Creating Apps & Python

Digital transformation

## Pile foundation design with D-Foundations

Most companies that are involved in designing and building pile foundations need to perform many analyses to ensure that their foundation designs meet the safety standards as described in the NEN. Commonly, D-Foundations software is used to perform these calculations. 

D-Foundations is a software-package, created by Deltares: An academic institute that focuses on Hydro- and Geotechnologies. It is mostly used in the Civil and Architecture sectors. With D-Foundations, you can calculate the required depth of a set of specified foundation piles, placed in a particular soil composition (based on GEF files) and under a certain load. Using location-specific soil information, these kind of foundation calculations can be applied to many different constructions in the Building and Infrastructure sector, such as a factory floor for heavy machinery, a tall apartment building, or even the foundations for wind turbines and high voltage pylons. 

![D-foundations software package.jpg](/uploads/D_foundations_software_package_426dca58f8.jpg)

## Analyzing, analyzing, analyzing

Normally, to find the most optimal and cost-effective design, a Geotechnical Engineer needs to analyse a great number of possible designs. This is a time-intensive and tedious process, which is very prone to error as well. With today’s high demands on short delivery times, there is only time to evaluate a handful of designs. Because of that, the best design is not always found within that time frame.

## More analyses in less time 

To be able to run more D-Foundations analyses in shorter time, a binding between the D-Foundations software and the VIKTOR platform is made. The working principle of this binding will be explained in more detail later on in this article. 

With this binding, a D-Foundations analysis can be ran with just a few lines of Python code (see example code below). We can take the necessary input data, which is already centralized in the VIKTOR application upon uploading, and run multiple different D-Foundations analyses, without any human interaction needed. This means: No need for opening the GUI of D-Foundations and going through each of the steps manually!

![Blog_automate_processing_with_D-foundations_3](https://cms.viktor.ai/uploads/D_foundations_analysis_ran_in_few_lines_of_code_97269b2df3.png)


## Using D-Foundations

Similar to other software packages of the Geo-technical suite of Deltares, D-Foundations uses a set of input, settings, and dump files to store the in- and- output data of the calculations. These input files can be analyzed by D-Foundations in batch-mode through the command line, which will then create a dump-file with the results of the analysis. These files, however, are not very easy to understand and even harder to write yourself.

Therefore, the following automated workflow was devised:

1. Generate a correct D-Foundations input file using Python
2. Send this file to the server where D-Foundations is installed*
3. Run a D-Foundations analysis on the input file and send the data dump-file back to the application*
4. Visualize or output the data where necessary

*This requires no interaction from the user, nor does it require any coding from a developer, as this functionality is build into the VIKTOR platform

Points 2 to 4 are quite generic, so the focus of this article will be on the first point: Generating the input file. 

## Generating the D-Foundations input file

The combination of the filled-in VIKTOR web interface and the soil information that has been uploaded to the platform database hold all the information needed to perform a D-Foundations calculation. It is a matter of compiling all this data in the correct format into a D-Foundations input file. While the general concept of compiling a text-based input file is quite easy, the hardest part of creating a valid input file was the reverse-engineering of all potential input options and finding which specific pieces of information needed to be put where. 

The current average D-Foundations input file that we work with can easily contain over 100,000 lines of text. Therefore, putting the right information in the right place is essential. Unfortunately, no detailed documentation of the input file layout is available, so this process took significant time to complete. An added complexity was that the layout of the input file would change significantly, depending on which options were selected. This required numerous edge-cases to be evaluated and a lot of logic to be implemented.

In order to ensure the input file is correctly generated, the following steps have been executed:

1. GEF files (containing soil information) are analyzed and corresponding soil layouts are defined
2. The properties of the selected pile type (selected by the user) are extracted from the database
3. The grid of piles is defined, which includes the pile's locations, height, properties and loads 
4. Any additional calculation options, selected by the user in the web interface, are collected
5. All data collected in steps 1 to 4 are inserted into a generic template (.foi file), which is then rendered as the final input file

The innovative part is making the binding generic enough in such a way that it can be applied over a wide range of different projects/designs. This makes the binding future proof and much more valuable to current and future customers. 

## Ways to use the D-Foundations file

Once the D-Foundations file has been created, it can be used in several ways:  

1. The user could download the generated input file, using a download-button in the VIKTOR application. This file could then be imported into D-Foundations manually in order to visually inspect the D-Foundations model and review the report generated by D-Foundations. 
2. The user could also have the VIKTOR application run the D-Foundations analysis on an external server, after which the results are send back to the application and can be output to the screen. This way, the user can quickly analyse a design without ever having to open the D-Foundations software package.

