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Hello everyone, today, we are going to look at how to showcase the results of your simulations by generating geographic maps that illustrate your calculations. MithraSIG provides very powerful tools for this, although, admittedly, they are not always the most intuitive. What we will do first is use an existing template to see how to create a printed map with a legend, a north arrow, a scale bar, etc. Then, in a second step, we will see how to create our own template so that all team members can generate results from different projects using the same base map.
So, let’s start with creating a layout from a predefined template. This is done here. You can choose from the different default models available. We’ll keep it simple, for example, by selecting one with a small legend. It might be useful, however, to customize it slightly to better match our company’s branding, rather than using Geomod or CSTB’s template. Once you’ve chosen the template, you’ll then select the area you want to represent. Some default parameters will need to be filled in, such as a title—e.g., "Calculation from January 24"—and other relevant information. You will have full control over these fields each time you create a map, allowing you to customize them as needed.
After entering this information, you can add one or more legends. By default, the software suggests noise level data, but you could instead display train traffic legends if preferred. It’s entirely up to you. I’ll keep it simple and display the noise level legend here. Next, you can choose from the available north arrows. If you want a custom-designed one, you’ll need to contact us to discuss how to do it. Then comes the scale bar selection. You need to choose one that is approximately suited to the scale and position it as desired. If, for example, there is no space at the bottom right, you can place it elsewhere, though it should be done thoughtfully. The goal today is not to create a perfect-looking map but rather to understand the techniques needed to achieve a functional result.
Once your layout is set up, you can further personalize it by adding a logo. This process is not particularly intuitive. Here’s how: you paste an image from a file. In this case, I have a PNG-format logo that I will insert using the "Paste from file" tool. After retrieving the file, I bring it to the foreground, as it is not placed there by default. Then, I resize and move it as needed. Similarly, you can modify the displayed text elements, inserting your own—e.g., "Hello everyone"—and positioning them as you wish. You can also change the font, color, etc., although these options are fairly standard.
A small remark here: earlier, we saw that labels were displayed on our receptors, but they are now missing from the printout. This is a frequent issue. At a small scale, labels are useful, but at a larger scale, they can become irrelevant. You can adjust this in the theme properties by setting a minimum and maximum display scale for the labels. This ensures that labels are visible only when appropriate. You may have noticed that the labels disappeared from the printed version. This is intentional to avoid cluttering your map. However, if you need to force their display, you can do so by right-clicking on the image object, selecting "Properties," and then navigating to the "Layers" tab. There, select the layer associated with the labels. Under "Annotations," you’ll find that labels are disabled by default. Enable them, apply the changes, and validate. The labels will then appear.
Once all these steps are complete, printing is straightforward. Simply select your printer and confirm the print. And there you have it—a properly formatted map using an existing template. Now, let’s see how to create and customize our own templates to match our company’s branding and facilitate sharing among colleagues. This is done within the "Layout" section, specifically under "Create a new print template." Instead of starting from scratch, which is possible but time-consuming, we will modify an existing template.
First, I’ll remove the Cadcorp logo and insert our company logo using the same "Paste from file" method. Again, I resize and reposition it appropriately. Next, let’s talk about dynamic variables. These are the fields that we will fill in each time we create a new map. In this case, they are placed within a table. You can create your own table using standard drawing tools. Once you have your table, add a text field for dynamic input. For example, I create a text box, resize it, and then assign it an attribute called "Prompt." This will be a text field where we enter the creator’s name. This step is not very intuitive, but it’s essential. You’ll see its effect shortly.
To add a scale indicator, we refer to the documentation. It is located under "Layout" and "Print Templates." The instructions detail how to insert a scale formula. For example, using this predefined formula, we can create a text field and insert the scale indicator dynamically. Since the software currently has no data to display, a placeholder appears instead. Now that we have built our custom template, we need to save it. Select the image object, go to "Layout," and choose "Save as print template." Give it a name, such as "Geomod" and "Tuto," and save it.
To test our template, we return to our MithraSIG project. When applying a layout, we can now find our "Geomod Tuto" template. After selecting it, you will see the effect of the dynamic variable: a prompt appears asking for the creator’s name. For example, I enter "David Collado," and the name is dynamically displayed on the map. Similarly, the scale indicator updates automatically. Finally, let’s discuss where this template is saved and how to share it with colleagues. MithraSIG stores a lot of data in the user’s personal directory. You can find the template in AppData > Local > MithraSIG, specifically in the NOL folder, which is a format specific to Cadcorp. Since these settings cannot be stored in the main installation directory, they are saved in your personal user folder.
