{"id":"https://openalex.org/W7127982717","doi":"https://doi.org/10.36937/cebel.2026.11118","title":"An Interpretable Hybrid Machine Learning and Deep Learning Framework for Predicting Compressive Strength of Recycled Powder Mortar","display_name":"An Interpretable Hybrid Machine Learning and Deep Learning Framework for Predicting Compressive Strength of Recycled Powder Mortar","publication_year":2026,"publication_date":"2026-02-05","ids":{"openalex":"https://openalex.org/W7127982717","doi":"https://doi.org/10.36937/cebel.2026.11118"},"language":"en","primary_location":{"id":"doi:10.36937/cebel.2026.11118","doi":"https://doi.org/10.36937/cebel.2026.11118","is_oa":true,"landing_page_url":"https://doi.org/10.36937/cebel.2026.11118","pdf_url":"https://doi.org/10.36937/cebel.2026.11118","source":{"id":"https://openalex.org/S4210194535","display_name":"Civil Engineering Beyond Limits","issn_l":"2687-5756","issn":["2687-5756"],"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Civil Engineering Beyond Limits","raw_type":"journal-article"},"volume":null,"type":"article","is_traditional":true,"indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"bronze","oa_url":"https://doi.org/10.36937/cebel.2026.11118","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5020156942","display_name":"Elif A\u011fcakoca","orcid":"https://orcid.org/0000-0001-8228-0592","observed_orcids":["https://orcid.org/0000-0001-8228-0592"]},"institutions":[{"id":"https://openalex.org/I103703290","display_name":"Sakarya University","ror":"https://ror.org/04ttnw109","country_code":"TR","type":"education","lineage":["https://openalex.org/I103703290"]}],"countries":["TR"],"is_corresponding":true,"raw_author_name":"Elif A\u011fcakoca","raw_affiliation_strings":["Department of Civil Engineering , Sakarya University , Esentepe Campus , 6 Serdivan , Sakarya , 54050 , Turkey"],"raw_orcid":"https://orcid.org/0000-0001-8228-0592","affiliations":[{"raw_affiliation_string":"Department of Civil Engineering , Sakarya University , Esentepe Campus , 6 Serdivan , Sakarya , 54050 , Turkey","institution_ids":["https://openalex.org/I103703290"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5020156942"],"corresponding_institution_ids":["https://openalex.org/I103703290"],"apc_list":null,"apc_paid":null,"fwci":9.2843,"has_fulltext":true,"cited_by_count":4,"citation_normalized_percentile":{"value":0.97077571,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":98},"biblio":{"volume":"7","issue":"1","first_page":"1","last_page":"1"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11847","display_name":"Recycled Aggregate Concrete Performance","score":0.9063000082969666,"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":0.9063000082969666,"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/T10033","display_name":"Concrete and Cement Materials Research","score":0.028599999845027924,"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/T10687","display_name":"Innovative concrete reinforcement materials","score":0.025800000876188278,"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/compressive-strength-prediction","display_name":"compressive strength prediction","score":0.9980000257492065},{"id":"https://openalex.org/keywords/hybrid-machine-learning-and-deep-learning","display_name":"hybrid machine learning and deep learning","score":0.8859999775886536},{"id":"https://openalex.org/keywords/support-vector-regression","display_name":"support vector regression","score":0.8349999785423279},{"id":"https://openalex.org/keywords/particle-swarm-optimization","display_name":"particle swarm optimization","score":0.9639999866485596},{"id":"https://openalex.org/keywords/model-interpretability","display_name":"model interpretability","score":0.7039999961853027},{"id":"https://openalex.org/keywords/shap","display_name":"SHAP","score":0.6840000152587891},{"id":"https://openalex.org/keywords/partial-dependence-plots","display_name":"partial dependence plots","score":0.753000020980835},{"id":"https://openalex.org/keywords/accumulated-local-effects","display_name":"accumulated local effects","score":0.628000020980835},{"id":"https://openalex.org/keywords/particle-size","display_name":"particle size","score":0.7170000076293945},{"id":"https://openalex.org/keywords/water-to-binder-ratio","display_name":"water-to-binder ratio","score":0.9829999804496765},{"id":"https://openalex.org/keywords/cementitious-materials","display_name":"cementitious materials","score":0.6000000238418579}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6137999892234802},{"id":"https://openalex.org/C5274069","wikidata":"https://www.wikidata.org/wiki/Q2285707","display_name":"Categorical