Cross-bore risk assessment and risk management tool
A method for cross-bore risk management involves receiving at least one dataset comprising a plurality of assets and cross-bore data. A risk probability value is calculated, using a processor, based on the cross-bore data for each asset of the plurality of assets using machine learning techniques. The risk probability values are spatially distributed around each respective asset. A graphical output is produced that illustrates the risk probability for a specified geographical area based on the spatially distributed risk probability values.
1. A method for cross-bore risk management, comprising:
receiving at least one dataset comprising a plurality of assets and cross-bore data;
calculating, using a processor, a risk probability value based on the cross-bore data for each asset of the plurality of assets using machine learning techniques;
spatially distributing the risk probability values around each respective asset; and
producing a graphical output illustrating the risk probability for a specified geographical area based on the spatially distributed risk probability values.
2. The method of claim 1 , wherein the risk probability value is calculated using orthogonalized quadrature.
3. The method of claim 1 , wherein spatially distributing the risk probability values comprises calculating field values at locations radially away from each respective asset.
4. The method of claim 1 , wherein spatially distributing the risk probability values comprises calculating field values around line segments.
5. The method of claim 4 , wherein calculating field values around line segments comprises distributing probability value perpendicularly along the length of the segment and radially from the end vertices using a field equation.
6. The method of claim 1 , wherein producing the graphical output comprises generating a raster image based on the spatially distributed risk probability values.
7. The method of claim 6 , further comprising producing contour lines of cumulative risk density using the raster image.
8. The method of claim 7 , further comprising generating polygons using the contour lines.
9. The method of claim 8 , further comprising prioritizing work based on the polygons.
10. The method of claim 1 , wherein the graphical output is a heat map.
11. A system for cross-bore risk management, comprising:
a processor; and
a memory storing computer program instructions which when executed by the processor cause the processor to perform operations comprising:
receiving at least one dataset comprising a plurality of assets and cross-bore data;
calculating a risk probability value based on the cross-bore data for each asset of the plurality of assets using machine learning techniques;
spatially distributing the risk probability values around each respective asset; and
producing a graphical output illustrating the risk probability for a specified geographical area based on the spatially distributed risk probability values.
12. The method of claim 11 , wherein the risk probability value is calculated using orthogonalized quadrature.
13. The system of claim 11 , wherein spatially distributing the risk probability values comprises calculating field values at locations radially away from each respective asset.
14. The system of claim 11 , wherein spatially distributing the risk probability values comprises calculating field values around line segments.
15. The system of claim 14 , wherein calculating field values around line segments comprises distributing probability value perpendicularly along the length of the segment and radially from the end vertices using a field equation.
16. The system of claim 11 , wherein producing the graphical output comprises generating a raster image based on the spatially distributed risk probability values.
17. The system of claim 16 , further comprising producing contour lines of cumulative risk density using the raster image.
18. The system of claim 17 , further comprising generating polygons using the contour lines.
19. The system of claim 11 , wherein the graphical output is a heat map.