Integrated Approach to Optimizing Selection and Placement of Water Pipeline Condition Monitoring Technologies
Abstract
:1. Introduction
2. Condition Assessment and Monitoring Technologies
3. Techniques for Placement of Condition Monitoring Technologies
4. Framework for Optimal Selection and Placement of CM Technology
- Redundant: Various technologies and back-up systems should be considered.
- Established: Methods should be reliable and with a proven track record.
- Reliable: Technologies should be developed through rigorous testing and evaluation in real-world systems.
- Accurate: Results should provide minimal false positives, false negatives, and errors.
- Viable: The approach should be cost-effective and financially feasible given the unique limitations of utilities.
Algorithm 1 RERAV-based integrative sensor placement and technology selection. |
Require: : Base DMA GIS DB, : kNN Results DB, : CM Technology Limits DB, : SFAHP Results Ensure: Optimal CM technology selection and placement.
|
4.1. Approach to Optimal Placement of CM Technologies
4.2. Approach to Selection of CM Technologies
5. Approach Implementation Through a Case Scenario
6. Discussion
- Suitability for analyzing entire water system networks at once, or by DMA, pipe size ranges (distribution or transmission systems), neighborhood, and so forth.
- Ability to reevaluate technology readiness and eventually include emerging methods that currently have the potential to revolutionize CM of underground infrastructure (e.g., QST and SSM).
- Adaptability to meet the needs and goals of different types and sizes of water utilities by systematically ranking CM methods based on their functionality and varying operational environments.
- Possibility of providing more or less importance to technology costs, wireless connectivity, scalability, power requirements, installation complexity, and additional criteria of importance to utilities.
- Applicability to most water utilities due to its lower data requirements. The approach utilizes data features typically employed for regular water audits and loss control studies.
- Ability to conduct a system-wide evaluation of pipe segments with the use of the CPI feature, which encodes the spatial context related to various factors such as construction quality, soil type, depth of bury, and traffic loads.
7. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
AHP | Analytic Hierarchy Process |
CA | Condition Assessment |
CBA | Cost–Benefit Analysis |
CM | Condition Monitoring |
CMMS | Computerized Maintenance Management System |
CPI | Cluster Proximity Index |
DBSCAN | Density-Based Spatial Clustering of Applications with Noise |
DFOS | Distributed Fiber Optic Systems |
DMA | District Metered Area |
DSS | Decision Support System |
kNN | K-Nearest Neighbors |
LR | Logistic Regression |
MCA-SC | Multicriteria Analysis for System Characteristics |
MCDA | Multicriteria Decision Analysis |
ML | Machine learning |
NDE | Non-Destructive Evaluation |
NDT | Non-Destructive Testing |
NGSA-II | Non-dominated Sorting Genetic Algorithm II |
OPTICS | Ordering Points to Identify the Clustering Structure |
PEC | Pulse Eddy Current |
RBDSS | Rule-Based Decision Support System |
RERAV | Redundant, Established, Reliable, Accurate, and Viable |
RF | Random Forest |
RMA | Robust Greedy Approximation |
RMIO | Robust Mixed Integer Optimization |
RUL | Remaining useful life |
SCADA | Supervisory Control and Data Acquisition |
SFAHP | Spherical Fuzzy Analytic Hierarchy Process |
SFLO | Shuffled Frog Leaping Optimization |
SFS | Spherical Fuzzy Sets |
SHM | Structural Health Monitoring |
SVM | Support Vector Machine |
SWAM | Spherical Weighted Arithmetic Mean |
TRL | Technology readiness level |
WSN | Wireless Sensing Network |
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Technology | Description | Advantages | Disadvantages | References |
---|---|---|---|---|
Acoustic Emission Testing (AET) | Detection and analysis of transient sound waves from a rapid release of energy within a material. |
|
| [24,25,47] |
Acoustic Pipe Wall Assessment (APWA) | Measurement of the average pipe wall loss using pulsed sound waves traveling through the pipe between two sensors. |
|
| [48,49,50] |
Stress Wave-based Testing (SWT) | Determines pipe wall thickness and defects from analysis of a controlled impact and resulting stress waves. |
|
| [37,47] |
Ground Microphones | Hardware-based method to detect the sound of water leaks from the ground surface. |
