{"id":"https://openalex.org/W4408938679","doi":"https://doi.org/10.3390/make7020030","title":"Comparative Analysis of Machine Learning Techniques for Predicting Bulk Specific Gravity in Modified Asphalt Mixtures Incorporating Polyethylene Terephthalate (PET), High-Density Polyethylene (HDPE), and Polyvinyl Chloride (PVC)","display_name":"Comparative Analysis of Machine Learning Techniques for Predicting Bulk Specific Gravity in Modified Asphalt Mixtures Incorporating Polyethylene Terephthalate (PET), High-Density Polyethylene (HDPE), and Polyvinyl Chloride (PVC)","publication_year":2025,"publication_date":"2025-03-27","ids":{"openalex":"https://openalex.org/W4408938679","doi":"https://doi.org/10.3390/make7020030"},"language":"en","primary_location":{"id":"doi:10.3390/make7020030","is_oa":true,"landing_page_url":"https://doi.org/10.3390/make7020030","pdf_url":"https://www.mdpi.com/2504-4990/7/2/30/pdf?version=1743081056","source":{"id":"https://openalex.org/S4210213891","display_name":"Machine Learning and Knowledge Extraction","issn_l":"2504-4990","issn":["2504-4990"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Machine Learning and Knowledge Extraction","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/2504-4990/7/2/30/pdf?version=1743081056","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5050300793","display_name":"Bhupender Kumar","orcid":"https://orcid.org/0009-0002-3874-0365"},"institutions":[{"id":"https://openalex.org/I4210133311","display_name":"Shoolini University","ror":"https://ror.org/02xe2fg84","country_code":"IN","type":"education","lineage":["https://openalex.org/I4210133311"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Bhupender Kumar","raw_affiliation_strings":["Department of Civil Engineering, Shoolini University, Solan 173229, Himachal Pradesh, India"],"raw_orcid":"https://orcid.org/0009-0002-3874-0365","affiliations":[{"raw_affiliation_string":"Department of Civil Engineering, Shoolini University, Solan 173229, Himachal Pradesh, India","institution_ids":["https://openalex.org/I4210133311"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5073151673","display_name":"Navsal Kumar","orcid":"https://orcid.org/0000-0002-6461-1041"},"institutions":[{"id":"https://openalex.org/I4210133311","display_name":"Shoolini University","ror":"https://ror.org/02xe2fg84","country_code":"IN","type":"education","lineage":["https://openalex.org/I4210133311"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Navsal Kumar","raw_affiliation_strings":["Department of Civil Engineering, Shoolini University, Solan 173229, Himachal Pradesh, India"],"raw_orcid":"https://orcid.org/0000-0002-6461-1041","affiliations":[{"raw_affiliation_string":"Department of Civil Engineering, Shoolini University, Solan 173229, Himachal Pradesh, India","institution_ids":["https://openalex.org/I4210133311"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5023387410","display_name":"Rabee Rustum","orcid":"https://orcid.org/0000-0001-6938-8144"},"institutions":[{"id":"https://openalex.org/I4210149788","display_name":"Heriot-Watt University Dubai","ror":"https://ror.org/058tx1a56","country_code":"AE","type":"education","lineage":["https://openalex.org/I4210149788"]}],"countries":["AE"],"is_corresponding":true,"raw_author_name":"Rabee Rustum","raw_affiliation_strings":["Institute of Sustainable Built Environment, School of Energy, Geosciences, Infrastructure, and Society, Dubai Campus, Dubai Knowledge Park, Heriot-Watt University, Dubai P.O. Box 501745, United Arab Emirates"],"raw_orcid":"https://orcid.org/0000-0001-6938-8144","affiliations":[{"raw_affiliation_string":"Institute of Sustainable Built Environment, School of Energy, Geosciences, Infrastructure, and Society, Dubai Campus, Dubai Knowledge Park, Heriot-Watt University, Dubai P.O. Box 501745, United Arab Emirates","institution_ids":["https://openalex.org/I4210149788"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5006074028","display_name":"Vijay Shankar","orcid":"https://orcid.org/0000-0002-9509-6804"},"institutions":[{"id":"https://openalex.org/I36909309","display_name":"National Institute of Technology Hamirpur","ror":"https://ror.org/01nc8zs04","country_code":"IN","type":"education","lineage":["https://openalex.org/I36909309","https://openalex.org/I4210152752"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Vijay Shankar","raw_affiliation_strings":["Department of Civil Engineering, National Institute of Technology Hamirpur, Hamirpur 177005, Himachal Pradesh, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Civil Engineering, National Institute of Technology Hamirpur, Hamirpur 177005, Himachal