{"id":"https://openalex.org/W7225459096","doi":"https://doi.org/10.1061/ajrua6.rueng-2014","title":"A Hybrid Bayesian Compressive Sampling and Radial Basis Function Algorithm for 2D Spatial Reconstruction of Concrete Strength Using Sparse Measurements","display_name":"A Hybrid Bayesian Compressive Sampling and Radial Basis Function Algorithm for 2D Spatial Reconstruction of Concrete Strength Using Sparse Measurements","publication_year":2026,"publication_date":"2026-11-07","ids":{"openalex":"https://openalex.org/W7225459096","doi":"https://doi.org/10.1061/ajrua6.rueng-2014"},"language":"en","primary_location":{"id":"doi:10.1061/ajrua6.rueng-2014","doi":"https://doi.org/10.1061/ajrua6.rueng-2014","is_oa":false,"landing_page_url":"https://doi.org/10.1061/ajrua6.rueng-2014","pdf_url":null,"source":{"id":"https://openalex.org/S2764572437","display_name":"ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems Part A Civil Engineering","issn_l":"2376-7642","issn":["2376-7642"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core","dongbi-c","jufo-1","norway-1","poland-20","russia-white-list-1"],"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":"ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering","raw_type":"journal-article"},"volume":null,"type":"article","is_traditional":true,"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/A5022899591","display_name":"Abdalla Elhadi Alhashmi","orcid":"https://orcid.org/0000-0001-6611-9416","observed_orcids":["https://orcid.org/0000-0001-6611-9416"]},"institutions":[{"id":"https://openalex.org/I129902397","display_name":"Dalhousie University","ror":"https://ror.org/01e6qks80","country_code":"CA","type":"education","lineage":["https://openalex.org/I129902397"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Abdalla Elhadi Alhashmi","raw_affiliation_strings":["Dalhousie Univ"],"raw_orcid":"https://orcid.org/0000-0001-6611-9416","affiliations":[{"raw_affiliation_string":"Dalhousie Univ","institution_ids":["https://openalex.org/I129902397"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5015086481","display_name":"Fadi Oudah","orcid":"https://orcid.org/0000-0002-1827-286X","observed_orcids":["https://orcid.org/0000-0002-1827-286X"]},"institutions":[{"id":"https://openalex.org/I129902397","display_name":"Dalhousie University","ror":"https://ror.org/01e6qks80","country_code":"CA","type":"education","lineage":["https://openalex.org/I129902397"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Fadi Oudah","raw_affiliation_strings":["Dalhousie Univ"],"raw_orcid":"https://orcid.org/0000-0002-1827-286X","affiliations":[{"raw_affiliation_string":"Dalhousie Univ","institution_ids":["https://openalex.org/I129902397"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I129902397"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.0,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"13","issue":"1","first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11046","display_name":"Geotechnical Engineering and Analysis","score":0.3792000114917755,"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/T11046","display_name":"Geotechnical Engineering and Analysis","score":0.3792000114917755,"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/T10479","display_name":"Structural Behavior of Reinforced Concrete","score":0.1559000015258789,"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/T10928","display_name":"Probabilistic and Robust Engineering Design","score":0.09989999979734421,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"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/radial-basis-functions","display_name":"radial basis functions","score":0.9049999713897705},{"id":"https://openalex.org/keywords/concrete-compressive-strength","display_name":"concrete compressive strength","score":0.9739999771118164},{"id":"https://openalex.org/keywords/random-field-simulation","display_name":"random field simulation","score":0.7300000190734863},{"id":"https://openalex.org/keywords/prediction-accuracy","display_name":"prediction accuracy","score":0.7319999933242798},{"id":"https://openalex.org/keywords/design-support-tools","display_name":"design support tools","score":0.7279999852180481},{"id":"https://openalex.org/keywords/in-situ-testing","display_name":"in situ testing","score":0.5519999861717224}],"concepts":[{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.6642000079154968},{"id":"https://openalex.org/C124851039","wikidata":"https://www.wikidata.org/wiki/Q2665459","display_name":"Compressed