{"id":"https://openalex.org/W4414604956","doi":"https://doi.org/10.1109/qpain66474.2025.11171958","title":"Optimization of Data-Driven Ensemble Models Using Firefly Algorithm for Enhanced 3D Printed Concrete Strength Prediction","display_name":"Optimization of Data-Driven Ensemble Models Using Firefly Algorithm for Enhanced 3D Printed Concrete Strength Prediction","publication_year":2025,"publication_date":"2025-07-31","ids":{"openalex":"https://openalex.org/W4414604956","doi":"https://doi.org/10.1109/qpain66474.2025.11171958"},"language":"en","primary_location":{"id":"doi:10.1109/qpain66474.2025.11171958","is_oa":false,"landing_page_url":"https://doi.org/10.1109/qpain66474.2025.11171958","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Conference on Quantum Photonics, Artificial Intelligence, and Networking (QPAIN)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5118849015","display_name":"Usmi Akter","orcid":null},"institutions":[{"id":"https://openalex.org/I1307585291","display_name":"Khulna University of Engineering and Technology","ror":"https://ror.org/04y58d606","country_code":"BD","type":"education","lineage":["https://openalex.org/I1307585291"]}],"countries":["BD"],"is_corresponding":true,"raw_author_name":"Usmi Akter","raw_affiliation_strings":["Khulna University of Engineering &#x0026; Technology,Dept. of Civil Engineering,Khulna,Bangladesh"],"affiliations":[{"raw_affiliation_string":"Khulna University of Engineering &#x0026; Technology,Dept. of Civil Engineering,Khulna,Bangladesh","institution_ids":["https://openalex.org/I1307585291"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5119761612","display_name":"Syed Effti Rezvi","orcid":null},"institutions":[{"id":"https://openalex.org/I92156820","display_name":"University of Cincinnati Medical Center","ror":"https://ror.org/02p72h367","country_code":"US","type":"healthcare","lineage":["https://openalex.org/I92156820"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Syed Effti Rezvi","raw_affiliation_strings":["College of Engineering and Applied Science, University of Cincinnati,OH,United States,45221"],"affiliations":[{"raw_affiliation_string":"College of Engineering and Applied Science, University of Cincinnati,OH,United States,45221","institution_ids":["https://openalex.org/I92156820"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5118849014","display_name":"Md. Jobayer Parvez Ratul","orcid":null},"institutions":[{"id":"https://openalex.org/I1307585291","display_name":"Khulna University of Engineering and Technology","ror":"https://ror.org/04y58d606","country_code":"BD","type":"education","lineage":["https://openalex.org/I1307585291"]}],"countries":["BD"],"is_corresponding":false,"raw_author_name":"Md. Jobayer Parvez Ratul","raw_affiliation_strings":["Khulna University of Engineering &#x0026; Technology,Dept. of Civil Engineering,Khulna,Bangladesh"],"affiliations":[{"raw_affiliation_string":"Khulna University of Engineering &#x0026; Technology,Dept. of Civil Engineering,Khulna,Bangladesh","institution_ids":["https://openalex.org/I1307585291"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5040995805","display_name":"S. Nanda Kishor","orcid":null},"institutions":[{"id":"https://openalex.org/I96426537","display_name":"Bangladesh University of Professionals","ror":"https://ror.org/01zphyp78","country_code":"BD","type":"education","lineage":["https://openalex.org/I96426537"]}],"countries":["BD"],"is_corresponding":false,"raw_author_name":"Saad Kaiyum Kishor","raw_affiliation_strings":["Bangladesh University of Professionals,Faculty of Business Studies,Dhaka,Bangladesh"],"affiliations":[{"raw_affiliation_string":"Bangladesh University of Professionals,Faculty of Business Studies,Dhaka,Bangladesh","institution_ids":["https://openalex.org/I96426537"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5118849015"],"corresponding_institution_ids":["https://openalex.org/I1307585291"],"apc_list":null,"apc_paid":null,"fwci":1.1279,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.821013,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12190","display_name":"Innovations in Concrete and Construction Materials","score":0.9902999997138977,"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/T12190","display_name":"Innovations in Concrete and Construction Materials","score":0.9902999997138977,"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/T10783","display_name":"Additive Manufacturing and 3D Printing Technologies","score":0.9865000247955322,"subfield":{"id":"https://openalex.org/subfields/2203","display_name":"Automotive 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/T12482","display_name":"Tunneling and Rock