Creating Apps & Python

Artificial Intelligence

## Using the functionality 

There are two ways in which you can use the functionality: 

- With VIKTOR, out-of-the-box, using our [free version]( https://www.viktor.ai/start-building-apps). 
- Without VIKTOR, in that case you integrate with your own Python code. 

This is a snippet of the functionality’s code. The whole code can be found and downloaded in the VIKTOR [GitHub repository](https://github.com/viktor-platform/sample-automatic-machine-learning):

```python 
@PlotlyView('Models', duration_guess, = 20) #for visualizing the model scores 
def calculate_models(self, params, entity_id, **kwargs): 
	comparison = get_model(params.dataset.data, params.dataset.target, params.choice.toggle) 
	comparison = pd.DataFrame(comparison) 
	cells = [comparison[col] for col in comparison.columns] 
 
fig = go.Figure(data=go.Table(header=dict(values=list(comparison.columns)), cells=dict(values=cells))) 
 
return__PlotlyResult(fig.to_json()) 
``` 

From the code snippet you can see that as little as three parameters can be used to make a machine learning model.  

In the video, you can see how a dataset is uploaded, different models and analysis plots are generated and viewed, and how this data is labeled in a VIKTOR application.

<div style="padding:56.25% 0 0 0;position:relative;"><iframe src="https://player.vimeo.com/video/708115759?h=77bf62082e&amp;badge=0&amp;autopause=0&amp;player_id=0&amp;app_id=58479" frameborder="0" allow="autoplay; fullscreen; picture-in-picture" allowfullscreen style="position:absolute;top:0;left:0;width:100%;height:100%;" title="Sample app Automatic Machine Learning"></iframe></div><script src="https://player.vimeo.com/api/player.js"></script>

</br> 

The application contains a toggle to distinguish between classification and regression, an input field for the name/path of your .csv file so it can easily find it, a target where the column needs to be learned and distinguished, different analysis plots that can be visualized, and a table input where data ca be inserted, after which the model predicts the new property.  

</br> 



## AI for everyone 
 
From the video, you can see that using the machine learning application is easily done in a couple of steps. 

1. In the app, the user can give the path to their .csv file, and which column they would like to predict. 
2. The back-end then handles everything and conveniently shows users the result for all tested models. 
3. Users can then see some plots to evaluate the model performance. The dataset is then labelled according to the model predictions so that outcomes can be compared.  
4. Finally, users can see what outcomes prediction on their new data will yield. 

Use our [free version](https://www.viktor.ai/try-for-free) to start using this app!

Blog_sample_app_auto_machine_learning

With this open-source Python code you can use different machine learning models to automatically classify and analyze data. More on VIKTOR.ai.

Use machine learning in a web-based application

Create your own automatic machine learning app

1. An introduction to Artificial Intelligence
2. Artificial Intelligence vs Machine Learning
3. AI and ML in organizations
4. Building a Machine Learning application
5. About VIKTOR

artificial intelligence AI machine learning ML engineering construction

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artificial intelligence machine learning engineering construction

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Have you ever wondered how you can use AI to automate engineering processes? This white paper is for all engineers, managers, or directors that want to know more about applying AI-methods, such as machine learning, in their organization. 

Find out more about the different types of AI and how you can use them to improve and automate (engineering) processes within your organization. You will also see some examples of real AI apps used by organizations in the industry and learn what it takes to build a machine learning application yourself. 

Keep scrolling to download the white paper.

Find how you can use AI automate engineering processes in your organization.

Download the White Paper and get INSPIRED

Have you ever wondered how you can use AI to automate engineering processes? This white paper is for all engineers, managers, or directors that want to know more about applying AI-methods, such as machine learning, in their organization. 

Artificial Intelligence: Engineering and Construction

Parametric design

## Pros and cons of Grasshopper

Grasshopper is a strong tool when it comes to 3D modelling and quickly developing scripts to manipulate geometries. Grasshopper seamlessly integrates with Rhino, which makes it possible to visualize changes in 3D models immediately, and is famous when it comes to architecture and BIM. Over the past years, different plug-ins have made it possible to integrate Grasshopper software with other software as well. 

However, sharing Grasshopper scripts internally is difficult, and externally impossible without revealing any of your source code. Additionally, managing and processing large amounts of data or performing numerical calculations and matrix operations for the optimization of designs can be difficult as well. 

## A Grasshopper web app 

That is why VIKTOR enables the development of web applications that you can use to link Grasshopper models to an interface and share these with internal and external users without requiring any downloads or knowledge about Grasshopper.

In this article, we give an example of such an application. You can find the open-source Python code in our [GitHub repository](https://github.com/viktor-platform).

## Using the functionality 

You can use the code out-of-the-box with VIKTOR.

