Digital transformation

Creating Apps & Python

Previously, many calculations for fender systems were done with rules of thumb. This is no longer possible, due to the stricter new standards. The calculations now have to be done in more detail, which creates a lot of extra work for the engineers. Calculating all options manually one by one is laborous, so it was decided to automate them. Louis: “The calculation of constructions will be increasingly automated. The time gained from this can then be used to solve complex problems.”

## Automating repetitive tasks

Peltenburg explains: “Repetitive elements are very suitable for programming, and so can be calculated using an application.” With repetitive elements, the same calculation must always be performed, something that an application on the computer can do much faster than a structural engineer by hand. “Once the repetition is found in a construction, letting a computer do that calculation allows one to save a lot of time,” says Peltenburg.

## RFEM and VIKTOR

MH Poly uses the calculation software RFEM to calculate the fender systems. Louis was very interested in creating an application for fender systems and asked Peltenburg to work on this together. The application they created contains an interface between the RFEM software and the VIKTOR platform. Peltenburg: “We offer an API to all our customers, on which applications can be made. This was the first time that it has been taken on in a professional manner.”

![Dlubal and VIKTOR.jpg](/uploads/Dlubal_and_VIKTOR_6fb3708ac2.jpg)

## Automating engineering

In the fall, the application will be presented on the MSc Engineering customer day. “We want to show our customers that the sector is changing and what possibilities there are in terms of automation.” Peltenburg says. Louis, “This application is a pilot to see how it goes, but it is my intention to collaborate more if everything goes well.” 

“The biggest advantage of using VIKTOR is to initiate a change in thinking amongst engineers. There is still a lot of traditional thinking, which has to change to a way that makes better use of ICT developments in the field of engineering”, says Louis. Peltenburg adds, “The nice thing is that you don’t know what will happen in the future. There are so many options it can be used for: [Artificial Intelligence](https://www.viktor.ai/blog/40/artificial-intelligence-machine-learning-engineering-construction), [Parametric Design](https://www.viktor.ai/parametric-design-software), [Machine Learning](https://www.viktor.ai/blog/40/artificial-intelligence-machine-learning-engineering-construction).”

*MH Poly is an engineering company that has been using the computational analysis software RFEM from Dlubal for years. This software is provided by MSc Engineering. Emiel Peltenburg is a specialist at MSc Engineering and distributor of the software RFEM and IDEA StatiCa.*

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Advantages of integrating RFEM calculation software with a cloud application

1. Why the Engineering and Construction industry is falling behind
2. Key components for a successful digital transformation
3. Stages of starting a digital transformation
4. Implementing your first digital transformation
5. What digital transformation means for you

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By 2021, the Engineering and Construction industry is one of the fastest growing sectors. Even though the sector is growing rapidly, it is still amongst the least digitized industries around the world. This is not due to the lack of supply of digital solutions, but rather due to the fact that the adoption of new technologies is not forthcoming within the industry. Finding ways to boost this adoption is both a big challenge and opportunity. Therefore, it is important we start at the right place.

There are three aspects to digital transformation: first people, then process, and after that technology. Adopting new digital solutions in your organization is impossible when your people are not on board with it. That is why convincing your people about the benefits is the first priority in digitally transforming your organization. Instead of looking at the digital solution from a technology perspective, you have to look at it from a people perspective. Your digital solution should never be implemented for digital’s sake. It is there to help the people in your organization do their job easier, quicker, better, and with more pleasure. After they are convinced that it will do just that, digital transformation can start!

In this white paper, it is explained how you can start digitally transforming your organization whilst keeping people at the centre of your digital solution. 



See how you can boost digital transformation in your company in the E&C industry by putting people first and technology second!

Download the White Paper and get INSPIRED

Read all about how you can boost digital transformation for your company in the Engineering and Construction industry by centralizing digital solutions around people, instead of around technology. 

People First, Tech Second: The Way to Boost Digital Transformation in the E&C Industry

## The problem of different versions
With the existing scripts it is possible to read input Excel files consisting of cone penetration test (CPT) data, which are converted to a suitable input for the D-Foundations software. An external package within Python was used for this, however, the different versions of this external package soon became a major obstacle. Colleagues started sharing the Python scripts while they were not all using the same version, making the scripts unusable. A lot of time is wasted trying to debug and fix such problems.

## The solution of the Viktor application
The VIKTOR application incorporates these scripts and ensures that all users are working with the same version of the external Python package. Furthermore, the Excel files are centralised in the database and do not need to be sent back and forth between colleagues. This means time savings as well as a reduction in the chance of errors.

> “It’s amazing that we are able to develop an application within 1 day, which ensures that everybody uses the same analyses and models. This saves time and reduces the chance of errors.” - Tjeerd Hermsen, BIM manager at Sweco


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Custom Python scripts in VIKTOR to save time and reduce chance of errors

### 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!

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. 

This guide will provide actionable insights that are useful for anyone involved in creating custom software tools. For example:

- 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 interesting in the process of creating awesome software tools

Learn the process to build apps that provide real value to your work and that other people will definitely want to use as well.

