[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-81902-en":3,"doc-seo-81902-105":31,"detail-sidebar-cat-0-en-105":93},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},81902,8796095462418,"Noah","https://ap-avatar.wpscdn.com/avatar/80000253c1241d02b47?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778826106357471780",8,"Research & Report","Markov Decision Process Approximation Methods for Water Distribution Network Inspection and Maintenance","Develops a repair-oriented inspection and maintenance decision framework for water distribution networks operating under data scarcity, motivated by utilities in remote locations such as the U.S. Virgin Islands. Formulates maintenance as a discounted Markov decision process integrated with high-fidelity hydraulic simulation to represent latent dynamics without pipe-level sensing. Produces state-dependent optimal policies, identifies heterogeneous failure characteristics with rare high-impact behaviors, and shows observable states can act as virtual sensing for specific pipe failures, enabling robust planning under resource constraints.","MARKOV DECISION PROCESS APPROXIMATION METHODS FOR WATER DISTRIBUTION NETWORK INSPECTION AND  \nMAINTENANCE: A CASE STUDY OF THE U. S. VIRGIN ISLANDS  \narXiv :2607 .04626v 1 [ cs .CE] 6 Jul 2026  \nMinsuk Seo  \nRepublic of Korea Army South Korea  \n[smith44189@gmail.com](smith44189@gmail.com)  \nDaniel Eisenberg  \nDepartment of Operations Research Naval Postgraduate School Monterey, CA, USA [daniel.eisenberg@nps.edu](daniel.eisenberg@nps.edu)  \nJefferson Huang  \nDepartment of Operations Research Naval Postgraduate School Monterey, CA, USA [jefferson.huang@nps.edu](jefferson.huang@nps.edu)  \nJuly 7, 2026  \nABSTRACT  \nWe develop a repair-oriented inspection and maintenance decision framework for water distribution networks. This work is motivated by utilities operating in data-sparse environments, such as in remote locations like the U.S. Virgin Islands, where data collection about network state and underground pipeline outages is limited to above-ground and easy to access information (e.g., water tank levels and pump operations) . We formulate the problem as a discounted Markov decision process and integrate it with high-fidelity hydraulic simulation. The model captures latent system dynamics without requiring pipe-level sensing. The results reveal state-dependent optimal policies and heterogeneous failure characteristics across pipes, including rare but high-impact behaviors. We further show that certain observable system states uniquely correspond to specific pipe failures, enabling a form of virtual sensing. These findings demonstrate that system-level dynamics can support inspection planning and maintenance decisions under uncertainty in resource-constrained settings.  \nKeywords Water Distribution Network · Markov Decision Process · Optimal Maintenance Policy · Water Network Tool for Resilience · U.S. Virgin Islands  \n1 Introduction  \nFormulating effective maintenance decisions for critical infrastructure networks presents a formidable challenge for utility service providers (Eisenberg et al., 2019) . For the purposes of this work, maintenance decisions refer to repair actions meant to keep aging and failed assets functioning. In conventional management systems, decision-making typically relies on factors evaluated independently for each asset, such as condition, degradation pattern, failure rates, and resource availability (Andersen et al., 2022 ; Frangopol et al., 2004 ; Beferull-Lozano et al., 2023) . Optimal maintenance decisions in this setting are only constrained by resources (e.g., time, cost, equipment, and manpower) . When the assets are connected into networks to deliver critical services (e.g., pipelines and power grids), repair decisions can have far greater complexity due to the high interdependence of components. Factors such as time delayed impacts, uncertain outage costs, and latent resource constraints complicate analysis and optimization of maintenance policies (Alderson et al., 2014 ; Dickenson, 2014 ; Thomas and Sela, 2024) . For example, in a water distribution network (WDN), cascading failures can propagate with time delays, such that certain segments of the network may not immediately experience the effects of a localized disruption. This means large leaks or pipe failures