When using the web app with VIKTOR, you should:

1. Make sure you have Rhino installed at ``C:\Program Files\Rhino 7\System\Rhino.exe``
2. Install a generic worker found on https://docs.viktor.ai/docs/worker. Make sure you select ``generic``.
3. Replace the config.yaml file in your worker folder with the file found at ``app/grasshopper/data/config.yaml``.
4. Copy the file found at ``app/grasshopper/data/run_grasshopper.bat`` in the same worker folder.

Below, you see a snippet of the Python code that shows how little lines of code are needed to run Grasshopper analysis and retrieve files. 

```python
# Run the analysis on grasshopper
generic_analysis = GenericAnalysis(files=files, executable_key="run_grasshopper,output_filenames=["output.txt", "output.obj", "output.mtl"])
generic_analysis.execute(timeout=300)

# Retrieve all files from the analysis
grass_hopper_data_bytes = generic_analysis.get_output_file("output.txt")
object_file = generic_analysis.get_output_file("output.obj")
material_file = generic_analysis.get_output_file("output.mtl")
```

In the video, you can see how a football stadium is [parametrically created](https://www.viktor.ai/blog/46/parametric-design-cloud-engineering-construction) through an integration with Grasshopper. On the left side of the application are all input parameters and a download button, in the middle you can see a 3D view of the mesh, and on the right side you can find all calculated information.

<div style="padding:48.75% 0 0 0;position:relative;"><iframe src="https://player.vimeo.com/video/706022495?h=9bdfe52a5e&autoplay=1&loop=1&autopause=0&amp;badge=0&amp;autopause=0&amp;player_id=0&amp;app_id=58479" frameborder="0" allow="autoplay; fullscreen; picture-in-picture" allowfullscreen style="position:absolute;top:0;left:0;width:100%;height:100%;" title="Sample app parametric football stadium with Grasshopper"></iframe></div><script src="https://player.vimeo.com/api/player.js"></script>


## Parametric football stadium in 3 steps

Complete the following three steps to parametrically design a football stadium with Grasshopper using the sample application. 

1. **Define input parameters**, such as the shape, pitch width, offset from pitch, depth factor, asymmetrie along length and width, and height. 
2. **Create a 3D model**. Based on the input parameters, the field width, field length, and number of seats are calculated. 
3. **Download the 3D model**. Unfold 'downloads' to download a .glb (Graphic Language Bytes) file of the design. 

![Parametrically design football stadium grasshopper](https://cms.viktor.ai/uploads/Blog_sample_app_parametric_stadium_grasshopper_98ab0e1008.png)





Blog_sample_app_grasshopper

Discover how you can make your Grasshopper scripts more accessible with this open-source web app. Read more on VIKTOR.ai.

Parametric design of football stadium with Grasshopper

Parametric football stadium with Grasshopper

1. What is parametric design, and how is it evolving
2. Collaborative parametric design: working together for better results
3. How to make tools available to others
4. Active collaboration on parametric design tools
5. Parametric design in a billion-euro project
6. What collaborative parametric design means for you



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Parametric design is a design approach that uses flexible models that can be adjusted and solved quickly. The goal is to see the effect of specific choices on your design by just changing the input variables. 

The benefits are:

- **Flexibility:** Parametric design allows you to react quickly to changes at a low cost. Always generate the best solution for your project no matter the time pressure.

- **Better decisions:** Automatically calculate thousands of different solutions, get deep insight, and mitigate risks. Make better decisions and provide more added value to clients.

- **Digital Assets:** Create models and data that are reusable in new projects. Improve them over time, building up digital capital

This white paper is for engineers, architects, project leaders, and innovation and business managers. You will learn how collaborative parametric design in the cloud helps organizations get better results with real study cases such as a billion-euro infrastructure project.
 
Additionally, we talk about how you can transform Python, Dynamo and Grasshopper scripts into user-friendly web apps and significantly impact your organization performance. 

Learn how collaborative parametric design models allow you to work together for better solutions.

Download the white paper and get INSPIRED

This white paper is for engineers, project leaders, and innovation and business managers that want to learn more about what collaborative parametric design can mean for them. Discover more about this new way of working in which stakeholders collaborate to find the best solution for a project.

The Future of Parametric Design is COLLABORATION

Tutorials

## Python and Excel in VIKTOR

With VIKTOR, it is possible to create a web app with Python that integrates with any desired software, just like Excel. This web app functions as a single point of truth with a central database that always contains the latest version of a project (with version history) that all stakeholders can access.

We have written open-source code as an example of such a VIKTOR application for the automated calculation of a simply supported beam under load with Excel. 

## Using the functionality

There are two ways in which you can use the functionality: 

- With VIKTOR, out-of-the-box, using our [free version](https://www.viktor.ai/start-building-apps). 
- Without VIKTOR, in that case you integrate the open-source code with your own Python code 

For a full tutorial on this web app click [here](https://docs.viktor.ai/docs/guides/tutorials/calculation-spreadsheet). 

