{"id":{"repo_id":"vt","oai_identifier":"oai:vtechworks.lib.vt.edu:10919/136940"},"canonical_url":"https://search.dev.ndltd.org/etd/vt/oai:vtechworks.lib.vt.edu:10919/136940","repository":{"repo_id":"vt","name":"Virginia Tech","base_url":"https://vtechworks.lib.vt.edu/oai/request"},"display":{"title":"Fuzzy Logic-based Economic Model for Water Main Renewal","abstract":"Drinking water mains are failing at accelerating rates nationwide, leaving managers without robust, consistent, validated tools to prioritize renewal projects in uncertain conditions. The research leveraged empirical pipeline data and advanced modeling technique to address the issue, beginning with the creation of a standard cost data framework developed using nearly 200 industry case studies from over 30 North American utilities. The data standard enabled an economic modeling study of common renewal tactics via life cycle costing and equivalent uniform cost comparisons of competing interventions over time. The modeling was tested through both deterministic and probabilistic methods to reveal the sensitivity of the models to variations/errors in key inputs such as discount rate, project costs, and pipe service life. Despite these efforts the economic models proved unreliable in accurately choosing the best intervention and timing when compared with best practices as defined by AWWA per the in-situ conditions, seeing only an average 63% match. To overcome the limitations, fuzzy modeling techniques were developed and applied using critical physical, spatial, and economic parameters driving pipe performance (St. Clair, 2015)(Ge 2017). The Type-2 model was trained and calibrated using nearly 37,000 break and advanced condition assessment records collected from participating utilities. The fuzzy model greatly outperformed the deterministic economic decision-making heuristics by achieving an 85% match with best practices in intervention selection and timing. The dissertation finally provides guidance on the deterministic vs fuzzy modeling techniques for practical implementation, addressing the benefits of integration with GIS for advanced asset management. The research provides a novel, results-backed approach that enables utilities to make defensible decisions under uncertainty amidst rapidly deteriorating critical water infrastructure.","abstract_html":"Drinking water mains are failing at accelerating rates nationwide, leaving managers without robust, consistent, validated tools to prioritize renewal projects in uncertain conditions. The research leveraged empirical pipeline data and advanced modeling technique to address the issue, beginning with the creation of a standard cost data framework developed using nearly 200 industry case studies from over 30 North American utilities. The data standard enabled an economic modeling study of common renewal tactics via life cycle costing and equivalent uniform cost comparisons of competing interventions over time. The modeling was tested through both deterministic and probabilistic methods to reveal the sensitivity of the models to variations/errors in key inputs such as discount rate, project costs, and pipe service life. Despite these efforts the economic models proved unreliable in accurately choosing the best intervention and timing when compared with best practices as defined by AWWA per the in-situ conditions, seeing only an average 63% match. To overcome the limitations, fuzzy modeling techniques were developed and applied using critical physical, spatial, and economic parameters driving pipe performance (St. Clair, 2015)(Ge 2017). The Type-2 model was trained and calibrated using nearly 37,000 break and advanced condition assessment records collected from participating utilities. The fuzzy model greatly outperformed the deterministic economic decision-making heuristics by achieving an 85% match with best practices in intervention selection and timing. The dissertation finally provides guidance on the deterministic vs fuzzy modeling techniques for practical implementation, addressing the benefits of integration with GIS for advanced asset management. The research provides a novel, results-backed approach that enables utilities to make defensible decisions under uncertainty amidst rapidly deteriorating critical water infrastructure.","abstract_has_math":false,"creators":["Welling, Stephen Michael"],"institution":"Virginia Tech","degree_name":"Doctor of Philosophy","degree_level":"doctoral","degree_discipline":"Civil Engineering","degree_department":"Civil and Environmental Engineering","school":null,"contributors":[],"advisors":[],"committee_chairs":["Sinha, Sunil Kumar"],"committee_members":["Deane, Jason