Mechanics","score":0.9840999841690063,"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/firefly-algorithm","display_name":"Firefly algorithm","score":0.6276000142097473},{"id":"https://openalex.org/keywords/ensemble-learning","display_name":"Ensemble learning","score":0.6204000115394592},{"id":"https://openalex.org/keywords/mean-squared-error","display_name":"Mean squared error","score":0.6061999797821045},{"id":"https://openalex.org/keywords/boosting","display_name":"Boosting (machine learning)","score":0.5794000029563904},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.5715000033378601},{"id":"https://openalex.org/keywords/metaheuristic","display_name":"Metaheuristic","score":0.429500013589859},{"id":"https://openalex.org/keywords/gradient-boosting","display_name":"Gradient boosting","score":0.4187000095844269},{"id":"https://openalex.org/keywords/ensemble-forecasting","display_name":"Ensemble forecasting","score":0.40549999475479126},{"id":"https://openalex.org/keywords/multi-objective-optimization","display_name":"Multi-objective optimization","score":0.36980000138282776}],"concepts":[{"id":"https://openalex.org/C154982244","wikidata":"https://www.wikidata.org/wiki/Q5451844","display_name":"Firefly algorithm","level":3,"score":0.6276000142097473},{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.6204000115394592},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.6061999797821045},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.5794000029563904},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.5715000033378601},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5608999729156494},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.49059998989105225},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.4343999922275543},{"id":"https://openalex.org/C109718341","wikidata":"https://www.wikidata.org/wiki/Q1385229","display_name":"Metaheuristic","level":2,"score":0.429500013589859},{"id":"https://openalex.org/C70153297","wikidata":"https://www.wikidata.org/wiki/Q5591907","display_name":"Gradient boosting","level":3,"score":0.4187000095844269},{"id":"https://openalex.org/C119898033","wikidata":"https://www.wikidata.org/wiki/Q3433888","display_name":"Ensemble forecasting","level":2,"score":0.40549999475479126},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.40209999680519104},{"id":"https://openalex.org/C68781425","wikidata":"https://www.wikidata.org/wiki/Q2052203","display_name":"Multi-objective optimization","level":2,"score":0.36980000138282776},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3395000100135803},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.322299987077713},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.32109999656677246},{"id":"https://openalex.org/C84525736","wikidata":"https://www.wikidata.org/wiki/Q831366","display_name":"Decision tree","level":2,"score":0.3199000060558319},{"id":"https://openalex.org/C113174947","wikidata":"https://www.wikidata.org/wiki/Q2859736","display_name":"Tree (set theory)","level":2,"score":0.3176000118255615},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.30809998512268066},{"id":"https://openalex.org/C71907059","wikidata":"https://www.wikidata.org/wiki/Q223323","display_name":"Root mean square","level":2,"score":0.3041999936103821},{"id":"https://openalex.org/C137836250","wikidata":"https://www.wikidata.org/wiki/Q984063","display_name":"Optimization problem","level":2,"score":0.30309998989105225},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.29269999265670776},{"id":"https://openalex.org/C122383733","wikidata":"https://www.wikidata.org/wiki/Q865920","display_name":"Approximation error","level":2,"score":0.2890999913215637},{"id":"https://openalex.org/C3019308078","wikidata":"https://www.wikidata.org/wiki/Q229367","display_name":"3d printed","level":2,"score":0.28760001063346863},{"id":"https://openalex.org/C30407753","wikidata":"https://www.wikidata.org/wiki/Q186191","display_name":"Compressive strength","level":2,"score":0.2874999940395355},{"id":"https://openalex.org/C2780092901","wikidata":"https://www.wikidata.org/wiki/Q3433612","display_name":"Correlation coefficient","level":2,"score":0.28189998865127563},{"id":"https://openalex.org/C188154048","wikidata":"https://www.wikidata.org/wiki/Q6803609","display_name":"Mean absolute error","level":3,"score":0.2685999870300293},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.26660001277923584},{"id":"https://openalex.org/C167085575","wikidata":"https://www.wikidata.org/wiki/Q6803654","display_name":"Mean squared prediction error","level":2,"score":0.262800008058548},{"id":"https://openalex.org/C2987595161","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Optimization