{"id":"https://openalex.org/W7125818942","doi":"https://doi.org/10.48550/arxiv.2601.17038","title":"Hybrid Deep Feature Extraction and ML for Construction and Demolition Debris Classification","display_name":"Hybrid Deep Feature Extraction and ML for Construction and Demolition Debris Classification","publication_year":2026,"publication_date":"2026-01-20","ids":{"openalex":"https://openalex.org/W7125818942","doi":"https://doi.org/10.48550/arxiv.2601.17038"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2601.17038","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2601.17038","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2601.17038","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5123923585","display_name":"Obai Alashram","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Alashram, Obai","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5124009696","display_name":"Nejad Alagha","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Alagha, Nejad","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5123967131","display_name":"Mahmoud AlKakuri","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"AlKakuri, Mahmoud","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5123906045","display_name":"Zeeshan Swaveel","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Swaveel, Zeeshan","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5123913376","display_name":"Abigail Copiaco","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Copiaco, Abigail","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5123923585"],"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/T11847","display_name":"Recycled Aggregate Concrete Performance","score":0.7279000282287598,"subfield":{"id":"https://openalex.org/subfields/2215","display_name":"Building and Construction"},"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/T11847","display_name":"Recycled Aggregate Concrete Performance","score":0.7279000282287598,"subfield":{"id":"https://openalex.org/subfields/2215","display_name":"Building and Construction"},"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/T11006","display_name":"BIM and Construction Integration","score":0.07010000199079514,"subfield":{"id":"https://openalex.org/subfields/2215","display_name":"Building and Construction"},"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":0.06560000032186508,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.6129999756813049},{"id":"https://openalex.org/keywords/pipeline","display_name":"Pipeline (software)","score":0.6001999974250793},{"id":"https://openalex.org/keywords/demolition","display_name":"Demolition","score":0.5684000253677368},{"id":"https://openalex.org/keywords/automation","display_name":"Automation","score":0.5666999816894531},{"id":"https://openalex.org/keywords/debris","display_name":"Debris","score":0.5497000217437744},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5271000266075134},{"id":"https://openalex.org/keywords/sorting","display_name":"Sorting","score":0.5110999941825867},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4875999987125397}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7181000113487244},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.6129999756813049},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.6001999974250793},{"id":"https://openalex.org/C2781469121","wikidata":"https://www.wikidata.org/wiki/Q331483","display_name":"Demolition","level":2,"score":0.5684000253677368},{"id":"https://openalex.org/C115901376","wikidata":"https://www.wikidata.org/wiki/Q184199","display_name":"Automation","level":2,"score":0.5666999816894531},{"id":"https://openalex.org/C2776023875","wikidata":"https://www.wikidata.org/wiki/Q637703","display_name":"Debris","level":2,"score":0.5497000217437744},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5271000266075134},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5166000127792358},{"id":"https://openalex.org/C111696304","wikidata":"https://www.wikidata.org/wiki/Q2303697","display_name":"Sorting","level":2,"score":0.5110999941825867},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4875999987125397},{"id":"https://openalex.org/C175309249","wikidata":"https://www.wikidata.org/wiki/Q725864","display_name":"Pipeline transport","level":2,"score":0.4571000039577484},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.36469998955726624},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.35030001401901245},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.3449000120162964},{"id":"https://openalex.org/C2778076428","wikidata":"https://www.wikidata.org/wiki/Q15798477","display_name":"Demolition waste","level":3,"score":0.32839998602867126},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.32409998774528503},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.319599986076355},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3192000091075897},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.304500013589859},{"id":"https://openalex.org/C4725764","wikidata":"https://www.wikidata.org/wiki/Q844704","display_name":"Extraction (chemistry)","level":2,"score":0.29910001158714294},{"id":"https://openalex.org/C34413123","wikidata":"https://www.wikidata.org/wiki/Q170978","display_name":"Robotics","level":3,"score":0.29260000586509705},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.28610000014305115},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.2757999897003174},{"id":"https://openalex.org/C5339829","wikidata":"https://www.wikidata.org/wiki/Q1425977","display_name":"Machine vision","level":2,"score":0.26759999990463257},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2581000030040741},{"id":"https://openalex.org/C206345919","wikidata":"https://www.wikidata.org/wiki/Q20380951","display_name":"Resource (disambiguation)","level":2,"score":0.25429999828338623}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2601.17038","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2601.17038","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2601.17038","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2601.17038","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":"article"},"sustainable_development_goals":[{"score":0.4838145971298218,"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"The":[0,102,141],"construction":[1,41,68],"industry":[2],"produces":[3],"significant":[4],"volumes":[5],"of":[6,147],"debris,":[7],"making":[8],"effective":[9],"sorting":[10],"and":[11,18,42,62,86,96,119,130,155,163],"classification":[12],"critical":[13],"for":[14,39,150,158],"sustainable":[15],"waste":[16],"management":[17],"resource":[19],"recovery.":[20],"This":[21],"study":[22],"presents":[23],"a":[24,82],"hybrid":[25,106],"vision-based":[26],"pipeline":[27],"that":[28,105],"integrates":[29],"deep":[30,138],"feature":[31],"extraction":[32],"with":[33,111,125,161],"classical":[34],"machine":[35],"learning":[36,139],"(ML)":[37],"classifiers":[38,113],"automated":[40],"demolition":[43],"(C\\&amp;D)":[44],"debris":[45,153],"classification.":[46],"A":[47],"novel":[48],"dataset":[49],"comprising":[50],"1,800":[51],"balanced,":[52],"high-quality":[53],"images":[54],"representing":[55],"four":[56],"material":[57],"categories,":[58],"Ceramic/Tile,":[59],"Concrete,":[60],"Trash/Waste,":[61],"Wood":[63],"was":[64],"collected":[65],"from":[66],"real":[67],"sites":[69],"in":[70],"the":[71,144],"UAE,":[72],"capturing":[73],"diverse":[74],"real-world":[75],"conditions.":[76],"Deep":[77],"features":[78,110],"were":[79,99],"extracted":[80],"using":[81,108],"pre-trained":[83],"Xception":[84,109],"network,":[85],"multiple":[87],"ML":[88],"classifiers,":[89],"including":[90],"SVM,":[91,117],"kNN,":[92,118],"Bagged":[93,120],"Trees,":[94],"LDA,":[95],"Logistic":[97],"Regression,":[98],"systematically":[100],"evaluated.":[101],"results":[103],"demonstrate":[104],"pipelines":[107],"simple":[112],"such":[114],"as":[115],"Linear":[116],"Trees":[121],"achieve":[122],"state-of-the-art":[123],"performance,":[124],"up":[126],"to":[127],"99.5\\%":[128],"accuracy":[129],"macro-F1":[131],"scores,":[132],"surpassing":[133],"more":[134],"complex":[135],"or":[136],"end-to-end":[137],"approaches.":[140],"analysis":[142],"highlights":[143],"operational":[145],"benefits":[146],"this":[148],"approach":[149],"robust,":[151],"field-deployable":[152],"identification":[154],"provides":[156],"pathways":[157],"future":[159],"integration":[160],"robotics":[162],"onsite":[164],"automation":[165],"systems.":[166]},"counts_by_year":[],"updated_date":"2026-01-28T23:18:48.515280","created_date":"2026-01-28T00:00:00"}