sensing","level":2,"score":0.6636999845504761},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.6100000143051147},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.567300021648407},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.4790000021457672},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4781999886035919},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.46630001068115234},{"id":"https://openalex.org/C197055811","wikidata":"https://www.wikidata.org/wiki/Q207522","display_name":"Probability density function","level":2,"score":0.41990000009536743},{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.41290000081062317},{"id":"https://openalex.org/C30407753","wikidata":"https://www.wikidata.org/wiki/Q186191","display_name":"Compressive strength","level":2,"score":0.4106000065803528},{"id":"https://openalex.org/C12426560","wikidata":"https://www.wikidata.org/wiki/Q189569","display_name":"Basis (linear algebra)","level":2,"score":0.4034999907016754},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.40149998664855957},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.3756999969482422},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.37220001220703125},{"id":"https://openalex.org/C52740198","wikidata":"https://www.wikidata.org/wiki/Q1539564","display_name":"Importance sampling","level":3,"score":0.36570000648498535},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.3603000044822693},{"id":"https://openalex.org/C130402806","wikidata":"https://www.wikidata.org/wiki/Q5361768","display_name":"Random field","level":2,"score":0.3571000099182129},{"id":"https://openalex.org/C774472","wikidata":"https://www.wikidata.org/wiki/Q6760393","display_name":"Margin (machine learning)","level":2,"score":0.3440999984741211},{"id":"https://openalex.org/C112313634","wikidata":"https://www.wikidata.org/wiki/Q7886648","display_name":"Complement (music)","level":5,"score":0.33730000257492065},{"id":"https://openalex.org/C149441793","wikidata":"https://www.wikidata.org/wiki/Q200726","display_name":"Probability distribution","level":2,"score":0.3319000005722046},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.32670000195503235},{"id":"https://openalex.org/C2779898584","wikidata":"https://www.wikidata.org/wiki/Q7820109","display_name":"Reconstruction algorithm","level":3,"score":0.3012999892234802},{"id":"https://openalex.org/C110121322","wikidata":"https://www.wikidata.org/wiki/Q865811","display_name":"Distribution (mathematics)","level":2,"score":0.295199990272522},{"id":"https://openalex.org/C180016635","wikidata":"https://www.wikidata.org/wiki/Q2712821","display_name":"Compression (physics)","level":2,"score":0.2939999997615814},{"id":"https://openalex.org/C207201462","wikidata":"https://www.wikidata.org/wiki/Q182505","display_name":"Bayes' theorem","level":3,"score":0.2856999933719635},{"id":"https://openalex.org/C170593435","wikidata":"https://www.wikidata.org/wiki/Q4128565","display_name":"Slice sampling","level":4,"score":0.27090001106262207},{"id":"https://openalex.org/C5917680","wikidata":"https://www.wikidata.org/wiki/Q2621825","display_name":"Basis function","level":2,"score":0.2599000036716461},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.25619998574256897},{"id":"https://openalex.org/C129848803","wikidata":"https://www.wikidata.org/wiki/Q2564360","display_name":"Sample size determination","level":2,"score":0.25119999051094055},{"id":"https://openalex.org/C122123141","wikidata":"https://www.wikidata.org/wiki/Q176623","display_name":"Random variable","level":2,"score":0.25040000677108765}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1061/ajrua6.rueng-2014","doi":"https://doi.org/10.1061/ajrua6.rueng-2014","is_oa":false,"landing_page_url":"https://doi.org/10.1061/ajrua6.rueng-2014","pdf_url":null,"source":{"id":"https://openalex.org/S2764572437","display_name":"ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems Part A Civil