|
| [42,43,44] |
Ultrasonic Methods | High-frequency mechanical vibrational sound waves inaudible to the human ear, used to detect and locate anomalies or to measure thicknesses. |
|
| [31,37,51] |
Vibro-Acoustic Monitoring (VAM) | Flow-traveling sensors or devices placed on pipe appurtenances, such as valves and hydrants. Capable of detecting and locating leaks and pipe bursts. |
|
| [41,42,43,50,52] |
Technology | Description | Advantages | Disadvantages | References |
---|---|---|---|---|
Corrosion Monitoring Methods (CMMs) | Methods that monitor for cumulative metal loss, real-time corrosion rates, and localized corrosion phenomena in pipelines with cathodic protection. |
|
| [9,31,36,37] |
Cathodic Protection Monitoring (CPM) | Coupon weight loss measurements or voltage measurement between the pipeline and the surrounding soil for assessing cathodic protection. |
|
| [53,54] |
Chemical Composition Method (CCM) | Methods that use the chemical properties of a substance to detect leaks. Typically include tracer gas and canine leak detection. |
|
| [42,43,44,55] |
Electromagnetic Systems | Methods that induce, analyze, and monitor magnetic or electric fields, relying on changes in magnetic flux, eddy currents, or remote electromagnetic interactions. |
|
| [28,31,37,47,54] |
Fiber-Optic Sensing (FOS) | Uses optical fibers to measure physical and chemical parameters by analyzing variations in the properties of light traveling through the fiber. |
|
| [5,56,57,58,59,60] |
Geospatial Remote Sensing (GRS) | Use of sensors to collect data about the Earth’s surface typically from satellites or aircraft. The sensor is classified as active when it provides its own energy source and passive when it measures reflected or emitted energy. |
|
| [25,55,61,62] |
Geotechnical Instrumentation and Monitoring (GIM) | Uses various technologies to monitor physical properties of structures to assess stability and predict hazardous conditions. |
|
| [46,63] |
Ground-Penetrating Radar (GPR) | A geophysical method that uses electromagnetic waves to acquire information below the ground surface. |
|
| [37,43,44,64,65,66,67] |
Infrared Thermography (IT) | Use of scanners to detect thermal contrasts on the ground surface from emitted infrared (IR) radiation. The thermographic images assist in the identification of water leaks. |
|
| [37,44,68,69,70] |
Light Detection and Radar (LiDAR) | A remote sensing technology that emits laser light pulses to measure distances. |
|
| [25,46,71] |
Low-Voltage Conductivity System (LVCS) | Use of an electrical circuit between the pipe and the ground surface to detect current as an indication of water leaks in non-metallic pipelines. A mobile probe is located either inside the pipe or at the ground surface. |
|
| [28,72] |
Negative Pressure Waves (NPW) | Technology that detects the sudden changes in pressure and flow caused by the onset of a water leak. |
|
| [43,73,74] |
Quantum Sensing Technology (QST) | Emerging non-destructive testing technology that uses quantum phenomena, including superposition and entanglement, for measurement of physical quantities with greater accuracy and sensitivity. |
|
| [75,76,77] |
Radiographic Testing (RT) | Involves using a source or radiation, such as gamma or X-rays, to pass through a pipeline and onto a detector to identify defects. |
|
| [31,37] |
RFID-Based Wireless Sensors | Devices with sensors embedded into elements of bolted connections, including, bolts, washers, and flanges. |
|
| [73,78,79] |
Smart Joint Assemblies (SJA) | Devices with sensors embedded into elements of bolted connections, including, bolts, washers and flanges. |
|
| [80,81,82] |
Smart Self-Sensory Materials (SSM) | Materials capable of monitoring their own condition without integrated or peripheral sensors. SSM incorporate functional fillers within their own material. |
|
| [83,84] |
Transient Pressure Monitoring (TPM) | Involve gathering and analyzing data of sudden pressure changes as a result of operational activities or anomalies within the pipeline system. |
|
| [44,73,85,86] |
Visual Inspections with CCTV | Use of visual inspection equipment on live-flow conditions to assess the internal condition of pipelines. |
|
| [41,47,50,69] |
Technology | Description | Advantages | Disadvantages | References |
---|---|---|---|---|
Bayesian Interference Systems (BISs) | Use of probability and likelihood functions to deduce water leaks and pipe bursts. |
|