This is a snippet of the functionality’s code from the VIKTOR [GitHub repository](https://github.com/viktor-platform/sample-spreadsheet): 

```python
def get_evaluated_spreadsheet(self, params): 
  inputs = [ 
     SpreadsheetCalculationInput('L', params['general']['beam']['length']), 
     SpreadsheetCalculationInput('W', params['general']['beam']['width']), 
     SpreadsheetCalculationInput('H', params['general']['beam']['height']),
     SpreadsheetCalculationInput('E', params['general']['beam']['E']),
     SpreadsheetCalculationInput('aw', params['general']['beam']['aw']), 
     SpreadsheetCalculationInput('wa', params['general']['beam']['wa']),
  ] 
  sheet_path = Path(__file__).parent / 'beam_calculation.xls'
  sheet = SpreadsheetCalculation.from_path(sheet_path, inputs=inputs) 
  result = sheet.evaluate(include_filled_file=True) 
  
  return result 
```

Here you can see it is very easy to send input to your Excel sheet. It is required to have a tab in Excel called ‘viktor-input-sheet'. To get results, you use the evaluate() method. Here, VIKTOR retrieves output from the ‘viktor-output-sheet' Excel tab. 
 
The documentation for the `SpreadsheetCalculation` class can be found [here](https://docs.viktor.ai/docs/guides/services/spreadsheet-calculator) 

In the video, you can see how a simply supported beam under load is calculated through an integration with a spreadsheet in a VIKTOR application.

<div style="padding:56.25% 0 0 0;position:relative;"><iframe src="https://player.vimeo.com/video/703176976?h=c6d27796c9&autoplay=1&loop=1&autopause=0&amp;badge=0&amp;autopause=0&amp;player_id=0&amp;app_id=58479" frameborder="0" allow="autoplay; fullscreen; picture-in-picture" allowfullscreen style="position:absolute;top:0;left:0;width:100%;height:100%;" title="Automating calculations in Excel with Python in VIKTOR"></iframe></div><script src="https://player.vimeo.com/api/player.js"></script>

</br> 

## Calculating a beam in 3 steps 

As you can see in the video, the process of automatically calculating a simply supported beam under load consists of 3 steps. 

1. **Insert general information**. Provide information about the beam (length, the width, height, and modulus of elasticity) and loads (starting point and distributed load amplitude).   
2. **Generate results**. View the maximum deflection and maximum bending stress, a schematic visualization, and a 3D visualization of the beam and loads.

</br>
 
![schematic visualization of a beam in Excel](https://cms.viktor.ai/uploads/Blog_sample_app_Excel_73a3917fda.svg)

*Schematic visualization of a simply supported beam under load.* 

 </br>

![3D visualization of a beam in Excel](https://cms.viktor.ai/uploads/Blog_sample_app_Excel_2c9ec5c3b5.png)

*3D visualization of a simply supported beam under load.* 

</br>

3. **Download the report**. Download the calculation sheet to view on your device. 

</br>

Use our [free version](https://www.viktor.ai/try-for-free) to start using this app!

Blog_sample_app_Excel_Python

Use this open-source web application to automate calculations with Excel and Python. Read more on VIKTOR.ai.

Automate Excel calculations with this open-source application. VIKTOR.ai.

Automating calculations in Excel through a web app

### Content: 

1. An introduction to developing successful applications
2. Designing and planning your application
3. How to start coding
4. Gathering and processing feedback
5. Increasing the adoption of your app



How to create successful application that ensure adoption

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How to create a succesful that people will adopt

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Do you want to build a successful application? This guide is here to help you make apps that provide real value to your work and that other people will definitely want to use too!

This guide will provide actionable insights that are useful for anyone involved in creating custom software tools, such as:

- **People developing the tools** (developers, engineers, data scientists, etc..)
- **End-users**, who will use the tools when they are released
- **Project managers** who are interested in workflow automation/optimization
- **Program managers,** who oversee and stimulate digital transformation initiatives
- **Domain experts**, who provide their expertise to people creating software tools
- And **anyone else i in the process of creating awesome software tools**

Over the past years, we, the application developers at VIKTOR, have built many apps for many different industries. We have learned a lot from that and now want to share our experiences with you. 

The principles and processes we explain in this guide are the same as we apply daily. They have helped us build anything from simple apps designed to save time to advanced engineering applications that are used to automatically calculate underground tunnels, dikes, and other large structures. 





Learn how you (developer, engineer, end-user, domain expert, project manager, etc.) can contribute to the creation of apps that provide real value to your work.

Build successful applications

This guide provides actionable insights for anyone involved in the creation of apps, such as engineers, developers, end-users, project managers, and domain experts. Learn how you can contribute to creating apps that provide real value to your work!

