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Automated 3D Rebar Shop-Drawing Generation Using Revit: A Method to Achieve Double the Work Efficiency

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Description

This session will explore the tool for automatic generation of 3D rebar shop drawing utilizing Revit. The tool uses parametric modeling to create complicated rebar shop drawings twice as fast as conventional methods. When applied to a diaphragm wall of the subway project, the tool achieved a labor cost reduction of 150,000 USD. In addition, the speeding up of the shop drawing creation process enabled a more rational design with more detailed design cross-sections, and a 10% reduction in rebar material compared to the initial design. To improve the efficiency of the subsequent rebar processing process, trials are underway to link rebar information in Revit with rebar processing machines. In large-scale infrastructure projects, activities related to reinforcement (such as drawing, fabrication, and assembly) significantly impact the schedules and costs. This tool is expected to contribute to solving human resource issues and to sustainability by reducing material losses.

Key Learnings

  • Learn how to apply parametric modeling to automatically generate 3D shop drawings.
  • Discover how to streamline rebar construction from detailed design to construction using BIM.
  • Estimate the potential for reducing material losses in reinforcement works.

Speakers

  • 香苗 宮岡
    Civil engineer at Shimizu Corporation. Experience includes construction management and design for tunnels and highways, and currently responsible for technology development. Specializes in BIM and develops applications that enhance the efficiency of design and construction processes, primarily using Revit.
  • Avatar for Yuko Ishizu
    Yuko Ishizu
    Yuko Ishizu is the representative of GEL and is based in Tokyo, Japan. She specializes in computational design within the fields of architecture and construction, dedicating herself to the development of computational tools that enhance design and construction processes. After graduating from Kobe University with a Master's degree in Architecture, she was honored with a Swiss government scholarship and completed the MAS CAAD program at ETH Zurich. Upon her return to Japan, Yuko embarked on her freelance career, gaining invaluable experience in the Design Department of Takenaka Corporation and in Design Tech at WeWork. Before founding GEL, she amassed expertise in developing plugins related to BIM and designing computational tools that span a broad spectrum, from design to structure, facilities, and civil engineering. She is meticulous about the application of technology, ensuring that lighter tasks receive nimble solutions while more complex challenges are addressed with robust tools. She undertakes programming for a diverse range of projects, from web apps to iOS, add-on development, and graphical coding. She is the co-author of 'Parametric Design with Grasshopper' and the author of 'Computational BIM with Revit + Dynamo'. Beyond her professional endeavors, Yuko has a rich background in education, having imparted knowledge at both corporate and academic institutions. Additionally, she serves as a board member of the Architectural Information Society.
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Transcript

KANAE MIYAOKA: So let's start our presentation. Thank you for coming to our session. We will be presenting on Automated 3D Rebar Shop-Drawing Generation Using Revit, a Method to Achieve Double the Work Efficiency. Let me first introduce about us. My name is Kanae Miyaoka from Shimizu Corporation. And my co-speaker is Yuko Ishizu from GEL company.

I am a civil engineer for Shimizu corporation, which is a Japanese construction company. We design and construct a wide range of structures from historical buildings to highrise buildings and infrastructure. Currently, I'm working at HQ in Tokyo, providing beam support service for projects in Japan and overseas. So please introduce yourself, Yuku Ishizu.

YUKU ISHIZU: Let me introduce myself. My name is Yuko Ishizu. And I'll be present to you Outburst based in Tokyo, Japan. My work focuses on computational design with architecture, construction, and civil engineering. I'm fascinated about developing computational tools that enhance design and construction processes. I'm focusing on applying technology in the right way, ensure that simple tasks are solved in lightweight solution, while more complex challenges get addressed with robust tools.

I handle programming across many areas, including web apps, iOS development, add-ons, and graphical coding. I also coauthor of the book Parametric Design with Grasshopper and wrote Computational Beam with Revit and Dynamo in Japanese.