| [44,87] |
Fuzzy Systems (FSs) | Use fuzzy logic to interpret ambiguous input to exact outputs using natural-language expressions. |
|
| [19,36,87] |
Machine Learning (ML) | Artificial Intelligence (AI) algorithms that learn from data, find patterns, and determine possible outcomes. |
|
| [44,87,88,89,90] |
Predictive Hydraulic Modeling (PHM) | System simulations to calculate differences between inflows and outflows. Discrepancies may indicate leaks or other NRWL. |
|
| [44,73] |
Predictive Structural Analysis Modeling (PSAM) | Pipe simulation to predict the effect of known deterioration. Enables proactive maintenance and decision-making. |
|
| [28,91,92] |
Statistical Interference Modeling (SIM) | Applied statistics to infer asset condition and predict RUL. Predicts possible leak or burst locations. |
|
| [26,73] |
Factor | Sample Rationale | Reference |
---|---|---|
Signal reliability | Achieving reliable acoustic leak detection signals in plastic pipelines. | [43] |
Sensor efficiency | Maximizing detection capabilities while minimizing total number of sensors. | [26] |
Precision | Precisely locating anomalies (i.e., PCCP wire breaks using AET with hydrophone stations). | [3] |
Data quality | Enhancing data collection and overall quality. | [32] |
Connectivity | Ensuring adequate sensor connectivity within a WSN. | [46] |
Detection accuracy | Preventing mislabeling of leaks (false positives) and avoiding incomplete water system coverage. | [10] |
Compatibility | Addressing technology constraints in terms of pipe size and type (i.e., acoustic sensors for PCCP lines). | [94] |
Installation viability | Enhancing the recorded signal by selecting adequate mounting points on the asset (i.e., noise loggers). | [52] |
Sensor spacing | Assuring sensor placement within technology’s recommended spacing (i.e., hydrophone arrays). | [95] |
Costs | Achieving a favorable cost/benefit ratio. | [96] |
Reference | Model(s) | Strategy | Associated Factors |
---|---|---|---|
Ferreira et al. [13] | Multiobjective Genetic Algorithm. | Pressure sensitivity analysis, multiobjective NGSA-II, and CBA in a real network. | Roughness coefficient, burst size, and pressure. |
Yang and Wang [9] | Multicriteria Decision Flowcharts/Sequential Subtraction Algorithm. | Economic considerations, Monte Carlo Sensor Fault Compensation, and EPANET Net3 simulation. | DCR, TDS, costs, and simulated pressures. |
Santos-Ruiz et al. [16] | Heuristic Algorithm based on Information Theory. | Maximize information relevance while minimizing redundancy, used Hanoi system network simulation with EPANET. | System pressures at nodes, sensor Relevance/Redundancy Index (RRI), and number of sensors. |
Zecchin et al. [97] | Dijkstra’s Algorithm, Metaheuristic Genetic Algorithms. | Nettrans model, graph-based representation, combinatorial leak and node optimization. | Detection coverage, system constraints, and anomaly characteristics. |
Hu et al. [98] | Error-Domain Model Falsification (EDMF) and Hierarchical Algorithms with Joint Entropy. | EPANET Net3 simulation for leak detection accuracy. | Node pressures and cost vs. information. |
Zhao et al. [96] | Fast messy Genetic Algorithm (fmGA) and Monte Carlo. | CBA, EPANET simulation with Net3, and detection coverage ratios. | Synthetic pipe bursts at network nodes, investment costs, and system pressures. |
Soroush and Abedini [15] | Genetic Algorithm and Block Ordinary Kriging. | Simplified, exhaustive, and random search optimization. | System pressure at nodes, economic cost of data collection. |
ChenLei et al. [8] | OPTICS (Ordering Points to Identify the Clustering Structure) Algorithm and Node Feature Matrix. | Spatial and non-spatial attributes, clustering for sensor placement optimization, ML comparison, and experimental testing. | Network pressures (normal and anomalies), sensor topological limitations, and economic feasibility. |
Jun and Kwon [11] | Sensitivity and Unsteady-Flow Analyses. | Hydraulic simulations and experimental testing. | Actual and simulated system pressure measurements. |
Rayaroth and Sivaradje [99] | Iterative Dichotomiser 3 (ID3) Decision Forest Classifier and Shuffled Frog Leaping Optimization (SFLO). | Pressure data for model training on real District Metered Area (DMA) and simulation in EPANET. | System pressure data, flow rates, computing time, and error rate (false positives). |