K.","Hancock, Kathleen","Edwards, Marc A."],"year":2025,"date_issued":"2025-08-01","date_published":"2025-08-01","updated_at":"2026-07-22T22:19:13Z","subjects":["Drinking water infrastructure","water main renewal","fuzzy logic decision-making","Type-2 fuzzy systems","condition assessment","risk-based planning","GIS-based prioritization","life cycle cost analysis"],"languages":["en"],"rights":["In Copyright"],"rights_urls":["http://rightsstatements.org/vocab/InC/1.0/"],"identifier_entries":[{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["vt_gsexam:44375"],"render_values":[{"text":"vt_gsexam:44375","href":null,"code":true}]}]},"links":{"outbound_url":"https://hdl.handle.net/10919/136940","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.committeechair","label":"Committee Chair","values":["Sinha, Sunil Kumar"]},{"key":"dc:contributor.committeemember","label":"Committee Member","values":["Deane, Jason K.","Hancock, Kathleen","Edwards, Marc A."]},{"key":"dc:contributor.department","label":"Department","values":["Civil and Environmental Engineering"]},{"key":"dc:creator","label":"Author","values":["Welling, Stephen Michael"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2025-08-02T08:00:17Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2025-08-02T08:00:17Z"]},{"key":"dc:date.issued","label":"Date","values":["2025-08-01"]},{"key":"dc:publisher","label":"Institution","values":["Virginia Tech"]},{"key":"dc:type","label":"Dc Type","values":["Dissertation"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Civil Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["doctoral"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Doctor of Philosophy"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["Virginia Polytechnic Institute and State University"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Drinking water infrastructure","water main renewal","fuzzy logic decision-making","Type-2 fuzzy systems","condition assessment","risk-based planning","GIS-based prioritization","life cycle cost analysis"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["In Copyright"]},{"key":"dc:rights.uri","label":"Rights URI","values":["http://rightsstatements.org/vocab/InC/1.0/"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["vt_gsexam:44375"]},{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/10919/136940"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Drinking water mains are failing at accelerating rates nationwide, leaving managers without robust, consistent, validated tools to prioritize renewal projects in uncertain conditions. The research leveraged empirical pipeline data and advanced modeling technique to address the issue, beginning with the creation of a standard cost data framework developed using nearly 200 industry case studies from over 30 North American utilities. The data standard enabled an economic modeling study of common renewal tactics via life cycle costing and equivalent uniform cost comparisons of competing interventions over time. The modeling was tested through both deterministic and probabilistic methods to reveal the sensitivity of the models to variations/errors in key inputs such as discount rate, project costs, and pipe service life. Despite these efforts the economic models proved unreliable in accurately choosing the best intervention and timing when compared with best practices as defined by AWWA per the in-situ conditions, seeing only an average 63% match. To overcome the limitations, fuzzy modeling techniques were developed and applied using critical physical, spatial, and economic parameters driving pipe performance (St. Clair, 2015)(Ge 2017). The Type-2 model was trained and calibrated using nearly 37,000 break and advanced condition assessment records collected from participating utilities. The fuzzy model greatly outperformed the deterministic economic decision-making heuristics by achieving an 85% match with best practices in intervention selection and timing. The dissertation finally provides guidance on the deterministic vs fuzzy modeling techniques for practical implementation, addressing the benefits of integration with GIS for advanced asset management. The research provides a novel, results-backed approach that enables utilities to make defensible decisions under uncertainty amidst rapidly deteriorating critical water infrastructure."]