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Scaling BIM for Resilience: Automated Designs to Retrofit Informal Housing

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By 2030 more than 3 billion people will be living in substandard housing conditions. This means that over a third of the global population won’t have access to safe housing. Also, climate change and rapid urbanization are contributing to the increased levels of risk in developing countries. One of the main causes of substandard housing is informal construction, which takes place when low-income families move from rural to urban areas and build their homes without technical guidelines. The result is massive neighborhoods filled with poorly built houses that lack structural components necessary to withstand natural events like earthquakes and windstorms.

view
Panoramic view of one of the informal neighborhoods in Medellin, Colombia.

This article details the results of six years of work focused on preventing deaths from earthquakes in Colombia. In total, the nonprofit Build Change has been working for 15 years in 14 countries saving lives from earthquakes and windstorms. By working together with the ministry of housing, government agencies, and building authorities, Build Change has been an influential leader in the efforts to solve the substandard housing crisis through innovation and technology.

zones
The seismic hazard shown in terms of Ground Peak Acceleration (left). The concentration of buildings in Colombia (right). 

Build Change’s approach for addressing unsafe housing is retrofitting, which essentially is strengthening an existing building that lacks structural integrity. This methodology is the most sustainable and cost efficient because it reduces the social impact of relocation and cuts down costs up to a third of reconstruction. 

Nicolas Abello of the nonprofit Build Change talks about using innovation and technology to help solve the challenge of substandard housing.

National House Improvement Program and Scalability Challenges

How to improve the living conditions of 2 million people?

In response to an imminent seismic risk and to improve lives of over 2 million people, Colombia’s government envisioned a nationwide program to take place on a four-year timeframe. The program “Casa Digna, Vida Digna” aims to improve 600,000 houses from an architectural and structural standpoint. Build Change was appointed as the technical consultant for the Ministry of Housing to design a framework that allows scalability and efficiency of the program. Our support to the national program focused on two areas: structural engineering and process innovation.

First, we had to come up with a structural solution to make poorly-built houses safer, keeping in mind cost efficiency and local construction materials available. Once the engineering was set, we had to solve different challenges involving the bigger picture such as working in spread-out remote locations, lack of qualified labor, and more importantly, the magnitude of the program. In other words, the engineering was not enough to implement “Casa Digna, Vida Digna.” Instead, we had to think of new workflows to ensure the program’s success.

Traditional Workflow of Retrofit Projects

Traditionally, the process of structurally retrofitting a building requires a sequence of steps that can be summarized into four stages: Data Collection, Data Processing, Document Production, and Construction. The workflow starts once a house has been identified as a potential beneficiary and all the requirements for subsidy are met. Once the subsidy procedures are completed, trained staff must visit the house to do an initial assessment and collect data. Then, this data is taken into the office where analysis is performed to define which structural solution should be implemented. Once the retrofit design is defined, engineers and architects assemble a set of construction drawings that must be approved by the building authority to start with the construction activities.

workflow
Traditional retrofitting workflow, from field data collection to construction of the retrofit solution.

To have a better understanding of the whole workflow, each stage is described below:

1. Data Collection

This part of the process takes place in the field, at the house intended to retrofit. It’s done by trained staff, either civil engineers, architects, or professionals instructed in earthquake-resistant construction. In this stage, two types of data are collected: a geometrical survey of the house, which records all the measurements of walls, windows, doors and floors in a hand sketch. Then, the second data type is homeowner information together with seismic site parameters to calculate the vulnerability status.

sketch
An example of a hand sketch recording measurements from an informal house in Bogota, Colombia.

2. Data Processing

This stage includes a vulnerability assessment, followed by a structural analysis which will establish the retrofitting techniques required. They breakdown as follows:

  • Vulnerability Assessment
    The factors that determine the degree of vulnerability of a house include site hazards (such as landslides or floods), seismic parameters intrinsic to the area and the structure configuration.

  • Structural Analysis
    Informal housing lacks a proper design and often are built without fundamental structural elements. Therefore, structural engineers must analyze the structure configuration and the materials used to calculate a seismic demand for the building.

  • Retrofit Proposal
    As a result of combining the vulnerability assessment and the structural analysis of the building, engineers come up with a retrofit proposal. This includes a set of structural elements such as columns, beams, ties, dowels and other solutions to ensure the building won’t collapse during an earthquake.