Both these options are generic functions that are part of the generic VIKTOR platform and are therefore available but not limited to the D-Foundations software package.

![Downloading D-foundations input files in a zip-file](/uploads/Downloading_D_foundations_input_files_in_a_zip_file_27781ea934.png)
*Download D-Foundations input files in a zip-file*

![VIKTOR interface for automation in D-foundations](https://cms.viktor.ai/uploads/VIKTOR_interface_for_automation_in_D_foundations_526f7e67c9.png)

*Run the D-Foundations analysis directly through the VIKTOR platform and visualize the result*

## Advantages of binding in D-Foundations

Binding D-Foundations with the VIKTOR platform has various advantages, like: 

- **Single source of truth**: By using the input data from the VIKTOR application, there is always a single source of truth for the analysis. This means no more random data files floating around on different computers or drives. Any changes to these files are logged centrally, so everyone has the most up-to-date data available at all times. 
- **Efficiency**: The binding enables running a great number of analyses in series or even in parallel. In most cases, the whole process of building a D-Foundations model, running the model and then processing the results takes around 5 seconds. This is easily 10 times faster than the average Geotechnical Engineer can setup and run a D-Foundations analysis.
- **Risk reduction**: Once validated, the binding performs all required actions consistently, hence significantly reducing the risk of errors made in the calculations.
- **No extra licenses**: The binding uses the clients own software package and licenses, so the client doesn’t need to purchase additional software or licenses. 
- **Easy to use**: Not only is the binding easy to use for the end-user (who now only has to worry about a handful of input parameters), the engineers and/or developers can write a working script in about 10 lines of code. The binding follows the same structure as the D-Foundations user interface, which is familiar for anyone who has used this software before. This allows engineers/developers to quickly build a user-friendly application, enabling any colleague to perform D-Foundations calculations easily.

## Potential for cost and quantity

These advantages offer a lot of potential for a company, both in terms of costs and quantity:

- The reduction in time allows companies to evaluate more foundation designs and find a more cost-effective design in the same time-frame.
- The reduction in time allows companies to increase their output by taking on additional design projects and completing more projects in the same time-frame.

## Endless possibilities

The binding that has been written for D-Foundations has been set up in such a way that it is as generic as possible. This allows it to be applied to many different varieties in the design of pile foundations. This opens up the possibility of evaluating a great many different designs in an optimization scheme. 

The binding is very user-friendly and follows roughly the same steps as setting up a D-Foundations calculation through the D-Foundations user interface. Therefore, writing the code to create a D-Foundations input file should be very straightforward for an engineer or developer who has used the software package before.

## D-Foundations and VIKTOR

The VIKTOR platform ensures that the data required for the D-Foundations calculations is all centralized in a single place. It also handles all data-traffic between the application and the server with the D-Foundations software package. This way, the Geotechnical Engineer doesn’t even need to have the software or the input data installed on his device to be able to perform D-Foundations calculations and analyses!



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Automate the processing of pile foundations with D-Foundations and Python

### 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..)
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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

Parametric design

## GRLWEAP

With the software program GRLWEAP you can perform a driveability analysis. To do so, you enter the data of the soil (the probing), the hammer and the pile you want to use. It will then calculate for you how long the pile driving will take and to what extent you can get the pile into the ground. In itself there is nothing wrong with the analysis, it is just that everything has to be manually entered into the program, which is a time-consuming task that is also prone to human error.

## Parametric design

The goal was therefore to design a [parametric design tool](https://www.viktor.ai/blog/46/parametric-design-cloud-engineering-construction) in which the input no longer has to be done manually. This means that not every possibility has to be entered separately and therefore multiple analyses can be done in a short period of time. Performing automated batch calculations in GRLWEAP was something that had not been done before, so everything had to be figured out from the start.

### Parameters

First, the input file had to be figured out. Which parameters do you want to control, so which variables are needed? Next, it was figured out how to get them into the input file and then write Python code to automatically build input files. These variables can be adjusted to the wishes of the user.

### Automation

In GRLWEAP a ground structure is used, which is normally filled in manually by a geotechnical technician. He analyses the soil information to define the structure. The [parametric design tool](https://www.viktor.ai/blog/46/parametric-design-cloud-engineering-construction) itself can interpret these probes and classify a soil structure. The soil structure is then translated (with some formulas) into a ground structure that GRLWEAP can read. Because a ground structure has to be defined per analysis, it was a time-consuming process to determine it manually. The automation therefore saves time.