In addition to my professional work, I have an educational background teaching at corporate and academic institutions. I also served as a board member of architecture information society in Japan.

KANAE MIYAOKA: So this is the agenda for this session. This session includes two parts. The first one is an overview of the development processes, including background and objectives, overview of development, on-site implementation, and its effect. And the second part details the development techniques, especially the steps in using tools, how the application was selected, and the details of the program and technical innovations.

Now let's begin the first part, development overview. Firstly, let me explain the background and objectives of the development. This is a view of assembly, the assembly of rebar for our subway construction project. In large scale infrastructures, rebar works such as drawing, manufacturing, and assembly, have a significant impact on the project schedule and cost.

Particular is the amount of 3D shop drawing is huge. And a lot of time and effort is devoted to creating, checking, and devising these drawings. Shop drawings are frequently revised as construction plans change. Each time this happens, short-term builds, section builds, and the pervading schedule must be revised. As a result, errors are likely to occur.

Our project have shown that more than 40% of design manpower was required for the work.

A good way to improve this task is to implement 3D shop drawings. However, creating 3D shop drawing tasks are a lot of the time. 3D shop drawings takes a lot of time even when created by highly skilled BIM operator familiar with rebar modeling. Additionally, errors can easily occur because the model can be very complicated. These are some of the barriers to implementing 3D shop drawing.

To solve this issue, we develop the automated 3D rebar shop drawing generation tool using Revit.

Here I will explain the overview of the development. This is a step in creation of a conventional 2D based shop drawing.

Here I will explain the overview of the development. This is a step in creation of a conventional 2D based shop drawings. The engineer gives instructions to create a shop drawing based on the information from the basic and detailed design studies. These instructions are often given orally or by hand on drawings, which can be burdensome for CAD operators and can also lead miscommunication. Therefore, we propose the route shown here. Also the development of the tool we used line inside the Revit and the Grasshopper.

So the important thing is to put the information from the detailed design into parameter data. By organizing labor information as parameter data and loading it into the program on Grasshopper, 3D shop drawings can be easily created in Revit.

This I will explain the development features. As you know, there are tools that support the creation of 3D models of the existing rebar, such as Revit, Rebar Functions, and SOFiSTiK. By using these tools, it is easy to create regular arrangement of labor as seen in basic design. However, shop drawings require irregular placement of rebar and the various type of shapes of rebar, according to the rules of each project.

So when we creating shop drawings, the applicability of these existing functions is limited. And eventually a lot of manual modeling is required. On the other hand, the developed tool organize a complex arrangement of labor found in shop drawings. Therefore, also it is takes more time to create parameter data. The complex model can be easily create in a very short time.

Now I will explain the parameter data, which is a key point of this development. This is the example of diaphragm wall. Parameter items are determined by the rules of rebar placement. The entire frame wall contains five types of rebar-- main bar, distribution bar, shear links, starter bar, and stiffener.

Since the placement rules are different for each type of rebar, the created the format for the parameter data for after organizing each rules. Here is the format of parameter data for diaphragm wall. The first lane-- name, face. z offset, y offset, is a parameter data. The user enters the new parameter information of the rebar for each information items.

I will explain each parameter items. First is shape. Shape is represent the rebar shape. The shape code is basically the permitted number of rebar shapes that is included in Revit. If we need to model a shape that is not included in Revit, we create an original rebar shape family. Those are letter A, B, C, D, like so, and tells the dimension by values defined by each rebar shape.

Next one is face and the offset. This is very important to locate. It's important to locate where the rebar will be generated. Face, the present face of the wall. In this development, the outside of the wall was set as number one, and the inside of the wall to be excavated is set as number two.

And X, Y, Z offset represents the distance between the starting position of the rebar and the origin. The origin point and the X, Y, Z settings are areas that we-- we means developers-- are free to determine. But we decided on them for ease of understanding for the users.

So next one is axis information. Axis parameter is set to indicates the direction of rebar bending As example, it's showing here in here, by setting the axis, it is possible to control the direction in which the rebar bends.