algorithm","level":2,"score":0.25760000944137573},{"id":"https://openalex.org/C137800194","wikidata":"https://www.wikidata.org/wiki/Q11713455","display_name":"Interpolation (computer graphics)","level":3,"score":0.2540999948978424},{"id":"https://openalex.org/C150217764","wikidata":"https://www.wikidata.org/wiki/Q6803607","display_name":"Mean absolute percentage error","level":3,"score":0.2533000111579895},{"id":"https://openalex.org/C128990827","wikidata":"https://www.wikidata.org/wiki/Q192830","display_name":"Coefficient of determination","level":2,"score":0.25209999084472656}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/qpain66474.2025.11171958","is_oa":false,"landing_page_url":"https://doi.org/10.1109/qpain66474.2025.11171958","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Conference on Quantum Photonics, Artificial Intelligence, and Networking (QPAIN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":15,"referenced_works":["https://openalex.org/W2086305332","https://openalex.org/W2741421471","https://openalex.org/W2808508889","https://openalex.org/W2889292355","https://openalex.org/W3048743805","https://openalex.org/W3111944458","https://openalex.org/W3131948835","https://openalex.org/W3173690989","https://openalex.org/W3184697131","https://openalex.org/W4284680168","https://openalex.org/W4379142103","https://openalex.org/W4400170509","https://openalex.org/W4403977659","https://openalex.org/W4404783803","https://openalex.org/W4406037859"],"related_works":[],"abstract_inverted_index":{"Three-dimensional":[0],"concrete":[1,18],"printing":[2],"(3DCP)":[3],"has":[4],"emerged":[5],"as":[6,160],"a":[7,21,201],"transformative":[8],"construction":[9],"technology.":[10],"However,":[11],"optimizing":[12],"mix":[13,194],"design":[14],"for":[15,140,192,205],"3D-printed":[16],"fiber-reinforced":[17],"(3DP-FRC)":[19],"remains":[20],"multifactorial":[22],"challenge":[23],"due":[24],"to":[25,129,187],"the":[26,65,107,123,141,169,172,178],"interplay":[27],"of":[28,69,76,96,134,171,180],"time-dependent":[29],"rheology,":[30],"printability":[31],"constraints,":[32],"and":[33,55,94,117,131,137,157],"heterogeneous":[34],"material":[35],"compositions.":[36],"This":[37],"study":[38],"develops":[39],"six":[40],"ensemble":[41,185],"machine":[42],"learning":[43,186],"(ML)":[44],"models-Decision":[45],"Tree":[46],"(DT),":[47],"Random":[48],"Forest":[49],"(RF),":[50],"Extreme":[51],"Gradient":[52],"Boosting":[53],"(XGB),":[54],"their":[56],"Firefly":[57],"Algorithm":[58],"(FA)-optimized":[59],"counterparts":[60],"(FADT,":[61],"FA-RF,":[62],"FA-XGB)-to":[63],"predict":[64],"compressive":[66],"strength":[67],"(CS)":[68],"3DP-FRC.":[70],"A":[71],"dataset":[72],"comprising":[73],"17":[74],"features":[75],"299":[77],"experimental":[78],"samples":[79],"was":[80],"used.":[81],"Evaluation":[82],"metrics":[83],"including":[84],"Root":[85],"Mean":[86,90],"Square":[87],"Error":[88,92],"(RMSE),":[89],"Absolute":[91],"(MAE),":[93],"Coefficient":[95],"Determination":[97],"(<tex":[98],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[99,113,120],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">$\\mathbf{R}^{\\mathbf{2}}$</tex>)":[100],"were":[101],"employed.":[102],"The":[103,175,196],"FA-XGB":[104,173,198],"model":[105],"achieved":[106],"highest":[108],"predictive":[109],"accuracy":[110],"with":[111,184],"<tex":[112,119],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">$\\mathrm{R}^{2}=98.87":[114],"\\%,":[115],"\\text{RMSE}=4.7$</tex>,":[116],"MAE":[118,132],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">$=3.31$</tex>":[121],"on":[122],"test":[124],"set.":[125],"FA":[126],"optimization":[127,183],"led":[128],"RMSE":[130],"reductions":[133],"19.44":[135],"%":[136,139],"27.83":[138],"XGB":[142],"model,":[143],"respectively.":[144],"SHapley":[145],"Additive":[146],"explanations":[147],"(SHAP)":[148],"analysis":[149],"identified":[150],"water-to-binder":[151],"ratio,":[152],"OPC,":[153],"superplasticizer,":[154],"silica":[155],"fume,":[156],"load":[158],"direction":[159],"key":[161],"influential":[162],"features.":[163],"Modified":[164],"Taylor":[165],"diagrams":[166],"further":[167],"validated":[168],"superiority":[170],"model.":[174],"findings":[176],"underscore":[177],"potential":[179],"integrating":[181],"metaheuristic":[182],"produce":[188],"robust,":[189],"scalable":[190],"models":[191],"3DP-FRC":[193],"design.":[195],"hybrid":[197],"framework":[199],"offers":[200],"reliable,":[202],"data-driven":[203],"approach":[204],"performance-oriented":[206],"3DCP":[207],"applications.":[208]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2026-03-07T16:01:11.037858","created_date":"2025-10-10T00:00:00"}