can exist in a WDN, yet customers may not experience low pressures or outages, even if they are imminent (Bunn, 2018) . Failure to respond prior to a cascade can cause widespread outages from only a few component failures (Shuang et al., 2014) . Optimal maintenance decisions must be able to identify and remediate these highly vulnerable operating states (Alderson et al.,  \nMaintenance decisions are further complicated for utility providers in remote environments with limited equipment or manpower for data collection on the system state (Borgdorff, 2020) . Many critical infrastructure network assets are buried underground, which can make it difficult to assess their operating condition (Alderson et al., 2018 ; Compare et al., 2020) . Uti","cbCaiedmBA9cB9na","https://ap.wps.com/l/cbCaiedmBA9cB9na","pdf",2536243,6,1,25,"English","en",105,"# Introduction\n## Water Distribution Networks\n# Background\n## Repair-Oriented Inspection and Maintenance Framework\n## Discounted Markov Decision Process and Hydraulic Simulation","[{\"question\":\"Why is the proposed maintenance framework designed for data-sparse environments like the U.S. Virgin Islands?\",\"answer\":\"Utilities in such settings have limited access to network state and underground outage information. The framework uses readily available operational observations rather than requiring pipe-level sensing.\"},{\"question\":\"How is the maintenance decision problem formulated in the study?\",\"answer\":\"It is formulated as a discounted Markov decision process. The model is integrated with high-fidelity hydraulic simulation to capture latent system dynamics.\"},{\"question\":\"What do the results say about optimal policies and failures in the network?\",\"answer\":\"The study finds state-dependent optimal policies and heterogeneous failure characteristics across pipes, including rare but high-impact behaviors. It also shows certain observable system states uniquely correspond to specific pipe failures, enabling virtual sensing for inspection planning.\"}]","Markov Decision Process Approximation Methods for Water Distribution Network Inspection and Maintenance | PDF",1784176949,63,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":88,"head_meta":90,"extra_data":92,"updated_unix":29},"markov-decision-process-approximation-methods-for-water-distribution-network-inspection-and-maintenance","",{"@graph":37,"@context":87},[38,55,70],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,52],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":51},"https://docshare.wps.com/document/research-report/",3,{"item":53,"name":13,"@type":44,"position":54},"https://docshare.wps.com/document/markov-decision-process-approximation-methods-for-water-distribution-network-inspection-and-maintenance/81902/",4,{"url":53,"name":13,"@type":56,"author":57,"headline":13,"publisher":59,"fileFormat":62,"inLanguage":24,"description":14,"dateModified":63,"datePublished":64,"encodingFormat":62,"isAccessibleForFree":65,"interactionStatistic":66},"DigitalDocument",{"name":9,"@type":58},"Person",{"url":42,"name":60,"@type":61},"DocShare","Organization","application/pdf","2026-07-29","2026-07-16",true,{"@type":67,"interactionType":68,"userInteractionCount":20},"InteractionCounter",{"@type":69},"ViewAction",{"@type":71,"mainEntity":72},"FAQPage",[73,79,83],{"name":74,"@type":75,"acceptedAnswer":76},"Why is the proposed maintenance framework designed for data-sparse environments like the U.S. Virgin Islands?","Question",{"text":77,"@type":78},"Utilities in such settings have limited access to network state and underground outage information. The framework uses readily available operational observations rather than requiring pipe-level sensing.","Answer",{"name":80,"@type":75,"acceptedAnswer":81},"How is the maintenance decision problem formulated in the study?",{"text":82,"@type":78},"It is formulated as a discounted Markov decision process. The model is integrated with high-fidelity hydraulic simulation to capture latent system dynamics.",{"name":84,"@type":75,"acceptedAnswer":85},"What do the results say about optimal policies and failures in the network?",{"text":86,"@type":78},"The study finds state-dependent optimal policies and heterogeneous failure characteristics across pipes, including rare but high-impact behaviors. 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