Build successful applications

Learn how to build apps that provide real value to your work and that other people will definitely want to use as well.

Building Successful Applications

> “It’s amazing that we are able to develop an application within 1 day, which ensures that everybody uses the same analyses and models. This saves time and reduces the chance of errors.” - Tjeerd Hermsen, BIM manager at Sweco

## Batch analysis in custom online tools

At VIKTOR a lot of experience has been gathered over the years regarding the analyses of (batch) calculations in a custom online tool. The input is often desired to be variable, which means a lot of different scenarios (models) can be evaluated with a lot of computing power. 

### The VIKTOR application

VIKTOR offers an easy solution. Models can be uploaded to the VIKTOR interface and the variable input can be specified. All it then takes is to press the run button. The models are then generated and evaluated on the server. While this happens, your laptop stays available for the next Zoom meeting, and a notification is given when the results are ready. Go ahead and grab a cup of coffee.

![batch analysis in custom online tool.png](/uploads/batch_analysis_in_custom_online_tool_65e75f31ce.png)

### Numerous possibilities

An example integration could be analysing a D-Foundations model with a large amount of cone penetration test (CPT) data. In no time, hundreds of bearing capacity calculations for various locations are performed. Or perhaps you would like to analyse a structural model in SCIA Engineer, with a variation in geometry / loads / stiffnesses, or assess the reinforcement configuration in IDEA StatiCa. Implementing these functionalities can be done in VIKTOR with very little effort.

![The internal forces of a tunnel cross-section and the reinforcement capacity.png](/uploads/The_internal_forces_of_a_tunnel_cross_section_and_the_reinforcement_capacity_de65e4635e.png)


## Interested?
Integrate your current software with our standard integrations, or we can assist to set this up for you.

![Software that is integrated with VIKTOR](/uploads/logos_220ecc3614.png)

“With the integration between IDEA StatiCa and the VIKTOR plaform, our customers can perform a lot more calculations in less time.” - Jo Gijbels, owner of Whitebox Engineering Software



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Generate hundreds of calculations in an instant with Cloud Computing

Artificial Intelligence

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Build clear and interactive dashboards to display your data. More info on VIKTOR.ai.

Display your data on through interactive Dashboards with VIKTOR

Due to the continuously growing amount of data, it is becoming more and more important for organizations to find ways to quickly extract useful information from large datasets. There are lots of software packages, Python libraries, and tools that you can use for this (think: Matlab, Excel, Numpy, Plotly, Matplotlib, Pandas). If you manage to make this complex data more accessible, usable, and understandable, you can not only obtain a lot of valuable insights, but also enhance decision-making, anticipate errors, compare results, and do much more to improve your organization’s performance.

The hard part often lies not with the extraction of information, but with the ability to make this information useful by, for example, visualizing it in the form of clear, effective, and interactive graphs, plots, tables, and infographics. For this, a user-friendly Graphical User Interface (GUI) is unmissable. And that is where VIKTOR comes into play. 

Turning data into valuable information

VIKTOR provides a user-friendly user interface with a rich collection of Python digital building blocks that can be used to quickly create all kinds of interactive visualizations, using your preferred library (Matplotlib, Plotly). With a VIKTOR application, you can turn your data into understandable insights through an interactive and customizable dashboard.

![visualizing data with VIKTOR](https://cms.viktor.ai/uploads/visualizing_data_with_VIKTOR_613259694c.svg)



In this article, we discuss how you can turn your data into valuable information with VIKTOR by integrating different Python libraries. The application shown in this article is a VIKTOR app that consists of four different pages, all doing something different with data. 

![Top navigation data science app](https://cms.viktor.ai/uploads/Top_navigation_data_science_app_95cefbe701.png)



In the app, users can easily switch between the pages by clicking the corresponding tab in the top navigation:

- CSV to Matplotlib visualization
- Plotly 
- NumPy interpolation
- Pokémon with Pandas

![Matplotlib, Plotly, Pandas, Numpy, Python](https://cms.viktor.ai/uploads/Matplotlib_Plotly_Pandas_Numpy_Python_0ab138fe50.png)

Visualizing data with VIKTOR

In this first page of the application, it is possible to upload CSV files to create Matplotlib visualizations. Matplotlib is an open-source plotting library for Python that can be used to create static, animated, and interactive visualizations. 

In the application, users can download an example CSV file about Pokémons by pressing the dark grey button ‘Download CSV example’. You can find the Pokémon data [here](https://abichat.github.io/minesnancy-visu.html).

After uploading and selecting the desired CSV file, you can plot its data to a Matplotlib using the parameters ‘X axis’ and ‘Y axis’. After defining the parameters, VIKTOR automatically plots the data and visualizes it in the view on the right.