3. Document Production

The third step of the process is crucial because it wraps everything done until now in a concise package of information known as the Construction Package. In other words, this document portrays a timeline for the house intervention because it shows the initial conditions of the structure, the results of the analysis and what’s needed to make it safer. In detail, this construction package includes:

  • Existing plans of the building, including architectural and structural elements

  • Results from the structural analysis that determine the intervention’s scope

  • Retrofit plans of the building, specifying materials and elements for the intervention

  • Construction details of the retrofit elements

  • Cost estimate or Bill of Quantities (BOQ)

4. Construction

Once the construction package is approved by the local building authority, the project has a green light to begin construction works. The retrofit solution includes the construction of new elements such as columns, beams, or jacketing, and also modification of existing walls, slabs, and existing structural components.

Bottlenecks and Limitations for Scalability

The previous retrofitting workflow has been implemented by Build Change in seven countries. However, it has never been used in a large-scale national development. With this in mind, to effectively implement a nationwide program for retrofitting, this workflow had to be reassessed from a cost and time perspective. The image below shows a map of the workflow, focusing on processing time and information exchange.

mapping
Mapping of the existing workflow, focusing on time and resources spent on each activity.

A systems-based analysis of the traditional workflow revealed several bottlenecks that limited its use for a nationwide implementation. The stages that represented most of the inefficiency were Data Collection, Data Processing, and Document Production. In the current workflow, information was not integrated on a single platform and different data types implied extra work for conversion and processing. The most significant bottlenecks identified are detailed below.

  • Data collection implied using trained resources such as engineers or architects to visit houses to conduct the geometric survey and vulnerability assessment. A large-scale implementation would be extremely costly using this scheme and the scarcity of trained staff could slow down the program.

  • The process of collecting geometric data for the house using a hand sketch was time consuming (it could take up to three hours) and produced inaccuracies down the line. For instance, during the design phase an engineer could find a missing measurement which forced the design process to stop until field staff confirms this missing value.

  • An engineer had to convert the hand sketch into a Revit model that represents the existing conditions. This required interpretation skills from the engineer and often led to errors in the model. Depending on the complexity of the house, this process could take up to two days.

  • The structural analysis was done using Excel spreadsheets that calculated shear and gravity loads. General behavior of the structure was verified through static linear analysis, checking stress concentrations and different failure modes. This engineering methodology is not suitable for a large-scale implementation because it focuses efforts in a case-by-case analysis.

  • Once the analysis defined the retrofit intervention, engineers proceed to add new structural elements to the model. This process involves a lot of Revit adjustments, dealing with phasing, element parameters, and graphics. The production of the Construction Package for one house could take up to nine days, taking into account rework caused by data inaccuracies and retake of measurements in field.

  • As a whole, there wasn’t a platform that integrates data from all stages to manage the project. Progress was tracked using an online spreadsheet but there was no way of incorporating all data types into one platform to have a global understanding of progress, delays and performance.

In summary, the existing methodology for retrofitting houses was not optimal for a large-scale deployment. The workflow heavily relied on qualified labor, which increased the cost considering the number of houses that would be retrofitted simultaneously. Also, the long processing times in design and document production phases, made the overall operation too expensive for scaling it up.

For instance, a typical two-story house would need a team of two trained professionals to assess and take measurements on site (usually civil engineers or architects), plus a structural engineer in the office performing analysis and retrofit design, and a drafter (could also be an engineer or architect) that puts together the set of plans, calculations and cost estimate in a construction package. This whole workflow normally takes from five to nine working days on full schedule, and varies depending on the complexity of the house. Now, considering the magnitude of the National Program we are addressing and the number of houses to deal with, this workflow is nonviable in terms of costs and time.

Proposed Workflow for Large-Scale Implementation

The reassessment of the retrofitting workflow showed key aspects to improve in order to make the national home improvement subsidy program feasible from a technical perspective. Qualified labor reliance, time spent in designs and document production were targeted for a redesign. The goal was to make a smooth workflow that streamlined the production of code-compliant retrofit designs with a focus on cost and time efficiency.