Engineering","issn_l":"2376-7642","issn":["2376-7642"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core","dongbi-c","jufo-1","norway-1","poland-20","russia-white-list-1"],"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":"ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":50,"referenced_works":["https://openalex.org/W1940717634","https://openalex.org/W1965597870","https://openalex.org/W1985633214","https://openalex.org/W2003170952","https://openalex.org/W2006454020","https://openalex.org/W2010383863","https://openalex.org/W2010577242","https://openalex.org/W2018345902","https://openalex.org/W2061171222","https://openalex.org/W2075967518","https://openalex.org/W2086379970","https://openalex.org/W2107699930","https://openalex.org/W2109192554","https://openalex.org/W2127271355","https://openalex.org/W2129131372","https://openalex.org/W2158410246","https://openalex.org/W2410098808","https://openalex.org/W2563965658","https://openalex.org/W2765166320","https://openalex.org/W2766398999","https://openalex.org/W2789615360","https://openalex.org/W2801562103","https://openalex.org/W2889097262","https://openalex.org/W2893736293","https://openalex.org/W2923135078","https://openalex.org/W3091437915","https://openalex.org/W3116013939","https://openalex.org/W3183930605","https://openalex.org/W3198435088","https://openalex.org/W3200164467","https://openalex.org/W3201755858","https://openalex.org/W4210968171","https://openalex.org/W4220932069","https://openalex.org/W4231857496","https://openalex.org/W4311633842","https://openalex.org/W4313361633","https://openalex.org/W4313420413","https://openalex.org/W4319729152","https://openalex.org/W4365445573","https://openalex.org/W4378417909","https://openalex.org/W4378839706","https://openalex.org/W4385679637","https://openalex.org/W4385773067","https://openalex.org/W4386028349","https://openalex.org/W4386232761","https://openalex.org/W4388922599","https://openalex.org/W4390169443","https://openalex.org/W4401044053","https://openalex.org/W4401099410","https://openalex.org/W4411865020"],"related_works":["https://openalex.org/W7172386636","https://openalex.org/W7214764730","https://openalex.org/W4415377266","https://openalex.org/W4365445573","https://openalex.org/W4308545224","https://openalex.org/W637867142","https://openalex.org/W2061638982","https://openalex.org/W2796372834","https://openalex.org/W7163745829","https://openalex.org/W2211939169"],"abstract_inverted_index":{"Abstract":[0],"Obtaining":[1],"the":[2,20,31,48,74,80,92,98,118,122,161,166,183,188,193,200,205,211],"spatial":[3,50,168],"distribution":[4,51,169],"of":[5,22,27,52,82,128,153,170,178,195,210],"concrete":[6,10,53,132,155,171],"properties":[7,54],"for":[8,33,46],"large-scale":[9],"structures":[11],"through":[12,106],"in":[13,199],"situ":[14],"tests":[15],"is":[16],"challenging":[17],"due":[18],"to":[19,78,116],"difficulty":[21],"collecting":[23],"an":[24,42,175],"adequate":[25],"number":[26],"representative":[28],"samples":[29],"and":[30,131,143,207],"need":[32],"strategically":[34],"selecting":[35],"optimal":[36],"sampling":[37,71,87,108,129,185],"locations.":[38],"This":[39],"paper":[40],"presents":[41],"advanced":[43],"computational":[44],"algorithm":[45,119,142],"reconstructing":[47],"2D":[49,167],"using":[55,135],"sparse":[56],"field":[57,94,138],"measurements.":[58],"The":[59,140,157],"developed":[60,115,141],"algorithm,":[61],"named":[62],"\u201cadvanced":[63],"Bayesian":[64,69],"compressive":[65,70,172],"sampling\u201d":[66],"(ABCS),":[67],"integrates":[68],"(BCS)":[72],"with":[73,174],"radial":[75],"basis":[76],"function":[77,127],"overcome":[79],"limitation":[81],"BCS":[83],"under":[84],"systematic":[85],"grid-based":[86],"method.":[88,213],"ABCS":[89],"ensures":[90],"that":[91,160],"reconstructed":[93],"not":[95],"only":[96],"captures":[97],"underlying":[99],"stochastic":[100],"variability":[101,134],"but":[102],"also":[103],"passes":[104],"precisely":[105],"measured":[107],"points.":[109,186],"A":[110],"generalized":[111],"design":[112,144,201],"aid":[113,145],"was":[114,197],"complement":[117],"by":[120],"quantifying":[121],"prediction":[123],"accuracy":[124],"as":[125],"a":[126],"density":[130],"material":[133],"extensive":[136],"random":[137],"simulations.":[139],"were":[146,191],"validated":[147],"on":[148],"two":[149],"real-life":[150],"case":[151],"studies":[152],"degraded":[154],"structures.":[156],"results":[158],"demonstrated":[159],"method":[162],"can":[163],"accurately":[164],"reconstruct":[165],"strength":[173],"acceptable":[176],"margin":[177],"error":[179],"while":[180],"significantly":[181],"reducing":[182],"required":[184],"Moreover,":[187],"calculated":[189],"errors":[190],"within":[192],"range":[194],"what":[196],"presented":[198],"aid,":[202],"confirming":[203],"both":[204],"practical":[206],"analytical":[208],"reliability":[209],"proposed":[212]},"counts_by_year":[],"updated_date":"2026-11-09T13:22:03.289127","created_date":"2026-11-08T00:00:00"}