Building Successful Applications

VIKTOR updates

![SDK v13](https://cms.viktor.ai/uploads/Blog_newsletter_SDK_v13_0636b26c32.gif)
</br>

## Easily upload files inside the editor

You can now make it easier for users to upload and manage files in your application by adding a `FileField` to the parametrization.

From now on, users can use the file manager in the editor to upload all files, which can then be selected in the FileField. This means that there is no longer the need to create separate file entities below an entity to enable file uploading. 

Visit the documentation for [more info](https://docs.viktor.ai/sdk/api/parametrization?_highlight=filefield#_FileField).

![File transfer modal](https://cms.viktor.ai/uploads/Blog_newsletter_File_Field_cdcc9b9df6.png)
*File transfer modal*

</br>
</br>

## Store data and files in your app

The new `Storage` feature lets you permanently store files and data, and use it anywhere in your app.

You can use the storage for multiple purposes, such as storing calculation results, using the data to generate a view or report without rerunning the calculation, or storing data that you don’t want to expose to users of your app.

This new feature provides many new opportunities, and we are looking forward to seeing how you will implement it!

[Read more](https://docs.viktor.ai/docs/create-apps/fundamentals/call-flow?_highlight=storage#persistent-storage) about easier data storage and retrieval in the documentation.

</br>
</br>

## Easily select entities inside the editor

You can now easily select entities from anywhere in the entity tree inside your editor.

Previously, selecting entities was only possible for child and sibling entity fields, or you would have to write the implementation yourself using the API module. With the introduction of `EntityOptionField` or `EntityMultiSelectField` it becomes possible to more efficiently retrieve data from any entity in the entity tree and allow end-users to intuitively select entities. 

Go to the documentation to [find out more](https://docs.viktor.ai/docs/create-apps/user-input/entity-selection?_highlight=enti) about the extended entity fields.  

![Select mutiple objects modal](https://cms.viktor.ai/uploads/Blog_newsletter_Entity_Field_7b76299d5e.png)
*MultiSelect*

</br>
</br>

# Everyone can develop apps for free!

Have you ever thought: "Hey, I know someone that should use VIKTOR!"? We have good news! We also believe that everyone should get the chance to build awesome apps. That is why everyone can now develop VIKTOR apps for free.

 

Help expand the VIKTOR Community! Share the good news, and send your colleagues or friends [this page](https://www.viktor.ai/start-building-apps). After submitting the form, they will get their developer account.

![Group 21.png](https://cms.viktor.ai/uploads/Group_21_0ff9c8f919.png)
*Invite a friend!*

</br>
</br>

## Track your application's activity

Did you want to monitor your organization's usage of apps? Good news! On the new Activity Dashboard, admins can keep track of:

- The number of users in your Cloud environment and on each workspace
- User activity
- The most used workspaces

We hope this new feature will help you understand which app provides the most value to your organization and how you can improve adoption.

 
![Activity dashboard](https://cms.viktor.ai/uploads/Blog_newsletter_Activity_dashboard_6f9a25e740.png)
*Activity dashboard*

</br>
</br>

## Automate app publishing

You can now use the new CLI command `ci-publish` to automatically publish a new app to VIKTOR in your CI pipeline after merging changes or creating a tag on the repository. This way you make sure your app always runs the latest code. Additionally, it makes publishing VIKTOR apps a lot easier, as it no longer requires any manual steps!

Visit the documentation for [more info](https://docs.viktor.ai/docs/create-apps/references/cli?_highlight=ci#ci-install).

![ci-publish command.png](https://cms.viktor.ai/uploads/ci_publish_command_a6a8b65675.png)
*ci-publish*

</br>
</br>

# Coming soon...

**Create basic apps with CLI command:** To make it faster and easier to set up new apps from scratch, we are adding a command to the CLI that generates a basic app for you. 

**Developer environment consolidation & self-servicing of tokens:** Your developer environment will be moved to your company's environment, so you will be able to create and use apps in a single place. Here, you will also be able to generate your own developer token.

**Workspace-specific workers:** We are building a feature that enables assigning a worker to a specific workspace so that apps from different workspaces that depend on the same integration can no longer block each other's tasks. 

</br>
</br>

# Other Changes

### SDK

- New version v13.0.0 is now available! 

### PLATFORM

- Fixed issue with PlotlyAndDataView not taking full height 
- Added fixed-size MapLabels
- Added support for defining up-axis in GeometryView
- Fixed the workspace invitation email to show the invitee name
- Updated app clearing to not delete the workspace image in development environments
- Fixed MapPoint 'wye' icon not showing color 
- Fixed the data view error state not clearing properly and improved text readability 
- Removed invalid `dseries` integration type from supported workers 
- Fixed param changes not being picked up in dynamic array tables in some cases 
- Fixed issue with stepwise editor missing a bottom border 
- Improve markdown rendering performance 
- Allowed deselection in the single file field 
- Updated the toggle button to use the primary color 


### CLI

- Support for development workspaces in primary environments
- Validation of credentials for easier debugging
- Use existing credentials as default values when reconfiguring the CLI




Blog_newsletter_april_2022

Discover what is new in the VIKTOR platorm for April 2022.