![GRLWEAP.png](/uploads/GRLWEAP_cd39f05275.png)

### Optimization

The real time gain lies in the batch analysis. You can enter different probes, types of hammers and types of piles and the tool will calculate all possible combinations. All results are collected and made understandable by means of tables. Here the optimal combinations can be extracted by the engineers.

## Future opportunities

The current tool is suitable for various pile driving problems, such as office buildings, tunnels or houses. To make the tool more interesting and specific for offshore, other soil investigation programs could be added. This is because there are not always probes available, something on which the current tool depends. An example could be Rockworks.

See how you can [create your own parametric design tools for automation and optimization](https://www.viktor.ai/parametric-design-software) as well.



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A parametric tool for automated pile drivability analysis with GRLWEAP and Python

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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The Future of Parametric Design is COLLABORATION

## Civil engineering automation

A structural design process consists of several or perhaps many design iterations. For each iteration, a civil engineer designs a structural model and uses structural analysis software, such as SCIA Engineer, to perform an assessment. If the SCIA software indicates that a structure does not meet the requirements or if some input has changed, this cycle is repeated. Therefore, this can be a very time-consuming process. The more manual actions, the greater the risk of errors. 

Time and risk can be significantly reduced by automating the above steps. VIKTOR offers a binding with SCIA Engineer that enables civil engineers to calculate structural models at the push of a button, using a [parametric Python model in the Cloud](https://www.viktor.ai/blog/46/parametric-design-cloud-engineering-construction), without even having to open the SCIA interface. 

## Open-source Python library

VIKTOR developers have encountered multiple civil engineering design projects for which a SCIA Engineer model had to be created. Using all this knowledge, a growing Python library with digital building blocks came to be. At some point this library got included in the [VIKTOR SDK](https://docs.viktor.ai/docs/reference/sdk/intro), making it publicly available.

In addition to the Python library, an integration with SCIA itself was developed that allows the civil engineer to automatically perform analysis within a user-friendly VIKTOR app without having to open the SCIA interface at all. 


## Design automation with SCIA

### Creating a SCIA model with a few lines of Python code

Building SCIA models using the VIKTOR SDK is done by writing object-oriented Python code; no knowledge of the SCIA Engineer API is required. All the relevant Python building blocks can be found in the module [*viktor.external.scia*](https://docs.viktor.ai/docs/reference/sdk/api/external/scia), of which the starting point is the *SciaModel*. 

Let’s start by creating a new instance of this class, to which subsequently desired elements (e.g. nodes, cross-sections, beams, supports, loads etc.) can be added. 

The example below shows what this could look like in Python code:


```python
Def create_scia_model(self, params) --> SciaModel:
Model = SciaModel()


# create nodes
Model.create_node(‘K:1’, 0, 0, 0) # origin
Model.create_node(‘K:2’, params.geometry.piles.x1, 0, 0)
Model.create_node(‘K:3’, params.geometry.piles.x2, 0, 0)
…
Model.create_node(‘K:n’, params.geometry.piles.floor.width, 0, 0)


# create pile beams
Model.create_beam(‘S:pile1’, top_pile_1, bottom_pile_1, cross_section)
…


Return model
```


We often recognise that it is difficult to verify whether the developed Python code actually builds up the correct SCIA Engineer model. That is why we recommend creating a visualization of the model within VIKTOR simultaneously. The visualization is not only helpful for visually verifying the model, it also serves as a very nice design overview for the end user! The method defined above can be used in a generic visualization method as shown below:


```python
@GeometryView(“SCIA model”, duration_guess=3)
def create_visualization_geometries(self, params, **kwargs):
scia_model = self.create_scia_model(params)


# nodes
geometry_group = Group([])
for node in scia_model.nodes:
node_obj = Sphere(Point(node.x, node.y, node.z), 1)
geometry_group.add(node_obj)


# beams
pile_diameter = params.geometry.piles.diameter
for beam in scia_model.beams:
point_top = Point(beam.begin_node.x, beam.begin_node.y, beam.begin_node.z)
point_bottom = Point(beam.end_node.x, beam.end_node.y, beam.end_node.z)
beam_obj = CircularExtrusion(pile_diameter, Line(point_top, point_bottom)
geometry_group.add(beam_obj)


…


return geometry_group
```


By using the geometric VIKTOR Python building blocks [(*viktor.geometry* module)](https://docs.viktor.ai/docs/reference/sdk/api/geometry), the elements of the structural SCIA Engineer model can be easily converted to geometric elements and passed to a geometry result. 


