Optimierung des städtischen Entwässerungsnetzentwurfs mittels Ameisenkolonieoptimierung (Optimizing urban drainage network design using ant colony optimization)
The codebase designs a drainage network by optimizing pipe diameters and selecting detention facilities to minimize costs while meeting hydraulic, regulatory, and water quality requirements.
- Hydraulic Modeling :—
- The
DesignRainfallclass uses an intensity-duration curve to interpolate rainfall intensity for a given duration, enabling runoff calculations. CatchmentAreacalculates runoff coefficients based on land use and estimates time of concentration using an empirical formula.DrainageSolutioncomputes total runoff volume using rainfall intensity, catchment area, and runoff coefficients.
- The
- Pipe Sizing :—
- The
calculateRequiredDiametermethod inMainuses Manning’s equation to size pipes based on peak flow, slope, and material properties, ensuring capacity meets demand while respecting velocity constraints.
- The
- Detention Facilities :—
DetentionFacilitymodels detention/retention basins with capacity, release rate, and cost calculations.- The
evaluateDetentionRequirementsmethod selects a facility based on cost per volume, ensuring sufficient storage for 20% of runoff volume.
- Cost Estimation :—
- Pipes:
DrainagePipe.calculateInstallationCostaccounts for material costs (fromPipeMaterial) and excavation costs based on pipe diameter and depth. - Detention Facilities:
DetentionFacility.getCostuses a base cost plus a capacity-dependent term, with different rates for detention vs. retention facilities. - Total Cost:
DrainageSolutiontracks the total cost by summing pipe and facility costs.
- Pipes:
- Regulatory and Environmental Compliance :—
RegulatoryStandardsdefines constraints like maximum velocity, minimum slope, and minimum cover.WaterQualityRequirementsspecifies limits for pollutants (TSS, oil/grease) and pH, with a sedimentation requirement.- The
calculateWaterQualityScoremethod inDrainageSolutionassigns scores based on HDPE pipe usage and detention facilities, though its simplistic approach (e.g., fixed increments) could be refined.
- Optimization using ant colony optimization (ACO) :—
- Uses ants to construct solutions by selecting paths from catchment nodes to the outfall.
- Pheromones guide path selection, updated based on solution quality (inversely proportional to cost).
- Heuristic information combines factors like pipe length, elevation difference, drainage area, flood risk, and water quality.
- Builds paths from catchment nodes to the outfall (node 0) using probabilistic node selection.
- Paths are converted to pipes with calculated diameters.
- Deposits pheromones based on solution quality and reduces pheromone levels to prevent premature convergence.
- The implementation includes core ACO elements: pheromone trails, heuristic information, evaporation, and probabilistic path selection.
- Parameters (
ALPHA,BETA,Q,EVAPORATION_RATE) are configurable, allowing tuning of exploration vs. exploitation. - The heuristic combines multiple factors (distance, elevation, drainage area, water quality), making path selection context-aware.
- Elevation factor penalizes pipes with insufficient slope, ensuring hydraulic feasibility.
- Provides detailed metrics (cost, runoff volume, pipe utilization, water quality score), enabling comparison of solutions.