We're building additional capability as we move forward.

      But for this solution in particular, if you look at the investment, we're talking roughly 1,200 hours to prototype, test, fail. And failure is always an option when you're doing something completely new. Then, we needed to validate and run the solution on one project. If we compare this to the traditional spend of approximately 3,400 hours, we can see that we are already more efficient, and we've made our investment back.

      And this is where it becomes really powerful. We then compare this to future spends on future projects where we're not starting from scratch, where we're just adding and developing on the existing solution, taking into account maybe local codes or various influences in different parts of the world.

      We may be looking at 600 hours on a project of similar size. This is less than 20% of the traditional approach hours. That's a very, very good value proposition.

      I'm just going to bring up a graph here, and this can show you. On a project where you're designing, in this case, 12 structures, by the time we have designed a third structure in automated approach, we've already broke even.

      So let's look at some of the benefits, disadvantages, and advantages. So some things you need to consider-- the initial development time. If you're going to do that while you have a project that's going on, you're not going to see any project progress while you're developing a solution because you're going to be creating that solution, prototyping, testing if it works or not. Your project team needs to be aware of that.

      Then, depending on the project particulars, it can be quite difficult to estimate the amount of time something's going to take. So if you have a new variation introduced-- so for example, originally, we were not going to do seismic loads for this solution. We decided we would because we are happy with the progress we were making in other parts of it.

      Or, for example, our initial thought on one project was to use baffle drop shafts, and then there was a change to vortex drop shafts. So we had to go back, re-prototype, test, validate, and make sure it worked.

      And then, of course, with any solution, you need to have a budget and a support team to be assigned. As the tools, our solution interacts with changes-- requires constant updates and debugging. So for example, versions of Revit or Excel or Robot structural analysis change, we will need to update this solution and test that it works with newer versions.

      Also, people's skill set. This is really key. Without people who understand both the engineering-- Python code, API calls, Robot, Revit, Dynamo-- you're not going anywhere. The number of people who can do this in world is quite finite.

      You need to find somebody who's excited about both the engineering aspect, the coding aspect, and working with these tools, and then you need to convince them to give up their time from their traditional project workload to work on something like this. Thankfully, we have those people here, and it's quite excited to see how far they've stretched the solution beyond what we originally thought was possible.

      And then, trust in engineering solutions. It can be quite difficult with many project managers and engineers to convince them that this works, especially when you're starting from scratch and you don't have a project in the past to say, well, we used it on these five projects, and it worked. So it's a hard sell. Therefore, it's critical that you have the world's leading [INAUDIBLE] experts as a stakeholder on your projects to help you get over that hump.

      And if we look at the advantages, we've significantly reduced the effort required to design shafts, and it's a big saving for our business. And we can be more competitive on bids. And it's a real value proposition in the sales stage as well.

      We have a reduction in errors because it's a single source of data. It might be a large source, but at least in multiple outputs, and we gain certainty in much earlier stages of projects. So at a traditional concept phase, we can produce pretty much detailed design and really test those assumptions and gain certainty that something is going to work in reality.

      And then if we look at the building information modeling, if we keep it at a stage that directly as it comes out of the solution without annotation, as things change in the project if we need to move shafts or change sizes of shops, we can model that without losing any time. There's very minimal input required to change size [INAUDIBLE] locations, and we can be extremely agile.

      The quality and consistency of this delivery is very high, so we're reducing the amount of technical checking required and enabling a higher focus in the pure engineering validation. So the benefit is clear to us.

      For a single set of engineering inputs, recreate multiple sets of deliverables and building information model, drawings sets, structural analysis, CFD, seismic load calculations, reinforcement quantity, calculation reports, force maps. In many ways, we've created digital twins or multiple sets of digital twins, and that's a really strong case for doing engineering automation like this.

      Thank you for your time, and thank you to Wojceich and Emmanuel.