VIKTOR platform updates | April 2022

What's new in VIKTOR (April 2022)

## Pros and cons of Dynamo 

Dynamo is a great tool for designers to create visual logic that you can use to easily calculate and visualize a parametric design of a structure. With Dynamo, designers can automate repetitive design tasks in order to use their productive time more wisely.  
 
However, the visual logic of Dynamo can be difficult to read for any other person than the one who created it. The logic quickly becomes ‘messy’, which reduces the usability. Especially for longer running projects this can become a problem when certain people leave or get assigned to a different project. Additionally, sharing Dynamo models is difficult, because an installation of Dynamo is required on each individual device to open the drawings.  
 
## A Dynamo web app  

With VIKTOR, you can develop user-friendly web applications that integrate with all kinds of software programs, such as Dynamo. VIKTOR apps have a clear and intuitive user interface in which users only see the adjustable parameters, which improves the usability of Dynamo software. VIKTOR also enables connecting Dynamo drawings to other computational programs through integrations, for example to calculate the strength of the structure. Additionally, everything runs in the Cloud, making your data always accessible to anyone with an internet connection! 

In this article, we give an example of such an application. Continue reading to learn more, or use our [free version](https://www.viktor.ai/try-for-free) to start using this app!

## Using the functionality 

You can use the code out-of-the-box with VIKTOR.

Here, you see a snippet of the Python code that shows how little lines of code are needed to edit, compute and visualize your Dynamo model within VIKTOR.   

```python
file = File.from_path(Path(__file__).parent.parent / "lib" / "files" / "dynamo_model_sample_app.dyn") 
input_file = DynamoFile(file) 
 
input_file.update("Height roof", params.step_2.height_roof) 
 
# Generate updated dynamo file and load into memory 
input_file = input_file.generate() 
files = [('input.dyn', BytesIO(input_file.getvalue_binary()))] 
 
# Run the analysis and obtain the output file 
generic_analysis = GenericAnalysis(files=files, executable_key="dynamo", 
                                   output_filenames=["output.xml", "geometry.json"]) 
generic_analysis.execute(timeout=60) 
 
# Generate output file and set up data for the DataViewer 
output_file = generic_analysis.get_output_file("output.xml", as_file=True) 
 
```

- In row 1 and 2 the dynamo model is loaded 
- In row 4 the dynamo model is updated  
- In row 7 and 8 the dynamo model is updated with your input parameter
- In row 11 to 13 the dynamo model is computed by the worker and sent back to the application 

And that’s it! Your model is now computed and visualized within the VIKTOR platform. 

The necessary modules are included in this sample app. All that needs to be done is import them, call them and let them do their work for you!

In the video you can see the functionality being used in an applicaton on the VIKTOR platform.

<div style="padding:56.25% 0 0 0;position:relative;"><iframe src="https://player.vimeo.com/video/698906446?h=5ff0ca5cc4&autoplay=1&loop=1&autopause=0&amp;badge=0&amp;autopause=0&amp;player_id=0&amp;app_id=58479" frameborder="0" allow="autoplay; fullscreen; picture-in-picture" allowfullscreen style="position:absolute;top:0;left:0;width:100%;height:100%;" title="Sample app Dynamo"></iframe></div><script src="https://player.vimeo.com/api/player.js"></script>

</br>

## Dynamo in 3 steps 

The process of using the Dynamo sample app consists of 3 steps.  

1. **Defining the location.** As a first step you can choose the location of you project on the map. In the example the construction of a residential block is used. By clicking on the map the necessary required space for the project is shown on the map. When the input parameters are adjusted, they are also adjusted on the map 

![Define location on the map](https://cms.viktor.ai/uploads/Blog_sample_Dynamo_1_5bf72b8656.png)
</br>

2. **3D dynamo model.** In this step the input parameters for your project are required. For this example the parameters are used such as the dimensions of the house and the number of houses. When the update button is pressed, the model is computed and vizualized. 

![Generate 3D model](https://cms.viktor.ai/uploads/Blog_sample_Dynamo_2_2bec3c873d.png)
</br>

3. **Report.** It is possible to compute additional information related to your project, such as the cost of construction or the impact on the environment. Using the Report functionality from VIKTOR a report is generated automatically, in which all this information is presented. It is of course possible to edit the template of this report to your taste. 

![Download report](https://cms.viktor.ai/uploads/Blog_sample_Dynamo_3_5d9bc8427f.png)

Use our [free version](https://www.viktor.ai/try-for-free) to start using this app!

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Blog_sample_app_Dynamo

Use this open-source sample application to make your Dynamo models more accessible. Read more on VIKTOR.ai.