To share the template with your colleagues, simply copy the Mithra Print Template file and send it to them. This file contains the template you just created. That’s it! Thank you for your attention. I hope this information will be useful in your work. See you soon for another tutorial!

Hello, today we are going to look at how to create custom symbologies or themes in MithraSIG to differentiate entities in a layer based on an attribute or a measured value during a calculation. We will start with a very simple case where I will artificially distinguish different building elements based on their height. To do this, it’s quite simple. We will add a theme to the buildings and choose an interval style since it is a value that varies between a minimum and a maximum, which we will divide into intervals of our choice—here, for example, I can select 4, 5, etc. You make your choice and also decide how to divide them, either by setting equal-length ranges, ensuring an equal number of elements per range, or using a more statistical approach. Once we have chosen the intervals and the method for distributing the buildings, we can see that, by default, the intervals are created with their own styles. These styles can, of course, be customized.
For instance, I can create a gradient where the lower buildings are colored yellow, and the taller ones are colored pink. Of course, I can select more relevant colors if needed, as I have access to a full range of tools to achieve the desired rendering. By selecting all the buildings, I can apply this gradient from the minimum color to the maximum. Once done, I obtain a symbology that differentiates the various building elements. Since we are working with MithraSIG, I will naturally link this technique to an acoustic application. Here, I have a façade noise calculation, and when I add a map to this calculation, I can access the building contour symbology based on the noise measured at the façade. If I choose a point representation instead, I get the same information but displayed as points.
An interesting option is to enable the dBmax checkbox, which gives access to the highest measured value on the building, the façade where it was simulated, and its height. What’s even more useful is that I can transfer this value as an attribute to the building layer. I will name this attribute LdenMax if I decide to work with the Lden value. After adding this attribute, I can check that the LdenMax value has been properly added to the building layer. From this point, I can use this attribute value to differentiate the various buildings.
The first action we can take with this LdenMax attribute is to add a theme based on individual values. We will use the expression generator, find the attribute, retrieve its internal name, and apply a condition. For example, I want to select buildings where Lden is greater than 68 dB. This becomes the default value in my expression. It’s important to note that the style assigned to the value -1 is applied to entities that meet the condition, while the style assigned to 0 applies to elements that do not meet the condition (less noisy buildings). Thus, the noisier buildings will be defined using this condition and a corresponding symbology. I will fill the interior of these polygons with red—this is just a demo, but you can also define an outline. You will see that the buildings exposed to noise levels above 68 dB are now clearly distinguished with a dedicated symbology.
Finally, we will end today’s session with another application of the LdenMax value retrieved from the façade calculation: labeling buildings with this value. As before, we add a theme, but this time, we add an annotation, which allows us to use labels. I have already prepared an expression. This window provides various functions to help generate more advanced expressions, including string manipulation and mathematical operations. Today, we will simply define an expression to display labels with the LdenMax value, rounding it to one decimal place and appending the dBA unit.
It’s quite straightforward. A summary is provided here, and we can further customize the labels, for example, by coloring them pink for a unique touch. Again, there is an entire set of tools available to refine the labels. You will notice that, at first, the display is quite cluttered because we haven’t set a scale for when the labels should appear. We can adjust this in the theme’s properties by defining a minimum display scale. Once applied, I can zoom in on my map, and when I reach a zoom level beyond 300, my labels will display the maximum noise value, as specified by the formula.
This concludes our introduction to thematic mapping in MithraSIG. Thank you for your attention, and I’ll see you in the next tutorial!

Hello, building on the integration of the BD Topo dataset we covered in the last tutorial, today we will look at how to quickly and efficiently integrate TMJA (Average Daily Traffic) data to define traffic levels for each road where information is available. For this exercise, we will also explore a lesser-known feature of MithraSIG: the table view. We will simulate TMJA data, integrate the roads, and assign artificially determined traffic levels so that you can clearly see the necessary steps.
Before we start, I’d like to mention our online manual, which can be a great help when you encounter issues. The relevant section for today's tutorial can be found at data.geomod.fr, under MithraSIG version 5 help/fr. By searching for “functions” on the homepage and navigating to Expressions > List of Expressions, you will find a helpful reference sheet that lists useful functions and properties, which we will leverage in the table view.