      ______
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      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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      Improve your experience – allows us to show you what is relevant to you

      Google Optimize
      We use Google Optimize to test new features on our sites and customize your experience of these features. To do this, we collect behavioral data while you’re on our sites. This data may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, your IP address or device ID, your Autodesk ID, and others. You may experience a different version of our sites based on feature testing, or view personalized content based on your visitor attributes. Google Optimize Privacy Policy
      ClickTale
      We use ClickTale to better understand where you may encounter difficulties with our sites. We use session recording to help us see how you interact with our sites, including any elements on our pages. Your Personally Identifiable Information is masked and is not collected. ClickTale Privacy Policy
      OneSignal
      We use OneSignal to deploy digital advertising on sites supported by OneSignal. Ads are based on both OneSignal 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 OneSignal has collected from you. We use the data that we provide to OneSignal to better customize your digital advertising experience and present you with more relevant ads. OneSignal Privacy Policy
      Optimizely
      We use Optimizely to test new features on our sites and customize your experience of these features. To do this, we collect behavioral data while you’re on our sites. This data may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, your IP address or device ID, your Autodesk ID, and others. You may experience a different version of our sites based on feature testing, or view personalized content based on your visitor attributes. Optimizely Privacy Policy
      Amplitude
      We use Amplitude to test new features on our sites and customize your experience of these features. To do this, we collect behavioral data while you’re on our sites. This data may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, your IP address or device ID, your Autodesk ID, and others. You may experience a different version of our sites based on feature testing, or view personalized content based on your visitor attributes. Amplitude Privacy Policy
      Snowplow
      We use Snowplow to collect data about your behavior on our sites. This may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, your IP address or device ID, and your Autodesk ID. We use this data to measure our site performance and evaluate the ease of your online experience, so we can enhance our features. We also use advanced analytics methods to optimize your experience with email, customer support, and sales. Snowplow Privacy Policy
      UserVoice
      We use UserVoice to collect data about your behaviour on our sites. This may include pages you’ve visited. We use this data to measure our site performance and evaluate the ease of your online experience, so we can enhance our platform to provide the most relevant content. This allows us to enhance your overall user experience. UserVoice Privacy Policy
      Clearbit
      Clearbit allows real-time data enrichment to provide a personalized and relevant experience to our customers. 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.Clearbit Privacy Policy
      YouTube
      YouTube is a video sharing platform which allows users to view and share embedded videos on our websites. YouTube provides viewership metrics on video performance. YouTube Privacy Policy

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      Customize your advertising – permits us to offer targeted advertising to you