variable","level":2,"score":0.592199981212616},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5733000040054321},{"id":"https://openalex.org/C130767629","wikidata":"https://www.wikidata.org/wiki/Q7905205","display_name":"Mortar","level":2,"score":0.5378000140190125},{"id":"https://openalex.org/C30407753","wikidata":"https://www.wikidata.org/wiki/Q186191","display_name":"Compressive strength","level":2,"score":0.5353000164031982},{"id":"https://openalex.org/C2779666059","wikidata":"https://www.wikidata.org/wiki/Q45190","display_name":"Cementitious","level":3,"score":0.4832000136375427},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4715000092983246},{"id":"https://openalex.org/C112972136","wikidata":"https://www.wikidata.org/wiki/Q7595718","display_name":"Stability (learning theory)","level":2,"score":0.4708000123500824},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4618000090122223},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.42820000648498535},{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.3977000117301941},{"id":"https://openalex.org/C77835500","wikidata":"https://www.wikidata.org/wiki/Q694124","display_name":"Pozzolan","level":4,"score":0.38580000400543213},{"id":"https://openalex.org/C180016635","wikidata":"https://www.wikidata.org/wiki/Q2712821","display_name":"Compression (physics)","level":2,"score":0.3732999861240387},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.3725999891757965},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3467000126838684},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.34369999170303345},{"id":"https://openalex.org/C133199616","wikidata":"https://www.wikidata.org/wiki/Q25386885","display_name":"Empirical modelling","level":2,"score":0.3407999873161316},{"id":"https://openalex.org/C196083921","wikidata":"https://www.wikidata.org/wiki/Q7915758","display_name":"Variance (accounting)","level":2,"score":0.33719998598098755},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.32919999957084656},{"id":"https://openalex.org/C158622935","wikidata":"https://www.wikidata.org/wiki/Q660848","display_name":"Nonlinear system","level":2,"score":0.319599986076355},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.30219998955726624},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3010999858379364},{"id":"https://openalex.org/C48921125","wikidata":"https://www.wikidata.org/wiki/Q10861030","display_name":"Linear regression","level":2,"score":0.29679998755455017},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.29339998960494995},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.2928999960422516},{"id":"https://openalex.org/C120936955","wikidata":"https://www.wikidata.org/wiki/Q2155640","display_name":"Empirical research","level":2,"score":0.2840000092983246},{"id":"https://openalex.org/C34559072","wikidata":"https://www.wikidata.org/wiki/Q2334061","display_name":"Design of experiments","level":2,"score":0.2833000123500824},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.27140000462532043},{"id":"https://openalex.org/C122383733","wikidata":"https://www.wikidata.org/wiki/Q865920","display_name":"Approximation error","level":2,"score":0.26010000705718994}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.36937/cebel.2026.11118","doi":"https://doi.org/10.36937/cebel.2026.11118","is_oa":true,"landing_page_url":"https://doi.org/10.36937/cebel.2026.11118","pdf_url":"https://doi.org/10.36937/cebel.2026.11118","source":{"id":"https://openalex.org/S4210194535","display_name":"Civil Engineering Beyond Limits","issn_l":"2687-5756","issn":["2687-5756"],"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Civil Engineering Beyond Limits","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.36937/cebel.2026.11118","doi":"https://doi.org/10.36937/cebel.2026.11118","is_oa":true,"landing_page_url":"https://doi.org/10.36937/cebel.2026.11118","pdf_url":"https://doi.org/10.36937/cebel.2026.11118","source":{"id":"https://openalex.org/S4210194535","display_name":"Civil Engineering Beyond Limits","issn_l":"2687-5756","issn":["2687-5756"],"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Civil Engineering Beyond Limits","raw_type":"journal-article"},"sustainable_development_goals":[{"display_name":"Industry, innovation and