{"id":"https://openalex.org/W7128063876","doi":"https://doi.org/10.1061/jmcee7.mteng-22801","title":"Gaussian Noise\u2013Augmented Machine Learning Models for Predicting the Healing Performance of Self-Healing Concrete","display_name":"Gaussian Noise\u2013Augmented Machine Learning Models for Predicting the Healing Performance of Self-Healing Concrete","publication_year":2026,"publication_date":"2026-10-27","ids":{"openalex":"https://openalex.org/W7128063876","doi":"https://doi.org/10.1061/jmcee7.mteng-22801"},"language":"en","primary_location":{"id":"doi:10.1061/jmcee7.mteng-22801","doi":"https://doi.org/10.1061/jmcee7.mteng-22801","is_oa":false,"landing_page_url":"https://doi.org/10.1061/jmcee7.mteng-22801","pdf_url":null,"source":{"id":"https://openalex.org/S111427844","display_name":"Journal of Materials in Civil Engineering","issn_l":"0899-1561","issn":["0899-1561","1943-5533"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["anvur-scientific","cwts-core","dongbi-b","jufo-1","ki-jl-1","norway-1","poland-100","russia-white-list-1","vabb-shw"],"host_organization":"https://openalex.org/P4310315747","host_organization_name":"American Society of Civil Engineers","host_organization_lineage":["https://openalex.org/P4310315747"],"host_organization_lineage_names":["American Society of Civil Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Materials in Civil Engineering","raw_type":"journal-article"},"volume":null,"type":"article","indexed_in":["crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.5281/zenodo.18509858","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5125195009","display_name":"Omar Omar","orcid":"https://orcid.org/0000-0003-3246-1517","observed_orcids":["https://orcid.org/0000-0003-3246-1517"]},"institutions":[{"id":"https://openalex.org/I121820613","display_name":"Louisiana State University","ror":"https://ror.org/05ect4e57","country_code":"US","type":"education","lineage":["https://openalex.org/I121820613"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Omar Omar","raw_affiliation_strings":["Louisiana State Univ"],"raw_orcid":"https://orcid.org/0000-0003-3246-1517","affiliations":[{"raw_affiliation_string":"Louisiana State Univ","institution_ids":["https://openalex.org/I121820613"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5080724942","display_name":"Heena Dhasmana","orcid":"https://orcid.org/0000-0001-9940-3544","observed_orcids":["https://orcid.org/0000-0001-9940-3544"]},"institutions":[{"id":"https://openalex.org/I121820613","display_name":"Louisiana State University","ror":"https://ror.org/05ect4e57","country_code":"US","type":"education","lineage":["https://openalex.org/I121820613"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Heena Dhasmana","raw_affiliation_strings":["Louisiana State Univ"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Louisiana State Univ","institution_ids":["https://openalex.org/I121820613"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5015514474","display_name":"Hassan Noorvand","orcid":"https://orcid.org/0000-0002-4798-2051","observed_orcids":["https://orcid.org/0000-0002-4798-2051"]},"institutions":[{"id":"https://openalex.org/I121820613","display_name":"Louisiana State University","ror":"https://ror.org/05ect4e57","country_code":"US","type":"education","lineage":["https://openalex.org/I121820613"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Hassan Noorvand","raw_affiliation_strings":["Louisiana State Univ"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Louisiana State Univ","institution_ids":["https://openalex.org/I121820613"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5059729027","display_name":"Marwa Hassan","orcid":"https://orcid.org/0000-0001-8087-8232","observed_orcids":["https://orcid.org/0000-0001-8087-8232"]},"institutions":[{"id":"https://openalex.org/I121820613","display_name":"Louisiana State University","ror":"https://ror.org/05ect4e57","country_code":"US","type":"education","lineage":["https://openalex.org/I121820613"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Marwa Hassan","raw_affiliation_strings":["Louisiana State Univ"],"raw_orcid":"https://orcid.org/0000-0001-8087-8232","affiliations":[{"raw_affiliation_string":"Louisiana State Univ","institution_ids":["https://openalex.org/I121820613"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I121820613"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.05134279,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"39","issue":"2","first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12247","display_name":"Microbial