The solution we found to improve our workflow is based on third-party apps integration and BIM-automated tools. By setting our priority on the time spent in each process, we managed to overcome interoperability barriers between different software used. With this in mind, each bottleneck was addressed with a particular solution that, altogether, represented time savings of up to 78% in the workflow. A breakdown of the bottlenecks and their solution is summarized in the table below:

bottleneck
Bottlenecks found in the traditional workflow and tech-based solutions to address each one.

For the most part, automation of BIM tools had the most impact in the overall performance of the workflow, creating the biggest savings in time and effort. On the other hand, use of third-party apps and the platform integration made it possible to keep track of every house during all stages, and kept information organized throughout the process. Each one of these solutions will be discussed in more detail later.

How We Did It: Framework for BIM-Automated Tools

In a nutshell, we reduced the processing time per house by 78% while keeping the thoroughness of structural safety analysis and quality. The keys for this improvement were third-party app integration and automation of our BIM tools. By tweaking our existing workflows and adjusting the engineering methodology, we achieved technical and economic feasibility to implement the large-scale national program.

The new workflow reduced the reliance on a structural engineer in the assessment and design phases, allowing nonspecialized staff to produce code compliant structural designs automatically. In other words, we adjusted our engineering methods to allow for their automation. This streamlined the design production without using costly resources (trained structural engineers) and focusing them at the end of the process for revision and approval.

Nicolas Abello explains how BIM and automation are used to streamline the process of data collection and engineering to retrofit housing.

Data Collection

In the traditional workflow, the information was registered using paper and predefined forms to gather data from the house and homeowners. This caused significant downtimes and delays down the line because of inaccuracies in measurements, missing information, human errors and inaccuracies. The way we tackled this issue was by integrating mobile apps that could be used in smartphones or tablets, even in an offline mode. We divided our data collection in two groups: geometric survey and general information survey. Having our data divided in two categories enabled us to use specific apps to deal with different types, simplifying the overall process.

Magic Plan

This mobile app allows you to draw house floorplans including walls, windows, doors, floors, and other existing elements with your fingertips. Using predefined templates, we created a standardized protocol to draw house floorplans including key elements for the design phase. Also, the app was linked to a laser measure device to speed up the measurement taking. Once the survey is completed and internet connection is available, the plan is uploaded to the cloud where it can be downloaded from the office or any location.

phone
Sample of a house floor plan recorded using Magic Plan.

Fulcrum

The second portion of the data collection happened in Fulcrum. This mobile app is widely used in construction, manufacturing, and logistics for data capture and creation of smart forms. We incorporated this app into our workflow to collect and process information based on location. The form builder allowed us to design mistake-proof surveys that guided the surveyor through the questionnaire with adaptive responses and skip logic which made the process simple and efficient. More importantly, Fulcrum allowed integration with GIS that included seismic and vulnerability parameters from official databases. In this way, detailed information that used to be processed in the office is now preloaded in a digital form and ready to be used in field.

fulcrum
GIS databases of seismic hazard being converted to a grid of points to be linked through GPS coordinates into the form.

The integration of these third-party apps into the system improved data collection, making it more accurate, faster, and intuitive enough to be used by a nontechnical surveyor. Similarly, by integrating GIS information from vulnerability and seismic hazard databases, the assessment is done by the app, removing the need for a structural engineer on site. This improvement was achieved by overlapping location-based data such as peak ground acceleration and soil type with house configuration data (wall lengths, location and inter-story height) to calculate vulnerability status.

In this way, the data collection could now be performed by social mobilizers, students, volunteers, or anyone with a few hours of training. With this we had a massive cost reduction, by allowing anyone with some instruction on how to use the apps to collect data. Just imagine training people remotely on how to take geometric surveys all around Colombia in a few minutes with an app and a smartphone. That’s what scalability is all about.

Once the data is collected using Magic Plan and Fulcrum, both data packages are sent to a web based platform developed specifically for this program by Build Change. Essentially, this platform receives the plan made in Magic Plan and links it with the Fulcrum data using the homeowner name and ID. At this point, information from the house measurements, materials, configuration and vulnerability status are stored in an XML file.

Existing Model Generation

Right after data has been collected and stored in our web based platform, the second step is to model the existing conditions of the house. Traditionally this was done by looking at a hand sketch with measurements and annotations of the house and manually modeling it in Revit. Now, with the data collection digitalized and stored in our web based platform as an XML file, this process improved substantially. To speed up the existing modelling, we designed visual programming scripts in Dynamo that automate the house creation in Revit.

