Water Pipe Network Rehabilitation Using Spatial Criticality Optimization
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Solution Overview
Problem
Existing models for water supply systems in post-earthquake scenarios fail to account for spatial variabilities in demand priorities and seismic ground motion intensities, leading to inadequate rehabilitation strategies for critical infrastructure like hospitals and firefighting stations.
Innovation Solution
A novel approach integrating spatial demand criticalities and seismic ground motion intensities into a stochastic combinatorial optimization problem, using a graph representation of water pipe networks and a simulated annealing algorithm with Monte Carlo simulation to identify cost-effective rehabilitation policies that enhance seismic resilience.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If existing models assume equal importance for all water demand types, then the model formulation is simple, but the rehabilitation strategy becomes inadequate for critical infrastructure
Solution Approach 1:
The patent applies local quality by assigning different weights to water demand types based on their criticality. Critical infrastructure nodes (hospitals, firefighting stations) are assigned higher weights than non-critical nodes (residential areas, industrial zones). This allows the model to differentiate the importance of various water demand types and prioritize rehabilitation efforts accordingly, resolving the contradiction between model simplicity and rehabilitation effectiveness.
2Reliability
If spatial variabilities of demand criticalities and seismic ground motion intensities are integrated, then the rehabilitation policy becomes optimized and equitable, but the computational complexity increases
Solution Approach 1:
The patent segments the water supply network into discrete nodes and edges, assigning criticality weights to nodes and seismic vulnerability parameters to edges. This segmentation allows the complex integration of spatial variabilities to be handled through systematic iteration over network elements, making the computation manageable while achieving optimized rehabilitation policies that consider both demand criticality and seismic ground motion intensities.
Solution Approach 2:
The patent performs preliminary calculations of criticality weights and seismic vulnerability parameters before the main optimization process. By pre-computing these values based on node locations, demand types, and ground motion intensity maps, the model reduces computational complexity during the actual rehabilitation policy optimization, while still achieving comprehensive integration of spatial variabilities.
3Loss of time
If the model considers only deterministic parameters, then the calculation is faster, but it fails to capture the probabilistic nature of seismic damage
Solution Approach 1:
The patent introduces dynamic probabilistic elements into the model by incorporating random variables for seismic ground motion intensities and pipe damage probabilities. Instead of using fixed deterministic values, the model dynamically samples from probability distributions to assess potential damage scenarios. This allows the model to capture the uncertain and probabilistic nature of seismic damage while maintaining reasonable calculation times through efficient sampling methods.
Data Source
AI summary
Systems and methods to identify a rehabilitation policy for water pipe networks are provided. A graph is built to represent a water pipe network including edges for each pipe and nodes for each water source or water user. An approach based on proximity analysis is used to determine the criticality of each node in the graph based on the spatial distribution of water demand type in the neighborhood where the node is located. The spatial variabilities of demand criticalities along with the spatial variabilities of the seismic ground motion intensities are integrated into the formulation of an optimization problem to identify rehabilitation policies the water supply network. A purpose-built simulated annealing algorithm is then used to solve the optimization problem. Results of the optimization may then be used to identify pipes in the water pipe network to replace based on a replacement budget.