### Visualizing structural models in the SCIA interface

It is always useful to manually load the created SCIA Engineer model in the SCIA Engineer interface, which will help to verify that, for example, all the loads are correctly defined in terms of placement and magnitude.

To visualise the created structural model in the SCIA interface, the following three files are needed:

- model file (*.esa)
- definition file (*.def)
- input file (*.xml)

The model file can simply be an empty project file. The other two files can be easily generated by calling the method *generate_xml_input()* on the SciaModel created above. It is probably a good idea to develop a download functionality such that the end user can manually download the input files as well:


```python
Def download_scia_input_xml(self, params, **kwargs):
Scia_model = self.create_scia_model(params)
Input_xml, _ = scia_model.generate_xml_input


Result = DownlaodResult(input_xml.getvalue(), ‘test.xml’)
Return ViktorResult(download_result=result)
Def download_scia_input_def(self, params, **kwargs):
M = SciaModel()
_, input_def = m.generate_xml_input()
Result = DownloadResult(input_def.getvalue(), ‘viktor.xml.def’)
Return ViktorResult(download_result=result)
```

The next step is to load the three files into the SCIA interface (*File > Update > XML file*) and verify the created model.


## Analyzing the structural SCIA model within VIKTOR

In addition to the binding to build up the SCIA Engineer structural model, the [*viktor.external.scia*](https://docs.viktor.ai/docs/reference/sdk/api/external/scia) module also offers an analysis Python building block (*SciaAnalysis*) which sends the required input files to the so-called *SCIA worker*, which runs on a designated (virtual) machine and executes SCIA. When the analysis has been completed, the worker sends the result file back to the web application. The OutputParser (from viktor.external.scia) can be used to easily extract specific results from the result file. 

Below you can find an example of the result processing and the associated visualisation.

```Python
@GeometryAndDataView(“SCIA Result”, duration_guess=60)
Def run_scia(self, params, **kwargs):
Scia_model = self.create_scia_model(params)


# create input files
Esa_path = Path(__file__).parent / ‘scia’ / ‘model.esa’
Input_esa = BytesI0()
With open(esa_path, “rb”) as esa_file:
Input_esa.write(esa_file.read())


Input_xml, input_def = scia_model.generate_xml_input()


# analyze SCIA model
Scia_analysis = SciaAnalysis(input_xml, input_def, input_esa)
Scia_analysis.execute(300) # timeout after 5 minutes
Scia_result = scia_analaysis.get_xml_output_file()


#parse analysis result
Parser = OutputParser(scia_result)
Table_element = parser.get_table_element_from_name(“Reactions”, parent=’Combinations – C1’)
Df = parser.convert_table_element_to_data_frame(table_element


Max_rz = float(max(df[‘R_z]))
Data_result = DataGroup(
DataItem(‘SCIA results’, ‘, subgroup=DataGroup(
DataItem(‘Maximum pile reaction’, max_rz, suffix=’N’, number_of_decimals=)
))
)


Geometry_group = self.create_visualization_geometries(params, scia_model)
Return GeometryAndDataResult(geometry_group, data_result)
```


*An example of the result processing*

![blog_python_scia_example_associated_processing](https://cms.viktor.ai/uploads/blog_python_scia_example_associated_processing_c130288e81.png)

*The associated visualization*

This video shows how the parametric geometry is build within in VIKTOR and what it looks like when SCIA Engineer is used to analyze the structural design from within the app, using Python. The model is downloadable so that results can be viewed in SCIA Engineer or directly within the web app. With the app, civil engineers are sure to always work with the latest structural model, perform fast structural analysis to save time, and get clear results and rich visualisations. 

<div style="padding:58.68% 0 0 0;position:relative;"><iframe src="https://player.vimeo.com/video/656990782?h=8f1a608832&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="SCIA and Python for parametric models in the Cloud"></iframe></div><script src="https://player.vimeo.com/api/player.js"></script>


## A total design solution

The SCIA binding developed by VIKTOR allows for an easy conversion between a [parametric model](https://www.viktor.ai/blog/46/parametric-design-cloud-engineering-construction), to a meaningful input for SCIA Engineer, using only Python. 

A VIKTOR worker takes care of a connection between the web application and the structural design and analysis software, such that SCIA can be executed from within the browser. 