      Adobe Analytics
      We use Adobe Analytics to collect data about your behavior on our sites. This may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, your IP address or device ID, and your Autodesk ID. We use this data to measure our site performance and evaluate the ease of your online experience, so we can enhance our features. We also use advanced analytics methods to optimize your experience with email, customer support, and sales. Adobe Analytics Privacy Policy
      Google Analytics (Web Analytics)
      We use Google Analytics (Web Analytics) to collect data about your behavior on our sites. This 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. We use this data to measure our site performance and evaluate the ease of your online experience, so we can enhance our features. We also use advanced analytics methods to optimize your experience with email, customer support, and sales. Google Analytics (Web Analytics) Privacy Policy
      AdWords
      We use AdWords to deploy digital advertising on sites supported by AdWords. Ads are based on both AdWords 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 AdWords has collected from you. We use the data that we provide to AdWords to better customize your digital advertising experience and present you with more relevant ads. AdWords Privacy Policy
      Marketo
      We use Marketo to send you more timely and relevant email content. To do this, we collect data about your online behavior and your interaction with the emails we send. Data collected may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, your IP address or device ID, email open rates, links clicked, and others. We may combine this data with data collected from other sources to offer you improved sales or customer service experiences, as well as more relevant content based on advanced analytics processing. Marketo Privacy Policy
      Doubleclick
      We use Doubleclick to deploy digital advertising on sites supported by Doubleclick. Ads are based on both Doubleclick 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 Doubleclick has collected from you. We use the data that we provide to Doubleclick to better customize your digital advertising experience and present you with more relevant ads. Doubleclick Privacy Policy
      HubSpot
      We use HubSpot to send you more timely and relevant email content. To do this, we collect data about your online behavior and your interaction with the emails we send. Data collected may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, your IP address or device ID, email open rates, links clicked, and others. HubSpot Privacy Policy
      Twitter
      We use Twitter to deploy digital advertising on sites supported by Twitter. Ads are based on both Twitter 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 Twitter has collected from you. We use the data that we provide to Twitter to better customize your digital advertising experience and present you with more relevant ads. Twitter Privacy Policy
      Facebook
      We use Facebook to deploy digital advertising on sites supported by Facebook. Ads are based on both Facebook 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 Facebook has collected from you. We use the data that we provide to Facebook to better customize your digital advertising experience and present you with more relevant ads. Facebook Privacy Policy
      LinkedIn
      We use LinkedIn to deploy digital advertising on sites supported by LinkedIn. Ads are based on both LinkedIn 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 LinkedIn has collected from you. We use the data that we provide to LinkedIn to better customize your digital advertising experience and present you with more relevant ads. LinkedIn Privacy Policy
      Yahoo! Japan
      We use Yahoo! Japan to deploy digital advertising on sites supported by Yahoo! Japan. Ads are based on both Yahoo! Japan 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 Yahoo! Japan has collected from you. We use the data that we provide to Yahoo! Japan to better customize your digital advertising experience and present you with more relevant ads. Yahoo! Japan Privacy Policy
      Naver
      We use Naver to deploy digital advertising on sites supported by Naver. Ads are based on both Naver 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 Naver has collected from you. We use the data that we provide to Naver to better customize your digital advertising experience and present you with more relevant ads. Naver Privacy Policy
      Quantcast
      We use Quantcast to deploy digital advertising on sites supported by Quantcast. Ads are based on both Quantcast 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 Quantcast has collected from you. We use the data that we provide to Quantcast to better customize your digital advertising experience and present you with more relevant ads. Quantcast Privacy Policy
      Call Tracking
      We use Call Tracking to provide customized phone numbers for our campaigns. This gives you faster access to our agents and helps us more accurately evaluate our performance. We may collect data about your behavior on our sites based on the phone number provided. Call Tracking Privacy Policy
      Wunderkind
      We use Wunderkind to deploy digital advertising on sites supported by Wunderkind. Ads are based on both Wunderkind 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 Wunderkind has collected from you. We use the data that we provide to Wunderkind to better customize your digital advertising experience and present you with more relevant ads. Wunderkind Privacy Policy
      ADC Media
      We use ADC Media to deploy digital advertising on sites supported by ADC Media. Ads are based on both ADC Media 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 ADC Media has collected from you. We use the data that we provide to ADC Media to better customize your digital advertising experience and present you with more relevant ads. ADC Media Privacy Policy
      AgrantSEM
      We use AgrantSEM to deploy digital advertising on sites supported by AgrantSEM. Ads are based on both AgrantSEM 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 AgrantSEM has collected from you. We use the data that we provide to AgrantSEM to better customize your digital advertising experience and present you with more relevant ads. AgrantSEM Privacy Policy