Make Dynamo models more accessible with open-source sample app

Sample app Dynamo

## Using the functionality 

You can use the functionality out-of-the-box with VIKTOR. VIKTOR provides an easy way to visualize input data, designs, and output results fast, just as the ability to conveniently integrate external software, such as SCIA and D-Foundations. 

This is a snippet of the functionality’s code from the VIKTOR [GitHub repository](https://github.com/viktor-platform): 

```python 
@memoize 
def run_dfoundations(foi_file_content: str, **kwargs) -> str: 
  """Sends input file through D-Foundations and obtains output file""" 
  foi_file = File.from_data(foi_file_content) 
  dfoundations_analysis = DFoundationsAnalysis(input_file=foi_file) 
  dfoundations_analysis.execute(timeout=300) 
  fod_file = dfoundations_analysis.get_output_file(as_file=True) 
  return fod_file.getvalue() 
```

This code snippet describes how a D-Foundations analysis is triggered in the application with 8 lines of Python code. The full application code can be found in the [GitHub repository](https://github.com/viktor-platform). 

In the video, you can see how pile foundations are analysed in a VIKTOR application.

<div style="padding:56.25% 0 0 0;position:relative;"><iframe src="https://player.vimeo.com/video/694319977?h=9b83f627ec&autoplay=1&loop=1&autopause=0&amp;badge=0&amp;autopause=0&amp;player_id=0&amp;app_id=58479" frameborder="0" allow="autoplay; fullscreen; picture-in-picture" allowfullscreen style="position:absolute;top:0;left:0;width:100%;height:100%;" title="Pile foundations with D-Foundations and SCIA"></iframe></div><script src="https://player.vimeo.com/api/player.js"></script>

</br> 


 
## Pile foundation design in 5 steps 

As you can see in the video, the process of analysing pile foundations consists of 5 steps.  

1. **Select CPT from an uploaded .GEF file.** The available CPTs are visible in the MapView.  

![Select CPT from uploaded GEf file](https://cms.viktor.ai/uploads/Blog_sample_app_pile_foundation_1_5cceaef44b.png)

*Select CPT*

</br> 

2. **Analyse CPT** The soil layout can be visualized in the GEF-tab. Next, the CPT can be analysed in the D-Foundations-tab, where the bearing capacity is obtained with respect to the depth of the CPT. 

![analyze CPT pile foundation](https://cms.viktor.ai/uploads/Blog_sample_app_pile_foundation_2_fa6e0182ff.png)

*Analayze CPT with D-Foundations*

</br> 

3. **Create a foundation.** Here, a foundation geometry with an applied load can be created and visualized. The colour of the foundation is linked to the soil layout of the selected CPT in step 1. 

![Create a pile foundation with D-foundations and SCIA](https://cms.viktor.ai/uploads/Blog_sample_app_pile_foundation_3_3f663fb037.png)

*Create pile foundation*

</br> 

4. **Analyse foundation.** In the SCIA result-tab, an analysis in SCIA is called to find the maximum reaction forces allowed to act on a single pile. 

![Analyze foundations with D-foundations and SCIA](https://cms.viktor.ai/uploads/Blog_sample_app_pile_foundations_4_3d245eee5a.png)

*Analyze foundation with SCIA*

</br> 

5. **Visualize combined results.** The results from the D-Foundations- and SCIA-analyses can be combined to plot a required pile tip level for the defined foundation at the selected CPT location. The user can choose to go back to step 3 to re-analyse the foundation with a different pile length based on this result.  

![Pile foundations visualize results](https://cms.viktor.ai/uploads/Blog_sample_app_Pile_Foundations_step_5_7da7b00bf6.png)

*Visualize combined results*

</br> 

Use our [free version](https://www.viktor.ai/try-for-free) to start using this app!



Pile foundations with D-Foundations and SCIA

Blog_sample_app_pile_foundation

Open-source web app for the parametric design of pile foundations with SCIA and D-Foundations. Read more on VIKTOR.ai.

Design Pile Foundations with D-Foundations and SCIA

How to design Pile Foundations with D-Foundations and SCIA

## The Complexity of the AEC Industry

The AEC industry is known for its complexity, involving various stakeholders, intricate building designs, and evolving project requirements. Coordinating different disciplines and managing modifications throughout the project lifecycle can be challenging, especially when it comes to aligning HVAC drawings with ongoing design changes from other disciplines (such as: architects, structural engineers, etc.).  

Solving the challenges of AEC work nature often boils down to enabling better coordination between teams. An improvement that often translates into valuable time and cost savings! In this article we will discuss one of AEC's main challenges and introduce a potential solution!  

## The Challenge of Aligning HVAC Drawings and Modifications in AEC 

During the design and construction phases, modifications to the building design are inevitable. However, incorporating these modifications into HVAC drawings can be time-consuming and prone to errors. Ensuring that HVAC systems are accurately represented in the drawings and aligning them with evolving project requirements pose significant challenges for AEC professionals. 