After the structural analysis, the results are returned and can be visualised on top of the model visualization. This means that the manual tasks of constructing the structural design for a SCIA Engineer model in the interface, analysing the structural model, and assessing the results can all be done by the civil engineer with a simple click of a button from within the VIKTOR interface!

See how you can [integrate with your preferred software](https://www.viktor.ai/platform), using only Python and how you can use [parametric design in the Cloud](https://www.viktor.ai/parametric-design-software) to build civil engineering apps!

![SCIA scan.png](/uploads/SCIA_scan_dd8182e4da.png)

See how you can build your own apps to automate civil engineering processes:

[![CTA_try_for_free](https://cms.viktor.ai/uploads/CTA_try_for_free_6af1b46cf2.svg)](https://m.viktor.ai/free_version)

## Related content

A list of articles about integrations with civil engineering software and structural design & analysis software that you might find interesting as well:

- [GeoTools](https://www.viktor.ai/blog/14/web-application-python-geotechnics)
- [RFEM models in the Cloud with Python](https://www.viktor.ai/blog/9/RFEM-automation-python-app)
- [Dynamo and Civil3D](https://www.viktor.ai/customer-cases/14/python-dynamo-civil3D-MXroad)
- [PLAXIS models in the Cloud with Python](https://www.viktor.ai/blog/34/integration-plaxis-python-parametricdesign)
- [Pile foundation processing with D-Foundations and Python](https://www.viktor.ai/blog/21/automation-python-D-foundations-web-application)
- [Pile driveability analysis with GRLWEAP and Python](https://www.viktor.ai/blog/22/parametric-design-python-GRLWEAP-pile-driveability)
- VIKTOR also offers integrations with AutoCAD for civil engineering. Go the our [platform page](https://www.viktor.ai/platform) for more information.




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Using Python and SCIA to create parametric models in the Cloud

## No more copy + paste

Roland Snelders has been involved in the development of the concrete slab reinforcement application for Heijmans. “We could read the internal moments from a SCIA file and these were collected in the report, usually in a table. Then those moments could be entered in Excel and the reinforcement was determined and tested in an iterative process. That calculation was therefore copied to the report. ” This way of working could be done more efficiently. It would be easy if copying numbers is no longer necessary, because everything is collected in one place.

## Excel, SCIA, and VIKTOR

That's exactly what the application they created does. Snelders: “From SCIA we can now export an XML file and read it into the application on the VIKTOR platform. The Excel sheet is also linked to this. After selecting the concrete cross-section data and various reinforcement options, I get a picture with colors that indicate where the reinforcement should be.” This ensures that you go directly from the SCIA file with a once-off data entry to a visualisation of the results that come from the Excel sheet, without having to copy anything.

## Centralized Excel sheets

The Excel sheet has not been completely transferred into the application but rather has been linked to it. This has the advantage that it does not have to be recreated, which would then have to be validated. In addition, all manufacturers know how to make a calculation in Excel, you can still continue to do that. “The application ensures that everyone now works with the same Excel sheet, since it can only be opened centrally,” says Snelders. This significantly reduces the risk of errors.

## Automated calculations

The application can be used in any construction that contains a slab, for example a floor, a wall, a roof or a bridge. “Initially, only rectangular slabs could be calculated and analysed, which was a major limitation,” says Snelders, “This has now been improved to allow for all types of slab shapes to be calculated.” 

This is the beginning in terms of automation. "For the time being it is not going to be possible to calculate entire constructions completely, because there is a lot of variation in every project." Snelders explains, "But it is very useful to have components of such a construction that can be automatically calculated." 

![Customer_case_automating_calculation_process_linking_excel_SCIA_2](https://cms.viktor.ai/uploads/Excel_and_SCIA_connected_c24d123297.png)


## More time for complex challenges

There is less variation in the components themselves, so they can be calculated perfectly with an application. This allows the structural engineer to have more time for the more complex and challenging aspects of their work.

Snelders: “The application makes work more fun. I do not find completing an Excel sheet challenging work. ” When filling in cells and copying data, a structural engineer adds little knowledge to the process. Where they do add knowledge is during the schematisation of the construction and the making of a calculation model, in order to subsequently arrive at an optimal and practical choice of reinforcement. "That work still remains, we have mainly automated the repetitive calculations."