Now, let’s return to our road data. We have an attribute called importance, which contains three values in our dataset: 3 (highlighted in red using a thematic style), 5 (symbolized in black), and 6 (displayed in light blue). A key detail we need is the exact name of this attribute. In MithraSIG, text fields are identified with a $ suffix, while numerical fields are marked with a # suffix, as seen in the width field.
Now, let's open the table view, select our filtered roads layer, and add a new column named TMJA, which will store integer values. The new column appears at the end of the table. We will now assign TMJA values based on the road importance: 1000 for roads with importance = 3, 500 for roads with importance = 5, and 100 for roads with importance = 6.
To do this, we use the fill tool to update the TMJA column. We write a conditional expression similar to an IF function in Excel. The syntax in MithraSIG is as follows:
iif(importance$ = "3", 1000, 0)
This expression means: If the importance value is "3", assign 1000; otherwise, assign 0.
A small red warning appears because the importance field is stored as text, not a number. To fix this, we ensure that 3 is treated as a string by enclosing it in quotes. After validating, we see that all roads with importance = 3 now have a TMJA of 1000.
We can verify this by selecting a row, checking the importance column, or zooming in on the corresponding road segment on the map. Indeed, the selected road appears red, confirming that it has the expected TMJA value.
Next, we apply the same logic for roads with importance = 5. We modify our expression:
iif(importance$ = "5", 500, TMJA#)
This means: If the importance value is "5", assign 500; otherwise, keep the existing TMJA value.
Be careful not to overwrite the previously assigned values. Alternatively, we could nest multiple IF (iif) conditions, but that would make the expression less readable. After validating, we check that roads with importance = 3 still have TMJA = 1000 and roads with importance = 5 now have TMJA = 500.
For roads with importance = 6, we could apply the same logic, but for simplicity, we will leave their TMJA at 0. At this point, we now have TMJA values assigned to all roads in our dataset.
Now that we have TMJA values, we need to integrate traffic attributes along with the road geometry. MithraSIG requires specific fields to perform noise propagation calculations, such as light vehicle traffic flow, heavy vehicle traffic flow, and speed limits.
For traffic flow, standard formulas are typically used to derive values from TMJA. Based on existing guidelines, we divide the TMJA by 15 for light vehicles and by 25 for heavy vehicles. For speed limits, the IGN dataset provides this information in the second-to-last column.
Now, let’s calculate morning traffic flow for light vehicles. In the table view, we add a new attribute called Morning VL Flow (VL = Véhicules Légers). Since it is a division, we store it as a decimal number. We set its value to:
TMJA# / 15
After validating, we verify that the new column contains calculated values. This process can be repeated for evening traffic flow and heavy vehicle flow. However, for this tutorial, we will keep it simple and proceed with only the morning light vehicle flow.
Now that we have all necessary data, let’s integrate it into MithraSIG. We use the road integration tool, selecting the relevant dataset and matching attributes: road geometry (filtered roads layer), number of lanes (NB_Voies), road width (Largeur), road category (Nature) → Derived from road names (Nom_Bande), morning light vehicle traffic flow → Morning VL Flow. The speed values are taken from the IGN dataset, ensuring consistency between traffic and noise calculations.
Once everything is set, we launch the integration process. After completion, we verify that the roads now contain traffic data, including TMJA values, vehicle flows, and speed limits.
At this stage, we are ready to run noise propagation calculations and generate noise maps. Reviewing the summary table, we notice that some roads still have TMJA = 0—these are the smaller roads that were intentionally left without traffic data. This is visible in the blue road sections on the map.
This tutorial demonstrated how to assign TMJA values using the table view, derive vehicle flows based on standard formulas, integrate traffic data into MithraSIG, and prepare for noise mapping calculations.
The table view in MithraSIG is a powerful tool, enabling attribute and spatial joins that significantly enhance data manipulation. While it is not the most intuitive feature at first, with practice, it becomes a valuable asset for handling large datasets efficiently.
Thank you for following along, and see you soon for the next tutorial!

Hello everyone, today we are going to create a MithraSIG project using the IGN BD Topo dataset for its quality, the richness of the data it provides, and its lower cost since it is free. We will go through a quick download process together. After these steps, the project will still need to be enriched with acoustic data, which is not provided by IGN but can be obtained from other sources, such as TMJA data or traffic surveys from your service providers. In any case, let's get started.
To find the dataset easily, simply search for BD Topo online. We will work with the Rhône (69) department as an example. You can also use regional-scale data if needed, but for most users, the department level is more than sufficient. Once downloaded, you can extract the ZIP file, which contains multiple layers within a somewhat complex directory structure. However, we will eventually find what we need, such as different types of buildings. I’ll let you explore the details, but today, we will only use the building layer. Of course, you are free to add more specific building types if necessary.