      Bidtellect
      We use Bidtellect to deploy digital advertising on sites supported by Bidtellect. Ads are based on both Bidtellect 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 Bidtellect has collected from you. We use the data that we provide to Bidtellect to better customize your digital advertising experience and present you with more relevant ads. Bidtellect Privacy Policy
      Bing
      We use Bing to deploy digital advertising on sites supported by Bing. Ads are based on both Bing 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 Bing has collected from you. We use the data that we provide to Bing to better customize your digital advertising experience and present you with more relevant ads. Bing Privacy Policy
      G2Crowd
      We use G2Crowd to deploy digital advertising on sites supported by G2Crowd. Ads are based on both G2Crowd 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 G2Crowd has collected from you. We use the data that we provide to G2Crowd to better customize your digital advertising experience and present you with more relevant ads. G2Crowd Privacy Policy
      NMPI Display
      We use NMPI Display to deploy digital advertising on sites supported by NMPI Display. Ads are based on both NMPI Display 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 NMPI Display has collected from you. We use the data that we provide to NMPI Display to better customize your digital advertising experience and present you with more relevant ads. NMPI Display Privacy Policy
      VK
      We use VK to deploy digital advertising on sites supported by VK. Ads are based on both VK 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 VK has collected from you. We use the data that we provide to VK to better customize your digital advertising experience and present you with more relevant ads. VK Privacy Policy
      Adobe Target
      We use Adobe Target to test new features on our sites and customize your experience of these features. To do this, we collect behavioral data while you’re on our sites. This data may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, your IP address or device ID, your Autodesk ID, and others. You may experience a different version of our sites based on feature testing, or view personalized content based on your visitor attributes. Adobe Target Privacy Policy
      Google Analytics (Advertising)
      We use Google Analytics (Advertising) to deploy digital advertising on sites supported by Google Analytics (Advertising). Ads are based on both Google Analytics (Advertising) 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 Google Analytics (Advertising) has collected from you. We use the data that we provide to Google Analytics (Advertising) to better customize your digital advertising experience and present you with more relevant ads. Google Analytics (Advertising) Privacy Policy
      Trendkite
      We use Trendkite to deploy digital advertising on sites supported by Trendkite. Ads are based on both Trendkite 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 Trendkite has collected from you. We use the data that we provide to Trendkite to better customize your digital advertising experience and present you with more relevant ads. Trendkite Privacy Policy
      Hotjar
      We use Hotjar to deploy digital advertising on sites supported by Hotjar. Ads are based on both Hotjar 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 Hotjar has collected from you. We use the data that we provide to Hotjar to better customize your digital advertising experience and present you with more relevant ads. Hotjar Privacy Policy
      6 Sense
      We use 6 Sense to deploy digital advertising on sites supported by 6 Sense. Ads are based on both 6 Sense 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 6 Sense has collected from you. We use the data that we provide to 6 Sense to better customize your digital advertising experience and present you with more relevant ads. 6 Sense Privacy Policy
      Terminus
      We use Terminus to deploy digital advertising on sites supported by Terminus. Ads are based on both Terminus 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 Terminus has collected from you. We use the data that we provide to Terminus to better customize your digital advertising experience and present you with more relevant ads. Terminus Privacy Policy
      StackAdapt
      We use StackAdapt to deploy digital advertising on sites supported by StackAdapt. Ads are based on both StackAdapt 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 StackAdapt has collected from you. We use the data that we provide to StackAdapt to better customize your digital advertising experience and present you with more relevant ads. StackAdapt Privacy Policy
      The Trade Desk
      We use The Trade Desk to deploy digital advertising on sites supported by The Trade Desk. Ads are based on both The Trade Desk 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 The Trade Desk has collected from you. We use the data that we provide to The Trade Desk to better customize your digital advertising experience and present you with more relevant ads. The Trade Desk Privacy Policy
      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

      Are you sure you want a less customized experience?

      We can access your data only if you select "yes" for the categories on the previous screen. This lets us tailor our marketing so that it's more relevant for you. You can change your settings at any time by visiting our privacy statement

      Your experience. Your choice.

      We care about your privacy. The data we collect helps us understand how you use our products, what information you might be interested in, and what we can improve to make your engagement with Autodesk more rewarding.

      May we collect and use your data to tailor your experience?

      Explore the benefits of a customized experience by managing your privacy settings for this site or visit our Privacy Statement to learn more about your options.