Here’s how we did it. All the information gathered in field is stored in an XML file, including the geometrical position and characteristics of all elements in the house. To extract this information, we used Python Dictionaries to parse the data and arrange it in a way that Dynamo can use it. The Python codes go through the hundreds of lines of code and obtains the required coordinates and element attributes for an automated modeling process. Some of the key info parsed by the script is shown below:

script
XML file with wall location coordinates, element dimensions, and characteristics needed for the automation scripts of the existing house.

A Python script deconstructed the list of values in the XML file and arranged the values needed by Dynamo to model wall, windows, doors, floors and slabs. There’s different values for each element, so they must be organized per type as follows:

  • Walls

             - X and Y coordinates for start and end points

             - Wall height

             - Wall type (thickness and material)

  • Windows and Doors

             - X and Y coordinates of the insertion point

             - Width, Height, Sill Height

             - Window/Door type

  • Slabs and Floors

             - X and Y coordinates of perimeter points

             - Elevation from level zero

             - Floor type (thickness and material)

Next, information is read by the Dynamo scripts separately for each category. For example, to model walls through Dynamo nodes we must obtain all the geometry from the parsed XML file and then organize it into start and end point coordinates. These can be represented as a point and then converted into a line with the “Line by Start Point End Point” node as shown below:

dynamo
Start- and end-point coordinates converted into base lines for walls using Dynamo.

The next input for wall creation are base and top levels. These are obtained from the parsed XML data, where each room created in the Magic Plan floorplan has an elevation. The Dynamo script then gathers this elevation and defines the levels accordingly. Finally, the wall type must be defined to the wall creation node to model the exact wall recorded in Magic Plan. This attribute is stored as a code which is replaced for the name of the wall family in Revit. The steps explained above are represented in the following portion of the script:

geometry
Existing wall creation using base line geometry, levels, and wall type.

Once the walls are modeled, similar scripts for windows, doors and slabs generate the rest of the existing house. Also, once these elements are placed by Dynamo, the “Phase Created” parameter is set to “Existing” to ensure they won’t interfere when the new (retrofit) elements are modeled. The result of the existing model is shown below:

house
Finished existing model of an informal house in Medellin, Colombia.

At this point, we’ve modeled the existing conditions of the house with exact measurements taken in field using Magic Plan, and a vulnerability status that resulted from Fulcrum data processed with seismic and soil parameters. The time spent in the existing house modeling decreased from one or two days to 10 minutes. Now, the house is ready to begin the structural analysis to determine the best retrofit solution.

Nicolas finished his studies in civil engineering in Bogota, Colombia. After working in commercial and infrastructure projects he became aware of how technology could improve productivity in the AEC sector. This encouraged him to question the way the industry works and inspired him to seek continuous improvement. He joined Build Change in 2018 to consolidate the New Frontier Technologies division, which oversees innovation for design and construction in all of the organization’s country programs. Since then Nicolas has worked in Colombia, Philippines, and Nepal leading the development of BIM tools for structural assessment, design and construction of safe houses and schools. Developments done by the New Frontier Technologies team have received international awards and created partnerships with major tech companies.

Noll is a senior international development professional who draws his primary motivation from innovation and from developing concepts that improve efficiency and effectiveness. For the past decade, Noll's focus has been on addressing the issue of substandard housing around the world. Specifically striving to improve houses’ resilience to natural disasters, including developing tech-based solutions to support the Government of Colombia in rolling out a national plan to seismically retrofit hundreds of thousands of houses, and providing homeowner-driven construction and retrofitting technical assistance to 25,000 households in rural Nepal, and to 2,000 families in informal neighborhoods of Port-au-Prince, Haiti. In 2018 Noll was promoted to head Build Change’s recently created New Frontier Technologies division.

Want more? Download the full class handout to read on.