Xie et al. [100] | Compressed Sensing and Enhanced Binary Artificial Bee Colony (ABC) Algorithm. | Sensor detection and redundancy, Quadratic Knapsack Problem (QKP), real system with synthetic data. | System topology and pressure, sensor cost, and mutual coherence. |
Sela and Amin [93] | Robust Mixed Integer Optimization (RMIO) and Robust Greedy Approximation (RMA). | Model-based fault detection matrix, optimization problem formulation and solving in 10 real water networks. | Sensor redundancy, budget, online and offline sensor scenarios. |
Yazdekhasti et al. [24] | Multicriteria Decision Analysis (MCDA) with weighting based on Monte Carlo simulations. | Literature review and expert validation, comparison of costs, reliability, ease of deployment, and leak size estimation. | Pipe material, diameter, placement feasibility, leak detection uncertainty. |
Cuguero-Escofet et al. [10] | Sensitivity Matrix, Correlation-Based Isolation Index, Metaheuristic Genetic Algorithm. | Hydraulic modeling with EPANET in real DMA, sensitivity matrix, and isolation index. | Topological data, sensor resolution, actual and simulated pressures. |
Nejjari et al. [14] | Sensitivity analysis, Evidential C-Means, and Exhaustive Search. | Fault sensitivity matrix and clustering to in a real network locate pressure sensors. | Distance to leak, pressure data. |
Saldarriaga and Salcedo [12] | Hydraulic Metaheuristics and Genetic Algorithms. | Hydraulic heuristics, NGSA-II, and EPANET simulation in real and synthetic networks. | Annual loss of profits from leaks, system pressure. |
Chang et al. [17] | Rule-Based Decision Support System (RBDSS). | EPANET simulation of a synthetic network. | Sensor accessibility and network complexity rules. |
Reed et al. [25] | Multicriteria Decision Flowcharts. | Literature review, case studies, and field tests. | Leakage, corrosion, joint integrity, pressure, loading, wall thickness, water temperature, and costs. |
Alternatives | Sample Criteria |
---|---|
A1 CM Technology 1 | C1 Installation complexity |
A2 CM Technology 2 | C2 Power requirements |
A3 CM Technology 3 | C3 Scalability |
A4 CM Technology 4 | C4 Wireless connectivity |
C5 CAPEX (capital expenses) | |
C5 OPEX (operational expenses) |
Layer | Attribute | Standard in Net3 | Supplemental for Case Scenario | Used for Case Scenario |
---|---|---|---|---|
Pipes | ID | ✓ | · | ✓ |
Node1 | ✓ | · | ✓ | |
Node2 | ✓ | · | ✓ | |
Length | ✓ | · | · | |
Diameter | ✓ | · | ✓ | |
Roughness | ✓ | · | · | |
MinorLoss | ✓ | · | · | |
Status | ✓ | · | · | |
Material | · | ✓ | ✓ | |
Installation Date | · | ✓ | ✓ | |
Coordinates | Node | ✓ | · | ✓ |
X-Coord | ✓ | · | ✓ | |
Y-Coord | ✓ | · | ✓ | |
Failures | ID | · | ✓ | ✓ |
Incident Date | · | ✓ | ✓ | |
Asset ID | · | ✓ | ✓ | |
Asset Year Installed | · | ✓ | ✓ |
ML Techniques | Tuning Parameters and Implementations |
---|---|
OPTICS | Parameters: dbscan package in R, MinPts = 5, max_eps = ∞, clustering eps = 3, (failure point location). |
kNN | Parameters: class package in R, training ≈ 70% (1985–2012), testing ≈ 30% (2012–2024), , weighted vote: no; numerical + categorical variables after one-hot encoding. |
Training dataset: independent vars. (from , , )—past failures (≈20%) 1985–1992; CPI; cluster membership; pipe age, material, size. Dependent var: pipe failed (≈50%) 1992–2012. | |
Testing dataset: independent vars. (same sources)—past failures (69.23%) 1985–2012; CPI; cluster membership; pipe age, material, size. Dependent var: pipe failed (≈30%) 2013–2024. |
Accuracy | Sensitivity | Specificity | Precision | Negative Predictive Value | F1-Score |
---|---|---|---|---|---|
81.74% | 52.78% | 94.94% | 82.61% | 81.52% | 64.41% |
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Calderon, D.; Najafi, M. Integrated Approach to Optimizing Selection and Placement of Water Pipeline Condition Monitoring Technologies. Eng 2025, 6, 97. https://doi.org/10.3390/eng6050097
Calderon D, Najafi M. Integrated Approach to Optimizing Selection and Placement of Water Pipeline Condition Monitoring Technologies. Eng. 2025; 6(5):97. https://doi.org/10.3390/eng6050097
Chicago/Turabian StyleCalderon, Diego, and Mohammad Najafi. 2025. "Integrated Approach to Optimizing Selection and Placement of Water Pipeline Condition Monitoring Technologies" Eng 6, no. 5: 97. https://doi.org/10.3390/eng6050097
APA StyleCalderon, D., & Najafi, M. (2025). Integrated Approach to Optimizing Selection and Placement of Water Pipeline Condition Monitoring Technologies. Eng, 6(5), 97. https://doi.org/10.3390/eng6050097