Finally, the rebar is generated in Revit by importing these parameters organized in this way into the program. So details of program and the in technical innovations will be presented in the next technical details part.

Now let's talk about on-site implementation. The developmental tool is being used in an actual project. The target site is a subway construction project called CP202 in Jakarta. This is a construction project to build three large subway station buildings and a subway between the stations. This subway runs North/South in center of Jakarta.

Here is a perspective of the completed three stations. The implementation was done for the diaphragm wall of three subway stations. The construction of the diaphragm wall was divided into a number of construction blocks called panel. The total construction quantity for the three subway station was number of tunnels is 382. And the concrete volume is 92,600 cubic meters. And the rebar volume is 13,800 tons.

So here I show you the workflow for on-site implementation. Before actual implementation,a A test trial of the developer tool was conducted with members of the on-site beam team who would be the users. As a result, we found it is difficult to create parameter data directory from the 2D technical drawings and the detailed design information. So we decided to create a rebar demarcation sheet as a step before creating parameter data.

This is a sample drawing that shows the shape of the rebar, its placement, and the location of the joint. Also, this creation is done by engineer. This rebar demarcation sheet allows even a person with no engineering knowledge to create parameter data.

After these macro improvements, modeling of diagram wall was performed using the developed tool. The conventional 2D-based drawings required three days for one panel. On the other hand, with the proposed method also the time was required to create a demarcation sheet and Create parameter data. But 3D modeling itself could be easily done in a short time. And the work time was completed in 1.5 days in total, including the time to create 2D drawings from the 3D model. This means in creation of construction drawings at twice the speed of conventional method.

As more additional effect, the traditional method requires a lot of time to make and review shop drawings. This often leads to delays with drawings not ready when construction starts and errors being found during construction causing rework.

A new tool reduces the time needed to create drawings. It allows for more detailed structural analysis without time constraints. By consulting just station structures and ground conditions, we develop efficient designs that reduce the amount of labor compared to the initial program. Additionally, parametric modeling improved the quality of the drawings.

Currently, we are able to proceed smoothly with the construction of three station buildings at the same time without any errors in the drawings. This is truly an example of front loading of BIM.

So now finish the first part. Let's move on to the next part.

YUKU ISHIZU: Now, let me dive in deeper into our more technical program explanation. In this project, retaining was of varying dimensions, but with the same structural configuration are used in the subway construction. This creates a need for a tool that can efficiently and accurately place a rebar. By developing a parametric model, we can streamline the process, enabling the tool to handle walls with different dimensions while maintaining consistency in the overall structure.

It automatically generates the standard component of main rebar, distribution rebar, starter bar stiffener bar, and shearlink while excluding individually different parametric models. The rebar is produced using native families, considering the possibility of adding manual adjustment later.

This program is a tool that generates rebar in Revit using RhinoInside.Revit. It needs a utilized information from CSV file that contain rebar details. The key aspect of automated generation using CSV file is ensuring the correct creation of the CSV data. To address this, the program includes a visualization feature that allows Rhino to quickly depict the rebar information, enabling you to verify the data before generating it in Revit. This functionality facilitate an interlocked workflow where you can edit the CSV and check the model ensuring accuracy and efficiency.

To use this tool, these three steps to generate rebars. First, enter all the necessary rebar information into CSV file, such as rebar type, shape, spacing, and count. Second, select the wall in Revit and load the CSV data into Rhino through Grasshopper using RhinoInside.Revit. In Rhino, you can visually confirm the rebar placement to ensure everything is correct. Third, once confirmed, instantiate rebar element in Revit and incorporate them into the detailed design and construction processes.

In step one, the user creates these three files, like those shown in this image. As mentioned earlier, the rebar's base point and plane are determined by wall space along with x offset, y offset that offset main axis and sub axis. The rebar types and shape are specified using shape and diameter columns. To alloy the rebar, we use spacing and count information and the shape parameter are determined with values for A, B, C, D, E, and G to specify the shape fully.

