{"id":"https://openalex.org/W7172056033","doi":"https://doi.org/10.48550/arxiv.2607.28481","title":"A Fuzzy Rule-based Neuro-Symbolic Approach for Pipe Severity Prediction in Sewer Networks","display_name":"A Fuzzy Rule-based Neuro-Symbolic Approach for Pipe Severity Prediction in Sewer Networks","publication_year":2026,"publication_date":"2026-07-30","ids":{"openalex":"https://openalex.org/W7172056033","doi":"https://doi.org/10.48550/arxiv.2607.28481"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2607.28481","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.28481","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2607.28481","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5144141550","display_name":"Ngoc Thai Le","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Le, Ngoc Thai","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5144112145","display_name":"Thanh Ma","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ma, Thanh","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5021966059","display_name":"Umberto Straccia","orcid":"https://orcid.org/0000-0001-5998-6757"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Straccia, Umberto","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11220","display_name":"Water Systems and Optimization","score":0.6331999897956848,"subfield":{"id":"https://openalex.org/subfields/2205","display_name":"Civil and Structural Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11220","display_name":"Water Systems and Optimization","score":0.6331999897956848,"subfield":{"id":"https://openalex.org/subfields/2205","display_name":"Civil and Structural Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11606","display_name":"Infrastructure Maintenance and Monitoring","score":0.2655999958515167,"subfield":{"id":"https://openalex.org/subfields/2205","display_name":"Civil and Structural Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11119","display_name":"Urban Stormwater Management Solutions","score":0.013000000268220901,"subfield":{"id":"https://openalex.org/subfields/2305","display_name":"Environmental Engineering"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/fuzzy-logic","display_name":"Fuzzy logic","score":0.5497999787330627},{"id":"https://openalex.org/keywords/code","display_name":"Code (set theory)","score":0.49149999022483826},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.48969998955726624},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.40119999647140503},{"id":"https://openalex.org/keywords/adaptive-neuro-fuzzy-inference-system","display_name":"Adaptive neuro fuzzy inference system","score":0.3601999878883362},{"id":"https://openalex.org/keywords/operator","display_name":"Operator (biology)","score":0.3596999943256378},{"id":"https://openalex.org/keywords/decoupling","display_name":"Decoupling (probability)","score":0.35120001435279846},{"id":"https://openalex.org/keywords/visual-inspection","display_name":"Visual inspection","score":0.3476000130176544}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5504000186920166},{"id":"https://openalex.org/C58166","wikidata":"https://www.wikidata.org/wiki/Q224821","display_name":"Fuzzy logic","level":2,"score":0.5497999787330627},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5203999876976013},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.49149999022483826},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.48969998955726624},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4681999981403351},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.40560001134872437},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.40119999647140503},{"id":"https://openalex.org/C186108316","wikidata":"https://www.wikidata.org/wiki/Q352530","display_name":"Adaptive neuro fuzzy inference system","level":4,"score":0.3601999878883362},{"id":"https://openalex.org/C17020691","wikidata":"https://www.wikidata.org/wiki/Q139677","display_name":"Operator (biology)","level":5,"score":0.3596999943256378},{"id":"https://openalex.org/C205606062","wikidata":"https://www.wikidata.org/wiki/Q5249645","display_name":"Decoupling (probability)","level":2,"score":0.35120001435279846},{"id":"https://openalex.org/C168820333","wikidata":"https://www.wikidata.org/wiki/Q448889","display_name":"Visual inspection","level":2,"score":0.3476000130176544},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.3303999900817871},{"id":"https://openalex.org/C42011625","wikidata":"https://www.wikidata.org/wiki/Q1055058","display_name":"Fuzzy