## HVAC-Specific Software Solutions 

To address the complexities of HVAC design in the AEC industry, several software solutions are available. These tools, such as AutoCAD MEP, Revit MEP, and DesignBuilder, provide specialized functionalities for HVAC design, simulation, and documentation. They help streamline the creation of HVAC drawings and facilitate coordination with other building systems. Despite the usefulness of various software tools for HVAC analysis and calculations, effectively coordinating with other specialists on a project remains a significant challenge. This aspect of the AEC industry underscores the necessity of adopting a holistic approach, where all specialists can collaborate and align their efforts as early as possible. 

## Necessity is the mother of invention: BIM LOD and Early-Stage Optimization 

Building Information Modeling [(BIM)](https://www.viktor.ai/blog/100/how-BIM-innovations-transform-AEC) is a collaborative process that enables the creation and management of digital representations and information of buildings. BIM Level of Development/ Detail (LOD) defines the level of detail and accuracy of the BIM model at different project stages. Early-stage optimization in BIM involves leveraging low LOD models and simulations to make informed decisions that enhance HVAC performance while considering cost and energy efficiency. Within low LOD it is feasible to have efficient [early-stage optimization](https://www.viktor.ai/blog/106/unlocking-early-stage-design-optimizations-with-topologic-and-bim). Especially, as it is still easy to solve HVAC problems by alternating layouts and deploying optimization plans without redoing a previously produced tedious workload.

![HVAC picture 1.png](https://cms.viktor.ai/uploads/HVAC_picture_1_34490cb4c0.png)

It is more feasible to fix problems and optimize solutions in the early phases of the project when it is in the design phase. On the other hand, fixing HVAC problems in the late stages of the building is extremely hard and expensive as physical installation had begun. Luckily enough, there is a new technology to enable HVAC engineers to optimize their solutions from the early stages, In addition, running efficient simulations of their systems as well as communicating easily with low LOD BIM models! 

## A New Promising Technology: Topologic by Prof. Wassim Jabi

Topologic, developed by Wassim Jabi, is an innovative software tool that offers advanced geometric and topological analysis capabilities. It allows AEC professionals, including HVAC engineers, to explore complex spatial relationships, optimize design layouts, and assess the impact of modifications more efficiently. By incorporating Topologic into their workflow, HVAC engineers can enhance coordination and improve the integration of HVAC systems within the low LOD models. Topologic automates HVAC simulation, calculation and layout of building geometries. As it understands the whole 3d mass of the BIM model it can quickly understand the shortest and most efficient path for the ducts while being accessible to technicians. 

![hv tiny.gif](https://cms.viktor.ai/uploads/hv_tiny_03a51eaa8d.gif)

*Topologic compartments and fire simulations*

Additionally, Topologic offers a Python binding that enhances its flexibility and integration capabilities. This allows HVAC engineers to leverage the power of Topologic in conjunction with other powerful software tools such as EnergyPlus and OpenStudio. EnergyPlus is a widely-used simulation engine for energy analysis and thermal performance evaluations in buildings, while OpenStudio is a platform that enables whole-building energy modeling and analysis. The integration of Topologic with EnergyPlus and OpenStudio opens up new possibilities for HVAC engineers to conduct more comprehensive simulations, analyses, optimize energy efficiency, and fine-tune HVAC system performance based on advanced geometric and topological insights provided by Topologic. 

![hv 33.gif](https://cms.viktor.ai/uploads/hv_33_aece7c93f3.gif)

*Building Energy Analysis with Topologic*

In addition, Topologic specializes in handling low Level of Development (LOD) models, particularly conceptual masses. This allows for the development of a generative design logic that can provide initial layouts of HVAC systems without disrupting the drawings of other specialists. What's more, the integration of Topologic with machine learning is a crucial advancement. By training it with diverse cases, Topologic can offer improved solutions and provide valuable assistance to engineers through its generative design capabilities.

![hv 4444444.gif](https://cms.viktor.ai/uploads/hv_4444444_dbb24b69a0.gif)

*Topological awareness of spatial and structural elements in 3d model based on wall function, dimensions, U and R-value*

To leverage the full potential of the promising technology of Topologic, the logical progression is to convert these code components into applications that enable engineers to efficiently carry out their required tasks. This entails developing an optimized workflow that relies on input from user-engineers, allowing for seamless execution of their tasks while saving the time of building the logic each time. Overall, apps are easier to operate and can host Topologic algorithms for multiple users simultaneously!