In second step, we load the data that was prepared in step one for each type of rebar. main rebar, distribution bar, startup bar, stiffener, and shearlinks. A separated CSV file is prepared. As you can see in the image, we load the data into Grasshopper. And from the Grasshopper UI, you can select visualize this desired CSV file that allow you to easily check and confirm the rebar configuration.

In the final step three, you can instantiate the rebar in Revit. With just the push of a button, the rebar is generated. If you have multiple walls with the same rebar configuration, you can easily create them by selecting each wall individually. Since the rebar is created using native Revit families, you can also use it to easy to make manual changes later if you need.

So why did we choose RhinoInside.Revit over Dynamo? Let me explain the key reasons. First, visual view functionality. Line I said, Revit allow us to visually check and manipulate the geometry directly in Rhino before finalizing the rebar placement. Unlike Dynamo, which requires a full integration with Revit to visualize changes, RhinoInside.Revit let us selectively export only the necessary parts to the model to Rhino for viewing the adjustment. This makes it more flexible too, especially when dealing with complex geometry or rebar layout as we can perform intermediate check efficiently.

Second, customization with Layer and UserText. While Dynamo supports parametric modeling, RhinoInside.Revit offers greater flexibility for manual input and customization using Layer and UserText features. This makes it easy to assign parameter and metadata to geometry, which crucial for fine tuning and management rebar placement. While Dynamo is structured along node-based approach, Rhino provide more intuitive control over the geometry and its associated parameters.

Third, superior geometry engine. Rhino geometry engine is more robust when handling complex and freeform geometries, making it ideal for projects involving intricate shapes like station retaining walls. While Dynamo is powerful for parametric workflow, it's not strong as strong when handling irregular geometry with same precision and adaptability as Rhino. This makes especially important when working with rebar in irregular shapes or parametric retaining walls.

Now, let me walk you through the technical details of the program. Now, let me explain the benefit of using RhinoInside.Revit with Python for rebar tools. First, convenience of Python. When developing various tools using RhinoInside, I often need a specific component such as rebar tools that aren't readily available by default. In these cases I rely on Revit API to write custom code. I prefer using Python over C# because Python allows me to avoid the need to reconfigure DLLs when Revit or RhinoInside.Revit version changes. This saves much time and reduce complexity.

However, there are challenges with version changes. Since I'm working with both RhinoInside.Revit and Revit API, there are frequent changes to keep up with. For example, with the release Revit 2023, the methods to access specific properties like labor diameter has changed. This means that I have to rewrite part of my code to ensure compatibility, and doing that in Grasshopper Python script can be quite tedious.

Another challenge is maintaining distributed Grasshopper files. When API changes cause tool to break, updating and distributing Grasshopper files can be difficult. User often modify these files. And when things break, it can take much time to track down and fix components that are causing issues.

So what is the solution? To address these problems, I've created a Python library and distribution installer. This solution makes it much easier to update and distribute the tool without handling, go through the hassle of manually updating every Grasshopper file. This also ensures that everyone is working with the latest version of the tool.

I'm implementing a feature that generate rebar bars from curve data in Rhino. Let me walk you through generating rebar from CSV data using RhinoInside. By the leveraging the necessary Python function.

These are two types of rebars, those using RebarShape and those that don't. Rebars without a shape are known as free form rebars.

For generating rebar with RebarShape, these are three essential methods that we can use. CreateFromCurves. This method generate rebar from the curve, which represents the center line of the rebar. The RebarShape is automatically determined by the system. If suitable RebarShape does not already exist in the project, a new one will be created automatically.

CreateFromCarvesAndShapes. This method also generates rebar from the curve but with a specific RebarShape. If a match shape isn't found in the project, the rebar will not be generated.