set","level":3,"score":0.3296999931335449},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3269999921321869},{"id":"https://openalex.org/C2780069185","wikidata":"https://www.wikidata.org/wiki/Q7977945","display_name":"Equivalence (formal languages)","level":2,"score":0.32339999079704285},{"id":"https://openalex.org/C26760741","wikidata":"https://www.wikidata.org/wiki/Q160402","display_name":"Perception","level":2,"score":0.30869999527931213},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.29089999198913574},{"id":"https://openalex.org/C2780049643","wikidata":"https://www.wikidata.org/wiki/Q5511139","display_name":"Fuzzy rule","level":4,"score":0.2825999855995178},{"id":"https://openalex.org/C2987376176","wikidata":"https://www.wikidata.org/wiki/Q224821","display_name":"Fuzzy inference system","level":5,"score":0.28049999475479126},{"id":"https://openalex.org/C166955791","wikidata":"https://www.wikidata.org/wiki/Q629579","display_name":"Macro","level":2,"score":0.27489998936653137},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2727000117301941},{"id":"https://openalex.org/C43126263","wikidata":"https://www.wikidata.org/wiki/Q128751","display_name":"Source code","level":2,"score":0.2720000147819519},{"id":"https://openalex.org/C774472","wikidata":"https://www.wikidata.org/wiki/Q6760393","display_name":"Margin (machine learning)","level":2,"score":0.2696000039577484}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2607.28481","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.28481","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2607.28481","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.28481","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Standard":[0],"automated":[1],"sewer":[2],"pipe":[3],"severity":[4,23,78,133,194],"assessment":[5],"relies":[6],"on":[7,74],"direct":[8],"image":[9],"classification,":[10],"creating":[11],"a":[12,30,51,66,124],"\"black":[13],"box\"":[14],"where":[15],"the":[16,177],"link":[17],"between":[18],"visual":[19],"defects":[20],"and":[21,77,80,105,119,163,169,193],"final":[22],"scores":[24],"remains":[25],"implicit.":[26],"This":[27],"study":[28],"introduces":[29],"modular,":[31],"fuzzy":[32,93],"rule-based":[33],"neuro-symbolic":[34],"framework":[35,178],"that":[36],"bridges":[37],"this":[38],"gap":[39],"by":[40,102,165],"decoupling":[41],"neural":[42],"perception":[43,48],"from":[44,62,97,142,186],"symbolic":[45],"reasoning.":[46],"The":[47],"module":[49],"utilizes":[50],"Swin":[52],"Transformer":[53],"to":[54,110,190],"predict":[55],"14":[56],"multilabel":[57],"inspection":[58],"CODE":[59,98,188],"degrees":[60,189],"directly":[61],"images.":[63],"For":[64],"reasoning,":[65],"DT,":[67],"specifically":[68],"Weka's":[69],"J48,":[70],"algorithm":[71],"is":[72],"trained":[73],"ground-truth":[75],"CODEs":[76],"labels,":[79],"its":[81],"paths":[82],"are":[83,100],"converted":[84],"into":[85],"19":[86],"fixed":[87],"IF--THEN":[88],"rules.":[89],"Inference":[90],"operates":[91],"via":[92,140],"logic:":[94],"t-norm":[95],"activations":[96],"conditions":[99],"weighted":[101],"rule":[103,191],"confidence":[104],"combined":[106],"with":[107,183],"corresponding":[108],"s-norms":[109],"produce":[111],"interpretable":[112],"class":[113],"evidence.":[114,195],"We":[115],"assessed":[116],"Product,":[117],"\u0141ukasiewicz,":[118],"Hamacher":[120],"operator":[121],"pairs":[122],"using":[123],"dataset":[125],"of":[126,157],"3,244":[127],"images":[128],"spanning":[129],"five":[130,143],"highly":[131],"imbalanced":[132],"classes.":[134],"Ground-truth":[135],"labels":[136],"were":[137],"robustly":[138],"generated":[139],"consensus":[141],"independent":[144],"large":[145],"language":[146],"models":[147],"analyzing":[148],"original":[149],"inspector":[150],"notes.":[151],"Our":[152],"results":[153],"show":[154],"an":[155],"improvement":[156],"accuracy,":[158,160],"balanced":[159],"Macro":[161],"F1":[162],"MCC":[164],"17.9%,":[166],"12.2%,":[167],"23.0%,":[168],"17.3%,":[170],"respectively,":[171],"over":[172],"image-only":[173],"based":[174],"classification.":[175],"Overall,":[176],"combines":[179],"competitive":[180],"class-balanced":[181],"performance":[182],"traceable":[184],"reasoning":[185],"predicted":[187],"supports":[192]},"counts_by_year":[],"updated_date":"2026-08-01T09:00:35.917206","created_date":"2026-08-01T00:00:00"}