infrastructure","score":0.5378999710083008,"id":"https://metadata.un.org/sdg/9"}],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7127982717.pdf","grobid_xml":"https://content.openalex.org/works/W7127982717.grobid-xml"},"referenced_works_count":38,"referenced_works":["https://openalex.org/W1998253841","https://openalex.org/W2038780961","https://openalex.org/W2063629125","https://openalex.org/W2765262761","https://openalex.org/W2996920233","https://openalex.org/W3080418585","https://openalex.org/W3157122772","https://openalex.org/W3196739012","https://openalex.org/W3198752860","https://openalex.org/W3216065183","https://openalex.org/W4200584684","https://openalex.org/W4206564180","https://openalex.org/W4289209300","https://openalex.org/W4297982544","https://openalex.org/W4307228493","https://openalex.org/W4307817841","https://openalex.org/W4313479953","https://openalex.org/W4313551293","https://openalex.org/W4313890312","https://openalex.org/W4317780800","https://openalex.org/W4319868680","https://openalex.org/W4382933461","https://openalex.org/W4383262780","https://openalex.org/W4384300769","https://openalex.org/W4386091631","https://openalex.org/W4390108560","https://openalex.org/W4391491933","https://openalex.org/W4393302874","https://openalex.org/W4404539279","https://openalex.org/W4404756614","https://openalex.org/W4404941329","https://openalex.org/W4405504116","https://openalex.org/W4407736309","https://openalex.org/W4413008343","https://openalex.org/W4413308348","https://openalex.org/W4413670006","https://openalex.org/W4413905670","https://openalex.org/W4414092319"],"related_works":["https://openalex.org/W7212051608","https://openalex.org/W4313890312","https://openalex.org/W4392946401","https://openalex.org/W4405233654","https://openalex.org/W4417466633","https://openalex.org/W7163763976","https://openalex.org/W7223185383","https://openalex.org/W7125613424","https://openalex.org/W4416746637","https://openalex.org/W4319052131"],"abstract_inverted_index":{"The":[0,140,227],"accurate":[1],"prediction":[2],"of":[3,29,47,92,102],"compressive":[4],"strength":[5],"(CS)":[6],"is":[7,162,174],"a":[8,191,230],"fundamental":[9],"requirement":[10],"for":[11,43,234],"structural":[12],"safety,":[13],"yet":[14,193],"conventional":[15],"empirical":[16],"models":[17,40],"remain":[18],"limited":[19],"in":[20,200,209],"their":[21],"ability":[22],"to":[23,97,135],"capture":[24],"the":[25,45,77,99,103,110,114,136,163],"complex":[26],"nonlinear":[27],"response":[28],"cementitious":[30],"materials.":[31],"Hybrid":[32],"machine":[33],"learning":[34,38],"(ML)":[35],"and":[36,64,88,90,123,132,148,155,181,186,203,211,218,224,241],"deep":[37],"(DL)":[39],"are":[41,69],"investigated":[42],"predicting":[44,235],"CS":[46,75],"recycled":[48],"powder":[49],"mortar":[50,243],"(RPM)":[51],"using":[52,84],"10-fold":[53,108],"cross-validation.":[54],"Material":[55],"type":[56],"(classified":[57],"as":[58,71,76],"kind),":[59],"particle":[60],"size,":[61],"water/binder":[62],"(W/B),":[63],"mass":[65],"replacement":[66],"ratio":[67],"(MRR)":[68],"used":[70],"input":[72,104],"variables,":[73],"with":[74,153,168,205],"target":[78],"response.":[79],"Model":[80],"performance":[81],"was":[82,95],"quantified":[83],"RMSE,":[85],"R\u00b2,":[86],"MAE,":[87],"MAPE,":[89],"analysis":[91],"variance":[93],"(ANOVA)":[94],"conducted":[96],"examine":[98],"statistical":[100],"significance":[101],"variables.":[105],"Based":[106],"on":[107,196,222],"cross-validation,":[109],"SVR\u2013PSO":[111],"model":[112],"exhibited":[113],"highest":[115],"average":[116],"predictive":[117],"performance,":[118],"attaining":[119],"R\u00b2":[120],"=":[121,125],"0.950":[122],"RMSE":[124],"2.042":[126],"MPa,":[127],"thereby":[128],"demonstrating":[129],"superior":[130],"accuracy":[131,147],"reliability":[133],"relative":[134],"other":[137],"evaluated":[138],"models.":[139],"interpretable":[141,232],"hybrid":[142],"ML\u2013DL":[143],"framework":[144,228],"achieved":[145],"high":[146],"robust":[149],"generalization":[150],"across":[151],"folds,":[152],"error":[154],"stability":[156],"analyses":[157,213],"confirming":[158],"unbiased":[159],"predictions.":[160],"MRR":[161],"dominant":[164],"factor":[165],"controlling":[166],"CS,":[167,197,236],"suggested":[169],"optimal":[170],"ranges;":[171],"this":[172],"influence":[173,221],"consistently":[175],"supported":[176],"by":[177],"ALE,":[178],"PDP,":[179],"SHAP,":[180],"ICE":[182,212],"analyses.":[183],"Feature":[184],"importance":[185],"PDP":[187],"demonstrate":[188],"Kind":[189],"has":[190],"secondary":[192],"meaningful":[194],"effect":[195],"reflecting":[198],"differences":[199],"composition,":[201],"morphology,":[202],"reactivity,":[204],"its":[206,216],"subtler":[207],"role":[208],"ALE":[210],"arising":[214],"from":[215],"categorical":[217],"indirect":[219],"mechanistic":[220],"hydration":[223],"pozzolanic":[225],"activity.":[226],"offers":[229],"robust,":[231],"tool":[233],"supporting":[237],"data-driven":[238],"mix":[239],"design":[240],"sustainable":[242],"optimization.":[244]},"counts_by_year":[{"year":2026,"cited_by_count":4}],"updated_date":"2026-11-10T11:39:43.021295","created_date":"2026-02-07T00:00:00"}