![CSV to matplotlib python](https://cms.viktor.ai/uploads/CSV_to_matplotlib_python_4d9ce5ec74.png)



Here, you can see how the data from the CSV file is visualized in the scatterplot in VIKTOR with a few lines of Python code:

```python
try:
	buffer = params.csv_page.file_link.file.open_binary()
	df = pd.read_csv(buffer)
	df.plot(kind='scatter', x=params.csv_page.options_x, y=params.csv_page.options_y)
except ValueError as err:
	raise UserException(err)

buffer.close()
```

CSV to Matplotlib visualization

With VIKTOR, it is super easy to use integrations with software like Plotly. Plotly is an open-source, browser-based data visualization library for Python. If you are looking for a way to build a clear and intuitive GUI for your data visualization, you can easily combine Plotly with VIKTOR in just 3 lines of Python code.

```python
@PlotlyView(''Results'', duration_gues=3)
def plotly_visualization(self, params, **kwargs):
	''''''Use the build-in gapminder dataset from plotly to create a plot. ''''''
	dataframe = px.data.gapminder()
	fig = px.scatter(dataframe, x=''gdpPercap'', y=''lifeExp'', animation_frame=''year'', animation_group=''country'', log_x=True, size_max=45, range_x=[100, 100000], range_y=25, 90])
	return PlotlyResult(fig.to_json())
```

![data science plotly python](https://cms.viktor.ai/uploads/data_science_plotly_python_199013058a.gif)

Plotly

This example shows how you can easily create interactive apps using NumPy and VIKTOR. 
NumPy is an open-source Python library that offers comprehensive mathematical functions, random number generators, linear algebra routines, and more. 

In this example, users can pick samples form a sin function and use NumPy to estimate an interpolation with given polynomial and check the error at a certain X value. In the application, you can use the slider to first define the number of samples. Then users can select ‘show interpolation’ and use another slider to define the polynomial. Finally, by using a third slider, users can check the error on certain locations. The error is shown on the data view on the right side of the application. 

![NumPy python data science](https://cms.viktor.ai/uploads/Num_Py_python_data_science_ce70d4190c.png)



Here you can see how NumPy is used to plot an interpolation with a given order polynomial in VIKTOR:

```python
# Interpolate over sinus curve
polyfit = np.polyfit(x_value, np.sin(x_value), params.numpy_interp.polynomial)
polyfit_function = np.poly1d(polyfit)
y_interpolated = polyfit_function(params.numpy_interp.x)
```

NumPy interpolation

This last example shows how easy it is to manipulate data using a combination of Pandas and VIKTOR. 

In this page, we use the same Pokémon database as in the first example (Matplotlib). Here, the Pandas open-source Python library is used to manipulate data by first removing all data-entries that are missing a value for its type. Then, VIKTOR automatically creates a correlation matrix that is used to plot a heatmap of the % of Pokémon that have two (or more) types. 

The input field at the left side of the applications allows users to define the types they want to compare. By clicking on ‘select all’, a full plot can be generated after pressing the update button. 

![Pandas Python Matplotlib data science](https://cms.viktor.ai/uploads/Pandas_Python_Matplotlib_data_science_4f51767a43.png)



Here you can see how easy it is to read a CSV file with Pandas in VIKTOR:

```python
dataframe = pd.read_csv(Path(__file__).parent / 'datasets' / 'pokemon.csv').dropna()
```

Pokémon with Pandas

If these examples sparked your interest, there are two ways in which you can use them yourself:

1. By downloading the open-source code from the VIKTOR GitHub repository and integrating it with your own Python code. You can find the entire code [here](https://github.com/viktor-platform/sample-data-science). 
2. By requesting an account for the VIKTOR demo environment. The demo environment offers a collection of 'sample' applications like this that you can use to better understand VIKTOR or as inspiration for your own VIKTOR applications. You can request an account for the demo environment [here](https://www.viktor.ai/demo-environment).

Would you like to build VIKTOR applications instead? No problem! Request a free VIKTOR account [here](https://www.viktor.ai/start-building-apps). 

Open-source Python code

Data science adds value to a business on all kind of aspects. It is only important that an organization has the tools to use their data well. To maximize the value of your data, it is crucial that you are able to interpret the extracted information clearly. By visualizing your data by means of interactive graphs, tables, and dashboards, you can communicate your findings and make them usable and understandable to anyone.


VIKTOR is an application development platform that enables people to rapidly build their own applications with a Python Software Development Kit (SDK) with digital buildings blocks that can be used to create all kinds of interactive visualizations, using your preferred Python library. With a VIKTOR app, you can interpret data quick and easy by means of understandable insights, provided on an interactive and customizable dashboard which enables you to turn your knowledge into assets!


Conclusion

Visualize your data with Python and VIKTOR

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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What exactly is artificial intelligence? 
What are the four types of AI? 
How does artificial intelligence differ from machine learning? 
And what are the different types of ML?

In this White Paper, we will answer all of these questions and more about the topic.

Find out how you can use AI to improve and automate (engineering) processes within your organization and learn how to build a machine learning application yourself as we guide you through the following chapters:

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

VIKTOR Blog

Visualize your data with Python and VIKTOR

Advantages of integrating RFEM calculation software with a cloud application

Custom Python scripts in VIKTOR to save time and reduce chance of errors

Generate hundreds of calculations in an instant with Cloud Computing

Customer Cases

White Papers & Guides