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:
( 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 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
Calculate Standard Deviation
Calculate the standard deviation of settlement rates for each cluster to determine the dispersion within the cluster.
Classify Clusters
Classify each cluster into one of the five risk categories based on the calculated standard deviation.
Update on Data Changes
Ensure that the model runs for those buildings whenever velocity data is added or updated.
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.
Web Service Integration
Include differential settlement risks in the web service (WS) as a separate product.
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.
Grafana Integration
Process in Grafana as a "background task".
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.
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:
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:
The obtained risk classification applies to each building in the respective cluster.
Actions
Calculate Standard Deviation
Classify Clusters
Update on Data Changes
Create Tileset
Web Service Integration
Documentation Updates
Grafana Integration
Add to Analysis_full
Expected Behavior
Additional Information