CreateFromRebarShape. This method generate rebar based purely on the RebarShape rather than using center. Instead of being based on calves, the rebar is generated using the bounding box of the shape.

Now, an important note. When we're using any of these methods, the generation location of the rebar can become complex, depends on the shape and diameter of the rebar. To simplify process generating rebar from CSV, we use CreateFromCurvesAndShape method. This approach generate rebar based on centerline, streamline the workflow.

Additionally, I've created functionality that generate curves from each RebarShape in Rhino. By mapping the shape between Rhino and Revit, I can now generate corresponding curves in Rhino by specifying the shape from Revit

We use Python to draw the center line of a Revit Shape directly in Rhino. This functionality allow us to accurately. replicate the center line geometry from Revit and visualize it within Rhino environment. By doing this, we can create mapping information that links the RevitShape and RhinoShape. This ensures consistency between the platform and allow for a seamless transition when working on rebar method in both Rhino and Revit.

The CSV file is used within the rebar tool to consolidate the information related to rebar shape. It summarize which RebarShape in Revit corresponds to which center type in Rhino when generating a central line geometry. By linking this RebarShape and central line types, we can ensure that rebar generated in Rhino matches an exact specification required in Revit, streamlining the entire rebar modeling process between two platforms.

Here is description of key columns in the RebarShape CSV file. First name, the name of shape in Revit. The second image, a link to an image file representing the RebarShape. These images provide a visual reference for the shape of dimension parameters.

Third, Revit family. The Revit family files was associated with RebarShape. This field includes a link, the specific Revit family files. Style, describe the style of the shape.

For HookAtStart and HookAtEnd, The hook angle at the start of the rebar is usually specified in degree, for example, 90 degree, 180 degree. If none hook is present, this value is typically set to 0.

HookOrientation. The orientation of the hook start and end rebar. Option, like right or left indicate the direction of the hook.

Links parameters, refer to the parameter that control the length of the rebar. This typically a dimension parameter label with letters such as A, B, C. For the length.

Rhino based line type. Describe the central line used in Rhino to generate the rebar. This would typically refer a specific line type such as rg01.

So let's take an example of RevitShape generation based on RhinoShape. In this case, straight line geometry, rg01, which is defining by the dimension parameter a. We can generate the corresponding RevitShape from this rg01 geometry by incorporating additional information such as HookAtStart, HookAtEnd, HookOrientation0, and HookOrientation1.

Some examples of the RebarShape generated this way include the RebarShape. 00, 01, 02, 03, 04, 05, 19, and 20. It's important to note that even though the center line might be the same, the final RevitShape will vary different, depends on the hook information.

By organizing RevitShape based on linear geometry types, we've made it possible to visualize all necessary RebarShape directly in Rhino. This ensures consistency between both platforms and allow us to work seamlessly across Rhino and Revit without losing any important RebarShape information.

Now that we've organizing in RebarShape and ensure that they can be fully visualized in Rhino, let's move on how we tackle the challenges related to a version control and Grasshopper file management.

This solution we developed was creating a Python library and distribution installer. This greatly simplified updates and distribution process. With this solution, user can continue using the same Grasshopper files even when I need to debug or improve features. This minimizes interruptions and reduce user frustration, making the workflow smoother and more efficient for everyone involved.

Here is a comparison of Grasshopper data before and after the improvement. As you can see before standardization, Grasshopper files were more complex and harder to manage. After the improvement by implementing standardized rules for rebar generation, we were able to simplify the structure significantly. This made this file cleaner and more efficient, streamlined the entire process. Not only did this clean up process reduce complexity, but it also greatly improved usability for the users. Now users can work with rebar generation rules that are both more intuitive and efficient.

In conclusion, by streamlining the Grasshopper files and standardizing rebar generation rules, we've not only simplified the process, but also made it far more user friendly and efficient. This ensures that the user can continue to work effectively while maintaining the flexibility needed for the future update

Thank you for the attention. And I'm happy to answer any question you might have. Thank you.

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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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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

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