You will also need the road layer. The most comprehensive one is Road Segment (Tronçon Route), which we will use, but there are other detailed layers you may want to explore. Additionally, we will retrieve land use data and hydrographic networks, which will be sufficient for today.
Now, let's see how to load these different layers into MithraSIG. First, we create a new MithraSIG project in a dedicated directory. I will name mine BD Topo, placing it in my demo folder where I also stored my IGN data. As always, we select the Lambert 93 projection, which is ideal for French data. We will keep the default settings for weather parameters, propagation methods, and emission methods. The next step is to load the data. However, before doing so, we must also retrieve elevation data (BD Alti), which is essential for creating a Digital Terrain Model (DTM or MNT). Without this, no layers can be properly integrated into MithraSIG.
To get the elevation data, we follow the same approach—search for BD Alti online and select the first link provided by our search engine. We will download the Rhône department’s elevation dataset. Once downloaded, we extract the ZIP file and store the contents in the desired directory. I personally like to keep all my data in a single workspace.
For the Rhône department (69), the BD Alti dataset consists of multiple tiles, each identified by its upper-left coordinates in Lambert 93 format. With this, we now have all the necessary data to create our acoustic model, which will be our next step.
In MithraSIG, we can load a basemap (such as an IGN background map) to focus on our study area. For this example, we will analyze the Parc d’Affaires de Crécy in Saint-Didier-aux-Monts-d'Or, north of Lyon. I will create a polygon to crop the previously downloaded layers, restricting our acoustic study to a specific area. Only objects within or intersecting this polygon will be included in our analysis.
Next, we integrate the elevation dataset into MithraSIG. The file format is .ask, which is a grid-based dataset. Let’s examine its structure: it consists of a 1000x1000 grid, similar to an image where each pixel represents a 25m x 25m area. The dataset provides altitude information for each pixel, and the coordinates of the upper-left corner define the image’s reference point.
To transform this data into a DTM (MNT in French), we first define the projection, which is Lambert 93 for France. The IGN elevation data appears in MithraSIG, but it is not yet usable. To make it functional for our acoustic model, we integrate it using the Terrain functionality under the Grid section. We keep the default settings, launch the conversion calculations from .ask to MNT, and generate a terrain layer within the model. This terrain layer will serve as the foundation for all subsequent acoustic calculations. Once the conversion is complete, we now have both the original dataset and the generated MNT, which is fully integrated into the model.
Since this layer contains a large number of points, I will limit its display to prevent performance issues. Now, our study area is clearly visible, and we can proceed with data integration. I will go back to the directory where we downloaded the data and start with the BD Topo layers.
We begin by importing road segments (Tronçon Route), stored in Shapefile format. Since the file is quite large (87 MB), the import may take some time depending on your computer’s capacity. After loading, we see that far too many roads have been imported, so we need to filter them based on our study area.
To do this, we zoom in on our polygon and select only the relevant roads. We can duplicate the selected roads into a new layer called Filtered Roads, keeping only what we need. This process will be repeated for all layers, including railways, buildings, etc.
Now, let’s apply a more precise spatial filter while importing the building layer. I will clean up the displayed layers to focus only on what we are working on. Since we already filtered the roads, we can now remove the original IGN road layer from the project. The next step is to extract only the buildings that intersect our study polygon.
We use MithraSIG’s search tool, selecting the Geometry tab to perform a spatial query instead of an attribute query. After retrieving the relevant buildings, we duplicate them into a new filtered layer called Filtered Buildings.
Upon verification, the filtered buildings match our study area. However, we also captured the study polygon itself, which needs to be removed. After cleaning up this issue, we now have a correctly filtered building dataset. Again, I will remove the original IGN building layer from the project to free up memory.
We repeat this process for other layers. Since there are no railways or water bodies in our area, we will move on to land use data, specifically vegetation zones. To integrate this layer, we clip it using the study polygon.
First, we select all vegetation zones and then use the “Clip Outside” tool, selecting our study polygon as the clipping boundary. This removes all areas outside our focus area. The process may take a few moments due to the large number of vegetation polygons.
After clipping, the vegetation data now precisely matches our study area. However, note that the clipped data is permanently modified, so if you need the original dataset later, you will have to re-import it.