Pradesh, India","institution_ids":["https://openalex.org/I36909309"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5023387410"],"corresponding_institution_ids":["https://openalex.org/I4210149788"],"apc_list":{"value":1400,"currency":"CHF","value_usd":1515},"apc_paid":{"value":1400,"currency":"CHF","value_usd":1515},"fwci":3.0135,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.89717457,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":95,"max":99},"biblio":{"volume":"7","issue":"2","first_page":"30","last_page":"30"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10264","display_name":"Asphalt Pavement Performance Evaluation","score":0.9998999834060669,"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/T10264","display_name":"Asphalt Pavement Performance Evaluation","score":0.9998999834060669,"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":0.9988999962806702,"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/T10892","display_name":"Drilling and Well Engineering","score":0.957099974155426,"subfield":{"id":"https://openalex.org/subfields/2212","display_name":"Ocean 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/polyvinyl-chloride","display_name":"Polyvinyl chloride","score":0.9262759685516357},{"id":"https://openalex.org/keywords/high-density-polyethylene","display_name":"High-density polyethylene","score":0.8658289909362793},{"id":"https://openalex.org/keywords/polyethylene-terephthalate","display_name":"Polyethylene terephthalate","score":0.7610242366790771},{"id":"https://openalex.org/keywords/materials-science","display_name":"Materials science","score":0.7047512531280518},{"id":"https://openalex.org/keywords/polyethylene","display_name":"Polyethylene","score":0.6735801100730896},{"id":"https://openalex.org/keywords/composite-material","display_name":"Composite material","score":0.5892677307128906},{"id":"https://openalex.org/keywords/asphalt","display_name":"Asphalt","score":0.584869384765625},{"id":"https://openalex.org/keywords/specific-gravity","display_name":"Specific gravity","score":0.5312941670417786},{"id":"https://openalex.org/keywords/polymer-science","display_name":"Polymer science","score":0.3685135841369629}],"concepts":[{"id":"https://openalex.org/C2776866176","wikidata":"https://www.wikidata.org/wiki/Q146368","display_name":"Polyvinyl chloride","level":2,"score":0.9262759685516357},{"id":"https://openalex.org/C181748761","wikidata":"https://www.wikidata.org/wiki/Q2641430","display_name":"High-density polyethylene","level":3,"score":0.8658289909362793},{"id":"https://openalex.org/C2780017307","wikidata":"https://www.wikidata.org/wiki/Q145863","display_name":"Polyethylene terephthalate","level":2,"score":0.7610242366790771},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.7047512531280518},{"id":"https://openalex.org/C2779751980","wikidata":"https://www.wikidata.org/wiki/Q143429","display_name":"Polyethylene","level":2,"score":0.6735801100730896},{"id":"https://openalex.org/C159985019","wikidata":"https://www.wikidata.org/wiki/Q181790","display_name":"Composite material","level":1,"score":0.5892677307128906},{"id":"https://openalex.org/C168056786","wikidata":"https://www.wikidata.org/wiki/Q202251","display_name":"Asphalt","level":2,"score":0.584869384765625},{"id":"https://openalex.org/C93656029","wikidata":"https://www.wikidata.org/wiki/Q11027905","display_name":"Specific gravity","level":2,"score":0.5312941670417786},{"id":"https://openalex.org/C126348684","wikidata":"https://www.wikidata.org/wiki/Q3456979","display_name":"Polymer science","level":1,"score":0.3685135841369629}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.3390/make7020030","is_oa":true,"landing_page_url":"https://doi.org/10.3390/make7020030","pdf_url":"https://www.mdpi.com/2504-4990/7/2/30/pdf?version=1743081056","source":{"id":"https://openalex.org/S4210213891","display_name":"Machine Learning and Knowledge Extraction","issn_l":"2504-4990","issn":["2504-4990"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Machine Learning and Knowledge Extraction","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:d59e047d53f9498f9009120d5aa23e75","is_oa":true,"landing_page_url":"https://doaj.org/article/d59e047d53f9498f9009120d5aa23e75","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Machine Learning and Knowledge Extraction, Vol 7, Iss 2, p 30 (2025)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.3390/make7020030","is_oa":true,"landing_page_url":"https://doi.org/10.3390/make7020030","pdf_url":"https://www.mdpi.com/2504-4990/7/2/30/pdf?version=1743081056","source":{"id":"https://openalex.org/S4210213891","display_name":"Machine Learning and Knowledge Extraction","issn_l":"2504-4990","issn":["2504-4990"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Machine Learning and Knowledge