},{"key":"dc:description.abstractgeneral","label":"General Abstract","values":["Water utilities across the US face mounting pressure to manage their aging water pipes wisely under increasing risk and limited budgets. Traditional methods often rely on outdated processes that consequently lead to poor replacement timing. This research includes a new model that utilizes fuzzy logic, a mathematical tool that works well even when there is inaccurate or missing data for the utilities to plan with. The model uses 41 factors including pipe condition and economic indicator to select which pipes should be replaced first. The model was developed and calibrated using nearly 27,000 instances of past utility pipe repairs and inspections, then proven in live workshops with utilities covering the West Coast, Midwest, and East Coast. It accurately matched expert recommendations over 80% of the time, thereby reducing errant project selection by 27% yet limiting catastrophic failures by 15% when compared to older methods. The findings/recommendations help save money, reduce disruptions, and protect the public and environment by improving decision making."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["Doctor of Philosophy"]},{"key":"dc:format.medium","label":"Dc Format Medium","values":["ETD"]},{"key":"dc:title","label":"Title","values":["Fuzzy Logic-based Economic Model for Water Main Renewal"]}]}],"canonical_facts":{"dc:contributor.committeechair":["Sinha, Sunil Kumar"],"dc:contributor.committeemember":["Deane, Jason K.","Hancock, Kathleen","Edwards, Marc A."],"dc:contributor.department":["Civil and Environmental Engineering"],"dc:creator":["Welling, Stephen Michael"],"dc:date.accessioned":["2025-08-02T08:00:17Z"],"dc:date.available":["2025-08-02T08:00:17Z"],"dc:date.issued":["2025-08-01"],"dc:description.abstract":["Drinking water mains are failing at accelerating rates nationwide, leaving managers without robust, consistent, validated tools to prioritize renewal projects in uncertain conditions. 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To overcome the limitations, fuzzy modeling techniques were developed and applied using critical physical, spatial, and economic parameters driving pipe performance (St. Clair, 2015)(Ge 2017). The Type-2 model was trained and calibrated using nearly 37,000 break and advanced condition assessment records collected from participating utilities. The fuzzy model greatly outperformed the deterministic economic decision-making heuristics by achieving an 85% match with best practices in intervention selection and timing. The dissertation finally provides guidance on the deterministic vs fuzzy modeling techniques for practical implementation, addressing the benefits of integration with GIS for advanced asset management. The research provides a novel, results-backed approach that enables utilities to make defensible decisions under uncertainty amidst rapidly deteriorating critical water infrastructure."],"dc:description.abstractgeneral":["Water utilities across the US face mounting pressure to manage their aging water pipes wisely under increasing risk and limited budgets. Traditional methods often rely on outdated processes that consequently lead to poor replacement timing. This research includes a new model that utilizes fuzzy logic, a mathematical tool that works well even when there is inaccurate or missing data for the utilities to plan with. The model uses 41 factors including pipe condition and economic indicator to select which pipes should be replaced first. The model was developed and calibrated using nearly 27,000 instances of past utility pipe repairs and inspections, then proven in live workshops with utilities covering the West Coast, Midwest, and East Coast. It accurately matched expert recommendations over 80% of the time, thereby reducing errant project selection by 27% yet limiting catastrophic failures by 15% when compared to older methods. The findings/recommendations help save money, reduce disruptions, and protect the public and environment by improving decision making."],"dc:description.degree":["Doctor of Philosophy"],"dc:format.medium":["ETD"],"dc:identifier.other":["vt_gsexam:44375"],"dc:identifier.uri":["https://hdl.handle.net/10919/136940"],"dc:language.iso":["en"],"dc:publisher":["Virginia Tech"],"dc:rights":["In Copyright"],"dc:rights.uri":["http://rightsstatements.org/vocab/InC/1.0/"],"dc:subject":["Drinking water infrastructure","water main renewal","fuzzy logic decision-making","Type-2 fuzzy systems","condition assessment","risk-based planning","GIS-based prioritization","life cycle cost analysis"],"dc:title":["Fuzzy Logic-based Economic Model for Water Main Renewal"],"dc:type":["Dissertation"],"thesis:degree_discipline":["Civil Engineering"],"thesis:degree_level":["doctoral"],"thesis:degree_name":["Doctor of Philosophy"],"thesis:institution_name":["Virginia Polytechnic Institute and State University"]},"updated_at":"2026-07-22T22:19:13Z"}