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我们通过 Google Analytics (Strictly Necessary) 收集与您在我们站点中的活动相关的数据。这可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID、您的 Autodesk ID。我们使用此数据来衡量我们站点的性能并评估联机体验的难易程度,以便我们改进相关功能。此外,我们还将使用高级分析方法来优化电子邮件体验、客户支持体验和销售体验。. Google Analytics (Strictly Necessary) 隐私政策
Typepad Stats
我们通过 Typepad Stats 收集与您在我们站点中的活动相关的数据。这可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID、您的 Autodesk ID。我们使用此数据来衡量我们站点的性能并评估联机体验的难易程度,以便我们改进相关功能。此外,我们还将使用高级分析方法来优化电子邮件体验、客户支持体验和销售体验。. Typepad Stats 隐私政策
Geo Targetly
我们使用 Geo Targetly 将网站访问者引导至最合适的网页并/或根据他们的位置提供量身定制的内容。 Geo Targetly 使用网站访问者的 IP 地址确定访问者设备的大致位置。 这有助于确保访问者以其(最有可能的)本地语言浏览内容。Geo Targetly 隐私政策
SpeedCurve
我们使用 SpeedCurve 来监控和衡量您的网站体验的性能,具体因素为网页加载时间以及后续元素(如图像、脚本和文本)的响应能力。SpeedCurve 隐私政策
Qualified
Qualified is the Autodesk Live Chat agent platform. This platform provides services to allow our customers to communicate in real-time with Autodesk support. We may collect unique ID for specific browser sessions during a chat. Qualified Privacy Policy

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改善您的体验 – 使我们能够为您展示与您相关的内容

Google Optimize
我们通过 Google Optimize 测试站点上的新功能并自定义您对这些功能的体验。为此,我们将收集与您在站点中的活动相关的数据。此数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID、您的 Autodesk ID 等。根据功能测试,您可能会体验不同版本的站点;或者,根据访问者属性,您可能会查看个性化内容。. Google Optimize 隐私政策
ClickTale
我们通过 ClickTale 更好地了解您可能会在站点的哪些方面遇到困难。我们通过会话记录来帮助了解您与站点的交互方式,包括页面上的各种元素。将隐藏可能会识别个人身份的信息,而不会收集此信息。. ClickTale 隐私政策
OneSignal
我们通过 OneSignal 在 OneSignal 提供支持的站点上投放数字广告。根据 OneSignal 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 OneSignal 收集的与您相关的数据相整合。我们利用发送给 OneSignal 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. OneSignal 隐私政策
Optimizely
我们通过 Optimizely 测试站点上的新功能并自定义您对这些功能的体验。为此,我们将收集与您在站点中的活动相关的数据。此数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID、您的 Autodesk ID 等。根据功能测试,您可能会体验不同版本的站点;或者,根据访问者属性,您可能会查看个性化内容。. Optimizely 隐私政策
Amplitude
我们通过 Amplitude 测试站点上的新功能并自定义您对这些功能的体验。为此,我们将收集与您在站点中的活动相关的数据。此数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID、您的 Autodesk ID 等。根据功能测试,您可能会体验不同版本的站点;或者,根据访问者属性,您可能会查看个性化内容。. Amplitude 隐私政策
Snowplow
我们通过 Snowplow 收集与您在我们站点中的活动相关的数据。这可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID、您的 Autodesk ID。我们使用此数据来衡量我们站点的性能并评估联机体验的难易程度,以便我们改进相关功能。此外,我们还将使用高级分析方法来优化电子邮件体验、客户支持体验和销售体验。. Snowplow 隐私政策
UserVoice
我们通过 UserVoice 收集与您在我们站点中的活动相关的数据。这可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID、您的 Autodesk ID。我们使用此数据来衡量我们站点的性能并评估联机体验的难易程度,以便我们改进相关功能。此外,我们还将使用高级分析方法来优化电子邮件体验、客户支持体验和销售体验。. UserVoice 隐私政策
Clearbit
Clearbit 允许实时数据扩充,为客户提供个性化且相关的体验。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。Clearbit 隐私政策
YouTube
YouTube 是一个视频共享平台,允许用户在我们的网站上查看和共享嵌入视频。YouTube 提供关于视频性能的观看指标。 YouTube 隐私政策

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定制您的广告 – 允许我们为您提供针对性的广告

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

是否确定要简化联机体验?

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

个性化您的体验,选择由您来做。

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

我们是否可以收集并使用您的数据,从而为您打造个性化的体验?

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