At this point, we are ready to integrate these layers into MithraSIG’s model section. Before doing so, it’s helpful to check the attributes of each layer. For example, in the vegetation layer, the “Nature” field describes the type of vegetation. In the road layer, the “NB_Voies” field contains the number of lanes, while “Largeur” stores the road width. Unfortunately, traffic data is missing, so we will need to integrate that later to generate noise maps.
For the building layer, the most critical attribute is height. We also have a housing count, which could be used for impact calculations, but we would need additional data to estimate the number of residents. The height attribute is particularly important because sound propagation is highly dependent on building height.
Now, let’s integrate the buildings. We use MithraSIG’s integration tool, selecting our filtered building layer and specifying height as the key attribute. If population data were available, we could also assign it. After integration, we obtain an acoustic building layer, where materials are set to default values but can be customized later.
We follow the same process for land use (vegetation) and roads. Since roads are a primary noise source, we define attributes such as number of lanes, width, and road type (mapped from road names). If acoustic data were available, we would also associate traffic information here.
Although traffic data must still be entered before running noise propagation calculations, this concludes today’s tutorial. A new feature coming in the next MithraSIG version will allow automatic integration of all these layers with a single click, significantly simplifying the process.
Thank you for your attention! Today’s tutorial was quite long, but we covered a lot of essential topics. See you soon for the next tutorial!

Hello,
Today, we are going to focus on using the Helmert adjustment function, which can be found in the main Geometry tab, right here. This is a practical tool as it allows you to georeference an image or a vector layer. Specifically, it enables you to scan a paper plan and integrate its information into your project, using it as a reference layer to add objects that may impact your acoustic model.
For today's example, I have prepared a classic project. We will use a PDF plan that provides some additional details. The integration process is very simple: select the PDF and drag it into the project. Initially, the plan will appear in a location that may not be convenient, so we move it aside to better visualize it. Once the plan is roughly in place, we start aligning it by making an initial adjustment.
The goal is to match elements in both documents. In this case, we want to align a specific entity in the model with a green-bordered area on the scanned plan. Our first task is to align the two documents. The rotation tool is quite handy for this.
We begin by placing the center of rotation. Since I need to shift the area to the left, I adjust the plan accordingly. If the first rotation is insufficient, I reposition the center and rotate again, making small incremental adjustments. I may also move the plan slightly to ensure enough space before rotating again.
Once the initial alignment is complete using the move and rotate tools, we start the Helmert transformation. This step involves selecting four reference points on the PDF plan and matching them to their corresponding locations in the project.
The first point on the PDF corresponds to this location in the project.
The second point aligns with this corner.
The third point should be placed here.
The fourth point is positioned at this location.
Once the fourth point is set, the document is shifted, rotated, and resized to match the reference points provided. If the precision of this first transformation is insufficient, you can repeat the Helmert transformation multiple times. Although this can be computationally intensive, each adjustment progressively improves the overlay accuracy. For this demonstration, I will not refine the alignment further, but the process can be iterated until achieving the desired precision.
After completing the transformation, the next step is to save your work. The PDF appears in the input data, and saving it is very straightforward: right-click on the file and select export. Since my project is in the southern region of Morocco, I ensure that I respect the projection settings.
I choose to save the file as an image and name it "test Helmert" before validating the export. The result is a JPEG file, which includes an associated georeferencing file for proper spatial alignment.
Now, I can integrate this raster file into a project. If I remove the original PDF and drag the JPEG into the project, I verify that the alignment is maintained. During export, I had chosen to compress the JPEG, which in this case resulted in a loss of quality. If the compression is unsatisfactory, you can simply disable it at the export stage to preserve the desired level of detail.
This is a highly practical and easy-to-use function, allowing you to integrate any document, including scanned paper plans, into your acoustic model.
That’s all for today. See you soon for more explorations of our software. Goodbye!

Hello,
For the past year, since the release of MithraSIG version 5.6, we have implemented the railway noise disturbance indicators defined in the decree of September 29, 2022. After providing the necessary documentation on how to calculate these indicators in our online resources at data.geomod.fr/mithraSIG/v5, today I would like to give you a short demonstration using a dataset containing a Shapefile of railway segments for the city of Strasbourg and a CSV file that defines the different traffic observations for each segment.