Extraction","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":false},"content_urls":{"pdf":"https://content.openalex.org/works/W4408938679.pdf"},"referenced_works_count":34,"referenced_works":["https://openalex.org/W202107547","https://openalex.org/W574919590","https://openalex.org/W1970493442","https://openalex.org/W1976405057","https://openalex.org/W2048370585","https://openalex.org/W2104189772","https://openalex.org/W2138364347","https://openalex.org/W2603020246","https://openalex.org/W2781685889","https://openalex.org/W2899485145","https://openalex.org/W2911912670","https://openalex.org/W2967859107","https://openalex.org/W2969642948","https://openalex.org/W2977188273","https://openalex.org/W3018047004","https://openalex.org/W3120346372","https://openalex.org/W3164357010","https://openalex.org/W3181467079","https://openalex.org/W4233326182","https://openalex.org/W4285088202","https://openalex.org/W4307853673","https://openalex.org/W4310798739","https://openalex.org/W4318480108","https://openalex.org/W4323048435","https://openalex.org/W4379365294","https://openalex.org/W4381251231","https://openalex.org/W4393040298","https://openalex.org/W4399811965","https://openalex.org/W4400091435","https://openalex.org/W4400564185","https://openalex.org/W6846894560","https://openalex.org/W6853458738","https://openalex.org/W6870085066","https://openalex.org/W6870395242"],"related_works":["https://openalex.org/W4323308780","https://openalex.org/W196197793","https://openalex.org/W2321825242","https://openalex.org/W2981671535","https://openalex.org/W2265427069","https://openalex.org/W2057106577","https://openalex.org/W2376601710","https://openalex.org/W2129993533","https://openalex.org/W2090305450","https://openalex.org/W1594900008"],"abstract_inverted_index":{"In":[0],"today\u2019s":[1],"rapidly":[2],"evolving":[3],"transportation":[4],"infrastructure,":[5],"developing":[6],"long-lasting,":[7],"high-performance":[8],"pavement":[9],"materials":[10],"remains":[11],"a":[12,21,237],"significant":[13],"priority.":[14],"Integrating":[15],"machine":[16,226],"learning":[17],"(ML)":[18],"techniques":[19],"provides":[20],"transformative":[22],"approach":[23,65],"to":[24,56,68,244],"optimizing":[25,231],"asphalt":[26,54,75,217,233],"mix":[27,218,234],"design":[28],"and":[29,47,59,93,122,152,171,187,197,240],"performance":[30,108],"prediction.":[31],"This":[32,220],"study":[33,221],"investigates":[34],"the":[35,70,111,132,141,153,176,179,203,210,223],"use":[36],"of":[37,74,101,178,199,225],"waste":[38],"plastics,":[39],"including":[40,81,110],"Polyethylene":[41,45],"Terephthalate":[42],"(PET),":[43],"High-Density":[44],"(HDPE),":[46],"Polyvinyl":[48],"Chloride":[49],"(PVC),":[50],"as":[51,202],"modifiers":[52],"in":[53,216,230],"concrete":[55,76],"enhance":[57],"durability":[58],"mechanical":[60],"performance.":[61],"A":[62,166],"predictive":[63,228],"modeling":[64,229],"was":[66,104],"employed":[67],"estimate":[69],"bulk-specific":[71],"gravity":[72],"(Gmb)":[73],"using":[77,106],"various":[78],"ML":[79,138],"techniques,":[80,139],"Artificial":[82],"Neural":[83],"Networks":[84],"(ANNs),":[85],"Support":[86],"Vector":[87],"Machines":[88],"(SVMs),":[89],"Gaussian":[90],"Processes":[91],"(GPs),":[92],"Reduced":[94],"Error":[95],"Pruning":[96],"(REP)":[97],"Tree.":[98],"The":[99,128],"accuracy":[100],"each":[102],"model":[103,134],"evaluated":[105],"statistical":[107],"metrics,":[109],"correlation":[112,143],"coefficient":[113],"(CC),":[114],"scatter":[115],"index":[116],"(SI),":[117],"mean":[118,124],"absolute":[119],"error":[120,126,155,185],"(MAE),":[121],"root":[123],"square":[125],"(RMSE).":[127],"results":[129],"demonstrate":[130],"that":[131],"ANN":[133,181],"outperformed":[135],"all":[136],"other":[137],"achieving":[140],"highest":[142],"(CC":[144],"=":[145,158,161,164],"0.9996":[146],"for":[147,150,212],"training,":[148],"0.9999":[149],"testing)":[151],"lowest":[154],"values":[156,174],"(MAE":[157],"0.0004,":[159],"RMSE":[160],"0.0006,":[162],"SI":[163],"0.00026).":[165],"comparative":[167],"analysis":[168,192],"between":[169],"actual":[170],"predicted":[172],"Gmb":[173],"confirmed":[175],"reliability":[177],"proposed":[180],"model,":[182],"with":[183],"minimal":[184],"margins":[186],"superior":[188],"accuracy.":[189],"Additionally,":[190],"sensitivity":[191],"identified":[193],"bitumen":[194,200],"content":[195],"(BC)":[196],"volume":[198],"(Vb)":[201],"most":[204],"influential":[205],"parameters":[206],"affecting":[207],"Gmb,":[208],"emphasizing":[209],"need":[211],"precise":[213],"parameter":[214],"optimization":[215],"design.":[219],"demonstrates":[222],"effectiveness":[224],"learning-driven":[227],"sustainable":[232],"design,":[235],"offering":[236],"cost-effective,":[238],"time-efficient,":[239],"highly":[241],"accurate":[242],"alternative":[243],"traditional":[245],"experimental":[246],"methods.":[247]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":2}],"updated_date":"2026-05-21T06:26:12.895304","created_date":"2025-10-10T00:00:00"}