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Automating concrete cross-section calculations by linking Excel sheets to SCIA

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

## Why a hackathon?

Knowledge development is one reason for the IB decided to organize a hackathon. Addiotionally, besides getting acquainted with programming hands-on, they also wanted to experience what it is like working with VIKTOR. 

As some structural engineers, such as Lester Neijzing, are currenlty part of a pilot project by the Gemeente Amsterdam in which VIKTOR is also being used, some are already a bit familiar with the platform. Now, the hackathon offers a great opportunity for the other structural engineers to get introduced to VIKTOR as well, and for those who are already acquainted to improve their programming skills. 

> "Participating in the hackathon is a fun way to get to know VIKTOR and learn more about programming with Python." - Lester Neijzing

![blog_hackathon_amsterdam](https://cms.viktor.ai/uploads/blog_hackathon_amsterdam_ae737a634c.jpg)

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## Quay wall applications

During the two-day hackathon, three teams used Python to develop applications on the VIKTOR platform. All of the apps are to some extend related to the parametric design application that Amsterdam is currenlty developing for the renovation of their quay walls. 

The stakes were high during the event: In total, three apps were developed, but only one could be the winner and would thus be further developed eventually. Next, each app will be described in more detail.

![kademuur renoveren.jpg](https://cms.viktor.ai/uploads/kademuur_renoveren_7f4840c190.jpg)

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### Application 1

Currently, structural engineers at the IB gather data about the state of quay walls in Excel sheets. Even though this process works well for the people who work with these sheets on a daily basis, it is difficult for other to interpret the data. For that reason, team 1 developed an application to make this data more transparent and easily adjustable. 

Of course, the IB could ask their GIS-department to visualize their data for them, but even then it would still remain a static process. With this application, engineers can visualize and adjust project data using different colors on a graphical map themselves. This also helps make it easier to explain to others why the work of the structural engineers is important and how they, for example, estimate workload and project duration.

### Application 2

Justl ike team 1, team 2 also worked on an application for the visualization of project data. With their app, it becomes easier and faster to project geometries on a map view and translate these to the calculation program SCIA, and after that to Word to generate downloadable reports.

Because this app includes a rather far-reaching integration, it was not unfortunately yet at a usable level at the end of the hackathon. On the flip side, this does mean that there is still a lot of room to improve and expand the tool. 

### Application 3

Opposing team 1 and 2, team 3 developed an application not aimed at the visualization of project data, but for the automated design and modelling of foundation piles, channels, and the surrounding soil. For this, the team integrated an Excel calculation sheet with an export to the calculation program SCIA to their VIKTOR application.

Just like with team 2's application, there was still some work to be done before team 3's app was ready to be used on real projects. However, the results were promising so far and made for an interesting concept for further developments.

## Choosing a winner

Based on the judge's critiques, a winner was chosen at the end of the second hackathon day: Application 1. Where the other apps mainly focused on integrating with pre-existing calculation software, application 1 stood out due to its unique use of data-processing and visualization - a nice use of VIKTOR that had not been done that often yet. 

> "I think it was this tool that won the hackathon because the rough outlines were already finished - the application worked in such a way that it produced the results that we were looking for. According to the jury, it was a unique tool, especially thanks to the maps and the visualizations." - Lester Neijzing

## Putting the pieces together

According to team 1 participants Rick and Lester, even though they were part of the same team, they were constantly programming completely different parts of the application. Only within the last few hours of the hackathon, when they began putting all the parts together, it felt like "it magically turned into a working product".

The application consists of several different parts, For example, it shows a graphical map and Excel sheet with risk drilling and assessments of quay walls, using different colors to indicate wether a wall is still sufficient or needs reinforcement. Team 1: "The map view really makes the data come to life". Within the application, it is also possible to adjust source data, link it immediately back to Excel (or the other way around), and export overview reports.

> "We knew what our vision was but did not yet realize how incredible it would be until we put everything together and it actually started working the way we envisioned." - Rick Voortman

![hackathon artikel 2.jpg](https://cms.viktor.ai/uploads/hackathon_artikel_2_b764fb081e.jpg)


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## What's next?

Following the hackathon, two things are certain:

1. The further development of team 1's winning application
2. A second edition of the hackathon

**Are you also interested in organizing a hackathon with your organization? Don't hesitate to contact us at contact@viktor.ai!**



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Structural engineers at the engineering firm of the municipality of Amsterdam organized a hackathon to boost their programming skills and learn about building apps with VIKTOR.

Develop programming skills through hackathon | VIKTOR.ai

Hackathon stimulates knowledge development

VIKTOR Blog