Model Risk of Differential Settlement

[DonZandbergen]

Description

To assess the risk of building settlement per cluster, the standard deviation of settlement rates within each cluster must be calculated. The greater the differential settlement, the higher the risk. This analysis consists of two steps: calculating the standard deviation within a cluster and classifying it to translate into a Differential Settlement Risk.

This analysis can be performed for all buildings with a velocity in the database.

Step 1: Calculation of Standard Deviation

The standard deviation (σ) of settlement rates within a cluster is used to determine the dispersion of these rates around the mean. The formula for standard deviation is as follows:

[ \sigma = \sqrt{\frac{1}{n} \sum_{i=1}^{n} (Vi - V{gem})^2} ]

where:

• ( n ) is the number of buildings in the cluster,
• ( V_i ) is the settlement rate of building ( i ),
• ( V_{gem} ) is the average settlement rate of the cluster.

The stddev function in SQL can also be used.

Step 2: Classification of Settlement Differences

Based on the calculated standard deviation, clusters are classified into one of five risk categories, depending on the degree of differential settlement. The categories are as follows:

• Risk A (σ ≤ 0.5): Settlement rates of the buildings are very close to each other.
• Risk B (0.5 < σ ≤ 1.0): There is a small variation in settlement rates.
• Risk C (1.0 < σ ≤ 2.0): Noticeable variation, requiring attention.
• Risk D (2.0 < σ ≤ 3.0): Large variation in settlement rates, potential risk of damage.
• Risk E (σ > 3.0 mm/year): Very large variation, high priority for further analysis and measures.

The obtained risk classification applies to each building in the respective cluster.

Actions

1. Calculate Standard Deviation

   • Calculate the standard deviation of settlement rates for each cluster to determine the dispersion within the cluster.

2. Classify Clusters

   • Classify each cluster into one of the five risk categories based on the calculated standard deviation.

3. Update on Data Changes

   • Ensure that the model runs for those buildings whenever velocity data is added or updated.

4. Create Tileset

   • Create a tileset of the classification at the building level and include it in the mapset Risk as “Risico verschilzakking”. Use the standard legend and colors: Standard map colors.

5. Web Service Integration

   • Include differential settlement risks in the web service (WS) as a separate product.

6. Documentation Updates

   • Update the documentation for differential settlement level in the Risk explanation in the Wiki.
   • Update the documentation for differential settlement level in the WS in the Wiki.

7. Grafana Integration

   • Process in Grafana as a "background task".

8. Add to Analysis_full

   • Include the differential settlement risk in Analysis_full.

Expected Behavior

• The standard deviation of settlement rates should be calculated and classified into risk categories.
• The tileset for differential settlement risk should be created and integrated into the Risk mapset.
• The differential settlement risk should be available as a product in the web service.
• Documentation should be updated accordingly.
• The differential settlement risk should be processed in Grafana and included in Analysis_full.

Additional Information

• Use the standard legend and colors for the map.
• Ensure the model runs whenever there are updates or additions to the velocity data.

[DonZandbergen]

De aanpak aanpassen: Zakkingsnelheden door extrapoleren naar de toekomst en verschil afzetten tegenover de beoordeling van de lintvoegmetingen.