Integrated Approach to Optimizing Selection and Placement of Water Pipeline Condition Monitoring Technologies
The gradual deterioration of underground water infrastructure requires constant condition monitoring to prevent catastrophic failures, reduce leaks, and avoid costly unexpected repairs. However, given the large scale and tight budgets of water utilities, it is essential to implement strategies for o...
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MDPI AG
2025-05-01
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| author | Diego Calderon Mohammad Najafi |
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| description | The gradual deterioration of underground water infrastructure requires constant condition monitoring to prevent catastrophic failures, reduce leaks, and avoid costly unexpected repairs. However, given the large scale and tight budgets of water utilities, it is essential to implement strategies for optimal selection and deployment of monitoring technologies. This article introduces a unified framework and methods for optimally selecting condition monitoring technologies while locating their deployment at the most vulnerable pipe segments. The approach is underpinned by an R-E-R-A-V (Redundant, Established, Reliable, Accurate, and Viable) principle and asset management concepts. The proposed method is supported by a thorough review of assessment and monitoring technologies, as well as common sensor placement approaches. The approach selects optimal technology using a combination of technology readiness levels and SFAHP (Spherical Fuzzy Analytic Hierarchy Process). Optimal placement is achieved with a k-Nearest Neighbors (kNN) model tuned with minimal topological and physical pipeline system features. Feature engineering is performed with OPTICS (Ordering Points to Identify the Clustering Structure) by evaluating the pipe segment vulnerability to failure-prone areas. Both the optimal technology selection and placement methods are integrated through a proposed algorithm. The optimal placement of monitoring technology is demonstrated through a modified benchmark network (Net3). The results reveal an accurate model with robust performance and a harmonic mean of precision and recall of approximately 65%. The model effectively identifies pipe segments requiring monitoring to prevent failures over a period of 11 years. The benefits and areas of future exploratory research are explained to encourage improvements and additional applications. |
| format | Article |
| id | doaj-art-c610f5e7c5db42a181a32fae265e9f32 |
| institution | Kabale University |
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| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
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| series | Eng |
| spelling | doaj-art-c610f5e7c5db42a181a32fae265e9f322025-08-20T03:47:49ZengMDPI AGEng2673-41172025-05-01659710.3390/eng6050097Integrated Approach to Optimizing Selection and Placement of Water Pipeline Condition Monitoring TechnologiesDiego Calderon0Mohammad Najafi1Center for Underground Infrastructure Research and Education (CUIRE), Department of Civil Engineering, The University of Texas at Arlington, Arlington, TX 76019, USACenter for Underground Infrastructure Research and Education (CUIRE), Department of Civil Engineering, The University of Texas at Arlington, Arlington, TX 76019, USAThe gradual deterioration of underground water infrastructure requires constant condition monitoring to prevent catastrophic failures, reduce leaks, and avoid costly unexpected repairs. However, given the large scale and tight budgets of water utilities, it is essential to implement strategies for optimal selection and deployment of monitoring technologies. This article introduces a unified framework and methods for optimally selecting condition monitoring technologies while locating their deployment at the most vulnerable pipe segments. The approach is underpinned by an R-E-R-A-V (Redundant, Established, Reliable, Accurate, and Viable) principle and asset management concepts. The proposed method is supported by a thorough review of assessment and monitoring technologies, as well as common sensor placement approaches. The approach selects optimal technology using a combination of technology readiness levels and SFAHP (Spherical Fuzzy Analytic Hierarchy Process). Optimal placement is achieved with a k-Nearest Neighbors (kNN) model tuned with minimal topological and physical pipeline system features. Feature engineering is performed with OPTICS (Ordering Points to Identify the Clustering Structure) by evaluating the pipe segment vulnerability to failure-prone areas. Both the optimal technology selection and placement methods are integrated through a proposed algorithm. The optimal placement of monitoring technology is demonstrated through a modified benchmark network (Net3). The results reveal an accurate model with robust performance and a harmonic mean of precision and recall of approximately 65%. The model effectively identifies pipe segments requiring monitoring to prevent failures over a period of 11 years. The benefits and areas of future exploratory research are explained to encourage improvements and additional applications.https://www.mdpi.com/2673-4117/6/5/97pipelineswatercondition assessmentcondition monitoringsensorsTRL |
| spellingShingle | Diego Calderon Mohammad Najafi Integrated Approach to Optimizing Selection and Placement of Water Pipeline Condition Monitoring Technologies Eng pipelines water condition assessment condition monitoring sensors TRL |
| title | Integrated Approach to Optimizing Selection and Placement of Water Pipeline Condition Monitoring Technologies |
| title_full | Integrated Approach to Optimizing Selection and Placement of Water Pipeline Condition Monitoring Technologies |
| title_fullStr | Integrated Approach to Optimizing Selection and Placement of Water Pipeline Condition Monitoring Technologies |
| title_full_unstemmed | Integrated Approach to Optimizing Selection and Placement of Water Pipeline Condition Monitoring Technologies |
| title_short | Integrated Approach to Optimizing Selection and Placement of Water Pipeline Condition Monitoring Technologies |
| title_sort | integrated approach to optimizing selection and placement of water pipeline condition monitoring technologies |
| topic | pipelines water condition assessment condition monitoring sensors TRL |
| url | https://www.mdpi.com/2673-4117/6/5/97 |
| work_keys_str_mv | AT diegocalderon integratedapproachtooptimizingselectionandplacementofwaterpipelineconditionmonitoringtechnologies AT mohammadnajafi integratedapproachtooptimizingselectionandplacementofwaterpipelineconditionmonitoringtechnologies |