## Web Apps to assist engineers 

VIKTOR aims to unlock the full powers of engineering by automating boring tasks, saving time and effort for creative engineering, and offering the power of advanced algorithms and data-driven processes to enhance design efficiency! As a  cloud-based platform designed specifically for engineers in the architecture, engineering, and construction (AEC) industry. It empowers engineers to rapidly develop custom web apps using Python. In view of emerging engineering complexities like interoperability, VIKTOR apps integrate with all engineering software and offer a range of tools to streamline and optimize engineering workflows.  Additionally,  enabling knowledge sharing through cloud accessibility!

## VIKTOR: Web Apps for Topologic

VIKTOR integrates with Topologic and provides web applications specifically tailored for AEC professionals. With VIKTOR, HVAC engineers can leverage the power of Topologic through an intuitive interface, enabling them to simulate, analyse, and optimize HVAC designs more effectively. 

![HVAC 5 Resized.png](https://cms.viktor.ai/uploads/HVAC_5_Resized_a56cc74f0e.png)

### 1. Initial layout and estimations system generation

The combination of Topologic and VIKTOR offers enhanced capabilities for early-stage design optimization, automatic layout of HVAC systems, and collaboration among project stakeholders.

Within low LOD (Level of Development) models, engineers have the opportunity to create a VIKTOR app utilizing Topologic that automates the generation of HVAC system layouts and calculations across the entire 3D model. This approach proves more efficient compared to configuring individual floor plans separately, resulting in significant time savings for the design process.

### 2. Data and geometry-aware system generation

Furthermore, apart from generating layouts, a VIKTOR app integrating Topologic has the capability to interpret various spatial data elements, including compartments, the structural skeleton of the building, and the U and R values of building components. Additionally, it can incorporate HVAC calculations such as Load Calculations, Airflow Calculations, and Duct Sizing Calculations, among others. This comprehensive approach enables the generative process of the VIKTOR and Topologic app to effectively comprehend the interplay between different disciplines, facilitating the determination of optimal strategies for HVAC system generation.

### 3. Smart UI to assist decision-making

Additionally, being a cloud-based application, the user interface of the app is specifically designed to support end-user engineers in making well-informed decisions. Engineers can obtain real-time simulations and results based on their input, presented in visually engaging graphs that provide a clear visualization of data besides the 3D geometry. Moreover, the app offers the convenience of automating reporting processes and facilitating easy sharing of results with just a few clicks. Additionally, the underlying logic of the app is accessible to user-developer engineers, allowing them to further enhance and expand the app's functionality with additional features.

### 4. All disciplines in one platform

To summarize, the application facilitates the integration of inputs from various (Architectural, structural and HVAC) disciplines, and It employs advanced algorithms to analyze the inputs and generate solutions that avoid clashes between disciplines’ inputs. Moreover, the application is designed to dynamically adapt to new inputs from any team while being easily accessible by authorized professionals to make essential modifications to app logic! This empowers innovative engineers to explore new tools and continuously enhance the performance of their apps to meet evolving project requirements.

## Conclusion

In the rapidly evolving AEC industry, HVAC design and coordination are critical for achieving comfortable and energy-efficient buildings. The complexity of the industry and the challenges associated with aligning HVAC drawings and modifications necessitate innovative solutions. Topologic and its integration with VIKTOR hold immense promise in addressing these challenges, empowering HVAC engineers with advanced tools for optimized design, coordination, and collaboration throughout the AEC process. By embracing these technologies, the industry can enhance the delivery of high-performing HVAC systems that meet the evolving needs of modern buildings.


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The AEC industry heavily depends on HVAC systems to create comfortable and healthy indoor environments in buildings. These systems regulate temperature, humidity, air quality, and ventilation, which are vital for occupant well-being and energy efficiency. To aid HVAC professionals, it is essential to leverage cutting-edge technologies and develop efficient tools.

Digital Solutions in HVAC Design: Benefits of Topologic and Early-stage Optimization

- What is Building Information Modeling (BIM)? 
- BIM for Architecture, Engineering, and Construction (AEC)
- Trends in BIM
- So, what's next? BIM level 3?
- Solve your BIM challenge with VIKTOR!
- Conclusion


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In the world of Architecture, Engineering, and Construction, Computer-Aided Design (CAD) has been a go-to technology for decades. While it was a significant breakthrough in its time, it has its limitations, and many industries now find it inefficient and lacking in collaboration. This has led to the rise of Building Information Modeling (BIM), which has brought many benefits to the table. Real-life projects such as hospitals, schools, and high-rise buildings have implemented BIM and have seen significant improvements in their project lifecycle across all AEC disciplines. In fact, industry leaders have also recognized the potential of BIM and have embraced it as a way to enhance their performance.

In this White Paper, we will explain VIKTOR's vision in BIM: Efficiently pioneering today's & next-gen BIM cloud-based Automation! We will provide information on the meaning, benefits, challenges, and future of BIM!

Efficiently pioneering today's & next-gen BIM cloud-based Automation! We will provide information on the meaning, benefits, challenges, and future of BIM!

VIKTOR Blog