Applications in Construction Materials","score":0.9071999788284302,"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"}},"topics":[{"id":"https://openalex.org/T12247","display_name":"Microbial Applications in Construction Materials","score":0.9071999788284302,"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"}},{"id":"https://openalex.org/T10687","display_name":"Innovative concrete reinforcement materials","score":0.03880000114440918,"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/T10810","display_name":"Polymer composites and self-healing","score":0.013399999588727951,"subfield":{"id":"https://openalex.org/subfields/2507","display_name":"Polymers and Plastics"},"field":{"id":"https://openalex.org/fields/25","display_name":"Materials Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/self-healing-concrete","display_name":"self-healing concrete","score":0.9990000128746033},{"id":"https://openalex.org/keywords/gaussian-noise-augmentation","display_name":"Gaussian noise augmentation","score":0.8999999761581421},{"id":"https://openalex.org/keywords/machine-learning-models","display_name":"machine learning models","score":0.7870000004768372},{"id":"https://openalex.org/keywords/data-augmentation","display_name":"data augmentation","score":0.5950000286102295},{"id":"https://openalex.org/keywords/concrete-durability","display_name":"concrete durability","score":0.41200000047683716}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5953999757766724},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.558899998664856},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5415999889373779},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.43059998750686646},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.3366999924182892},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.31209999322891235},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2847000062465668},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.2590999901294708},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.25040000677108765},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.24539999663829803}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1061/jmcee7.mteng-22801","doi":"https://doi.org/10.1061/jmcee7.mteng-22801","is_oa":false,"landing_page_url":"https://doi.org/10.1061/jmcee7.mteng-22801","pdf_url":null,"source":{"id":"https://openalex.org/S111427844","display_name":"Journal of Materials in Civil Engineering","issn_l":"0899-1561","issn":["0899-1561","1943-5533"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["anvur-scientific","cwts-core","dongbi-b","jufo-1","ki-jl-1","norway-1","poland-100","russia-white-list-1","vabb-shw"],"host_organization":"https://openalex.org/P4310315747","host_organization_name":"American Society of Civil Engineers","host_organization_lineage":["https://openalex.org/P4310315747"],"host_organization_lineage_names":["American Society of Civil Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Materials in Civil Engineering","raw_type":"journal-article"},{"id":"doi:10.5281/zenodo.18509858","doi":"https://doi.org/10.5281/zenodo.18509858","is_oa":true,"landing_page_url":"https://doi.org/10.5281/zenodo.18509858","pdf_url":null,"source":{"id":"https://openalex.org/S4306400562","display_name":"Zenodo (CERN European Organization for Nuclear Research)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I67311998","host_organization_name":"European Organization for Nuclear Research","host_organization_lineage":["https://openalex.org/I67311998"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Dataset"},{"id":"doi:10.5281/zenodo.18509859","doi":"https://doi.org/10.5281/zenodo.18509859","is_oa":true,"landing_page_url":"https://doi.org/10.5281/zenodo.18509859","pdf_url":null,"source":{"id":"https://openalex.org/S4306400562","display_name":"Zenodo (CERN European Organization for Nuclear Research)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I67311998","host_organization_name":"European Organization for Nuclear Research","host_organization_lineage":["https://openalex.org/I67311998"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Dataset"}],"best_oa_location":{"id":"doi:10.5281/zenodo.18509858","doi":"https://doi.org/10.5281/zenodo.18509858","is_oa":true,"landing_page_url":"https://doi.org/10.5281/zenodo.18509858","pdf_url":null,"source":{"id":"https://openalex.org/S4306400562","display_name":"Zenodo (CERN European Organization for Nuclear Research)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I67311998","host_organization_name":"European Organization for Nuclear Research","host_organization_lineage":["https://openalex.org/I67311998"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Dataset"},"sustainable_development_goals":[{"display_name":"Industry, innovation and infrastructure","score":0.4943999946117401,"id":"https://metadata.un.org/sdg/9"}],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W6949304276","https://openalex.org/W7167822414","https://openalex.org/W6893275951","https://openalex.org/W7163650325","https://openalex.org/W4399892141","https://openalex.org/W7131346086","https://openalex.org/W7213488601","https://openalex.org/W3167769443","https://openalex.org/W6891764578","https://openalex.org/W7157913635"],"abstract_inverted_index":{"Abstract":[0],"Self-healing":[1],"concrete":[2],"is":[3],"an":[4,122,127],"innovative":[5],"approach":[6],"to":[7,35,97],"extend":[8],"concrete\u2019s":[9],"service":[10],"life":[11],"by":[12,66],"autonomously":[13],"repairing":[14],"cracks,":[15],"thereby":[16],"reducing":[17],"the":[18,22,37,45,99,106,134,195],"environmental":[19,57],"impact":[20,46],"of":[21,40,47,101,109,200],"construction":[23],"industry.":[24],"Although":[25],"a":[26,117,220],"few":[27],"studies":[28],"have":[29],"employed":[30],"machine":[31],"learning":[32],"(ML)":[33],"models":[34,103,193],"predict":[36],"healing":[38,173,237],"efficiency":[39,108],"concrete,":[41],"they":[42],"often":[43],"overlooked":[44],"critical":[48],"variables":[49,168],"such":[50],"as":[51,243],"bacteria":[52],"type,":[53],"encapsulation":[54,171],"method,":[55],"and":[56,69,77,83,126,160,175,188,191,202,210,233,239,262],"conditions.":[58],"In":[59],"addition,":[60],"their":[61],"predictive":[62],"performance":[63],"was":[64,114],"limited":[65],"data":[67,95],"scarcity":[68],"weak":[70],"generalization,":[71],"resulting":[72],"in":[73,104,116,208,223,246,254],"lower":[74],"R":[75,197],"2":[76,198],"higher":[78],"root-mean":[79],"square":[80],"error":[81,86],"(RMSE),":[82],"mean":[84],"absolute":[85],"(MAE)":[87],"values.":[88],"This":[89],"study":[90],"applied":[91],"train-fold-only":[92],"Gaussian":[93],"noise":[94],"augmentation":[96,113,183],"improve":[98],"generalization":[100],"ML":[102,142],"predicting":[105],"self-healing":[107],"bacterial":[110,170],"concrete.":[111],"Data":[112],"embedded":[115],"nested":[118],"cross-validation":[119,132],"scheme,":[120],"using":[121],"outer":[123],"fivefold":[124],"StratifiedKFold":[125],"inner":[128],"threefold":[129],"grid":[130],"search":[131],"on":[133],"augmented":[135],"training":[136],"folds":[137],"for":[138],"hyperparameter":[139],"tuning.":[140],"Five":[141],"algorithms,":[143],"extreme":[144],"gradient":[145,148],"boosting":[146,149],"(XGB),":[147],"regression":[150,154],"(GBR),":[151],"support":[152],"vector":[153],"(SVR),":[155],"long":[156],"short-term":[157],"memory":[158],"(LSTM),":[159],"gated":[161],"recurrent":[162],"units":[163],"(GRUs),":[164],"were":[165],"evaluated.":[166],"Input":[167],"included":[169],"techniques,":[172],"conditions,":[174],"mix":[176],"design":[177],"parameters.":[178],"The":[179],"results":[180,218],"showed":[181],"that":[182],"significantly":[184],"enhanced":[185],"model":[186,271],"robustness":[187],"accuracy.":[189],"XGB":[190,249],"GBR":[192,258],"achieved":[194],"highest":[196],"values":[199,212],"0.92":[201],"0.91,":[203],"respectively,":[204],"with":[205,214],"substantial":[206],"reductions":[207],"RMSE":[209],"MAE":[211],"compared":[213],"nonaugmented":[215],"models.":[216,248],"These":[217,265],"indicate":[219],"notable":[221],"improvement":[222],"prediction":[224],"reliability":[225],"over":[226],"prior":[227],"studies.":[228],"Shapley":[229],"additive":[230],"explanations":[231],"(SHAP)":[232],"permutation":[234],"analyses":[235],"confirmed":[236],"time":[238],"initial":[240],"crack":[241],"width":[242],"dominant":[244],"features":[245],"both":[247],"captured":[250],"more":[251,260],"nonlinear":[252],"variation":[253],"secondary":[255],"features,":[256],"whereas":[257],"exhibited":[259],"consistent":[261],"balanced":[263],"attributions.":[264],"findings":[266],"provide":[267],"deeper":[268],"insights":[269],"into":[270],"behavior":[272],"beyond":[273],"conventional":[274],"accuracy":[275],"metrics.":[276]},"counts_by_year":[],"updated_date":"2026-10-29T09:56:32.521200","created_date":"2026-02-07T00:00:00"}