We can visualize the data by dragging it into MithraSIG. For the purpose of this tutorial, we will zoom in on a smaller area and, using the OpenStreetMap integration we previously implemented, quickly generate an acoustic model. We verify that the selected area is relevant, and then we retrieve all the necessary data layers, including road networks, buildings, land use, and terrain, which will allow us to automatically define our Digital Terrain Model (DTM or MNT). Depending on the connection speed, this process may take some time. Once validated, the data is automatically downloaded, and we resume once the import is complete.
After the import is finished, we can see that all necessary objects have been retrieved for the calculations, including land use data and buildings. As a reminder, when importing data from OpenStreetMap, building heights are not provided. By default, MithraSIG assigns a height of 8.40 meters, as shown here. We can also see that roads have been imported, but they do not yet contain traffic data, meaning they are not currently noise sources, since there are no vehicles assigned to them. However, today, our focus will be on railways, so we will return to the Shapefile import done earlier and use the railway integration tool.
The railway data is stored in the N Ferroviaire layer. We then proceed with attribute mapping:
The comments field serves as an identifier for each railway line.
The nature field defines the railway segments.
The number of tracks is automatically detected when possible.
With this, the Shapefile is converted into a railway noise source. A warning appears, indicating that our dataset covers a larger area than our study zone. However, our railway segments are correctly defined—though they currently lack train traffic data, meaning no noise propagation can be calculated yet.
To address this, we use the train traffic data available in the CSV file mentioned earlier. We apply automatic attribute joining, linking the railway segments to their corresponding traffic information from the CSV file. After a few moments, we successfully retrieve the traffic details for each segment, including train types, speeds, and frequencies. This data is applied not only to our study area but also to all available railway segments within our dataset.
Returning to our calculation area, we now need to illustrate the various noise indicators. To do this, we place a few receptors within the study area, leaving all settings at default values. We define R0 and add R1 a bit further away before launching a new receiver calculation.
Next, we define a calculation domain and start a receiver calculation. We select our preferred propagation method, choosing Harmonoise (1/3 oct). One key parameter is that we must enable the signature calculation option, which will be processed using a beam-tracing method. The rest of the settings remain unchanged, as our goal today is to obtain dynamic noise signatures and indicators. Once the calculations are complete, we can retrieve the results.
To access the calculated information, we simply right-click within the study area. For R0, we can view the noise signal received from the railway source. We select the railway segment of interest, then choose the specific train signature for analysis. Additionally, we can access dynamic noise indicators, retrieving values for each receiver (R0, R1) and for each railway segment.
The interface allows us to switch between different indicators, selecting the one most relevant to our analysis. With just a few months of development, Geomod has successfully integrated the decree of September 29, 2022, into MithraSIG, demonstrating our commitment to providing fast and effective solutions for our users.
That’s all for today. See you soon for another tutorial!

Hello,
Today, I’d like to quickly show you how to calculate the DNT-ATR, the building façade insulation index, using our interface.
I’ve prepared a very simple model—the classic test model available in the menu, which allows you to experiment with certain features in just a few clicks. I have disabled all railway and road sources, and I don’t have any industrial sources either. For this demonstration, I have kept only one road segment, which I have deliberately configured in a simplified manner: no light vehicles, neither during the day nor at night, and an extremely high number of heavy vehicles. As a result, you can see that the nighttime noise level reaches 96.79 dB.
Once the calculation is launched—I have already run it in advance, so I have my results ready—we can analyze them. For this tutorial, I will rely on the results of a receiver calculation, but this index can also be accessed through a horizontal calculation. The advantage of using a receiver calculation is that I can display labels, so I will add two:
One for the LAEq Night index, which I will label accordingly, as this will serve as the reference value.
One for the DNT-ATR index, where I can define a comfort level, meaning the maximum indoor noise level I want to ensure inside the building.
For the first demonstration, I set the indoor noise limit to 30 dB, and for a second example, I lower it to 20 dB, naming this second column DN.
I display the labels, and to better understand the results, I simply analyze the values at different receiver positions near the road. For example, at a receiver close to the road segment, we see that for an LAEq of 87.1 dB, with the 30 dB indoor constraint, the required insulation level is 57.1 dB (i.e., the difference between the outdoor and indoor noise levels).
In the second case, where I set a more demanding indoor threshold of 20 dB—a somewhat unrealistic requirement, but useful for illustration—you can see that the necessary insulation increases by 10 dB, reflecting the stricter noise reduction target.
That was a quick look at this insulation index. The essential formulas used to compute it are provided here, and I refer you to the extensive regulations on the subject, which you may already be familiar with.
I hope this explanation clarifies how MithraSIG performs façade insulation calculations. See you soon for another tutorial!