{"id":"https://openalex.org/W7125914190","doi":"https://doi.org/10.1109/smc58881.2025.11343215","title":"AI-Powered Pavement Moduli Prediction: Advancing Smart City Infrastructure with Scalable and Interpretable Solutions <sup>*</sup>","display_name":"AI-Powered Pavement Moduli Prediction: Advancing Smart City Infrastructure with Scalable and Interpretable Solutions <sup>*</sup>","publication_year":2025,"publication_date":"2025-10-05","ids":{"openalex":"https://openalex.org/W7125914190","doi":"https://doi.org/10.1109/smc58881.2025.11343215"},"language":null,"primary_location":{"id":"doi:10.1109/smc58881.2025.11343215","is_oa":false,"landing_page_url":"https://doi.org/10.1109/smc58881.2025.11343215","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Conference on Systems, Man, and Cybernetics (SMC)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5124145766","display_name":"Xinyu Guo","orcid":null},"institutions":[{"id":"https://openalex.org/I885383172","display_name":"National University of Malaysia","ror":"https://ror.org/00bw8d226","country_code":"MY","type":"education","lineage":["https://openalex.org/I885383172"]}],"countries":["MY"],"is_corresponding":false,"raw_author_name":"Xinyu Guo","raw_affiliation_strings":["University Kembangan Malaysia,Bangi,Selangor,Malaysia,43600"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University Kembangan Malaysia,Bangi,Selangor,Malaysia,43600","institution_ids":["https://openalex.org/I885383172"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5068612679","display_name":"Nan Sun","orcid":"https://orcid.org/0000-0002-3266-1909"},"institutions":[{"id":"https://openalex.org/I188329596","display_name":"University of Canberra","ror":"https://ror.org/04s1nv328","country_code":"AU","type":"education","lineage":["https://openalex.org/I188329596"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Nan Sun","raw_affiliation_strings":["The University of New South Wales,School of Systems &#x0026; Computing,Canberra,ACT,Australia,2600"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The University of New South Wales,School of Systems &#x0026; Computing,Canberra,ACT,Australia,2600","institution_ids":["https://openalex.org/I188329596"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100454835","display_name":"Yue Chen","orcid":"https://orcid.org/0000-0003-3024-7015"},"institutions":[{"id":"https://openalex.org/I188329596","display_name":"University of Canberra","ror":"https://ror.org/04s1nv328","country_code":"AU","type":"education","lineage":["https://openalex.org/I188329596"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Yue Chen","raw_affiliation_strings":["The University of New South Wales,School of Engineering and Technology,Canberra,ACT,Australia,2600"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The University of New South Wales,School of Engineering and Technology,Canberra,ACT,Australia,2600","institution_ids":["https://openalex.org/I188329596"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5123092482","display_name":"Jianfeng Xue","orcid":null},"institutions":[{"id":"https://openalex.org/I188329596","display_name":"University of Canberra","ror":"https://ror.org/04s1nv328","country_code":"AU","type":"education","lineage":["https://openalex.org/I188329596"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Jianfeng Xue","raw_affiliation_strings":["The University of New South Wales,School of Engineering and Technology,Canberra,ACT,Australia,2600"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The University of New South Wales,School of Engineering and Technology,Canberra,ACT,Australia,2600","institution_ids":["https://openalex.org/I188329596"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"5380","last_page":"5386"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11606","display_name":"Infrastructure Maintenance and Monitoring","score":0.8862000107765198,"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/T11606","display_name":"Infrastructure Maintenance and Monitoring","score":0.8862000107765198,"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/T10264","display_name":"Asphalt Pavement Performance Evaluation","score":0.08410000056028366,"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/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.002300000051036477,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.5896000266075134},{"id":"https://openalex.org/keywords/smart-city","display_name":"Smart city","score":0.47769999504089355},{"id":"https://openalex.org/keywords/falling-weight-deflectometer","display_name":"Falling weight deflectometer","score":0.47540000081062317},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.46239998936653137},{"id":"https://openalex.org/keywords/transparency","display_name":"Transparency (behavior)","score":0.42480000853538513},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.38260000944137573},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.37779998779296875},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.3580000102519989}],"concepts":[{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.5896000266075134},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5720999836921692},{"id":"https://openalex.org/C2777103469","wikidata":"https://www.wikidata.org/wiki/Q1231558","display_name":"Smart city","level":3,"score":0.47769999504089355},{"id":"https://openalex.org/C2780656047","wikidata":"https://www.wikidata.org/wiki/Q5432395","display_name":"Falling weight deflectometer","level":3,"score":0.47540000081062317},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.46239998936653137},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.427700012922287},{"id":"https://openalex.org/C2780233690","wikidata":"https://www.wikidata.org/wiki/Q535347","display_name":"Transparency (behavior)","level":2,"score":0.42480000853538513},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.38260000944137573},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.37779998779296875},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.3580000102519989},{"id":"https://openalex.org/C75684735","wikidata":"https://www.wikidata.org/wiki/Q858810","display_name":"Big data","level":2,"score":0.3555000126361847},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.35109999775886536},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3425999879837036},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.33660000562667847},{"id":"https://openalex.org/C2780440489","wikidata":"https://www.wikidata.org/wiki/Q5227278","display_name":"Data-driven","level":2,"score":0.3199999928474426},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.3174000084400177},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.3116999864578247},{"id":"https://openalex.org/C2778827112","wikidata":"https://www.wikidata.org/wiki/Q22245680","display_name":"Feature engineering","level":3,"score":0.2937999963760376},{"id":"https://openalex.org/C2780598303","wikidata":"https://www.wikidata.org/wiki/Q65921492","display_name":"Flexibility (engineering)","level":2,"score":0.28459998965263367},{"id":"https://openalex.org/C2780996376","wikidata":"https://www.wikidata.org/wiki/Q1747460","display_name":"Pavement management","level":2,"score":0.28209999203681946},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.2745000123977661},{"id":"https://openalex.org/C82578977","wikidata":"https://www.wikidata.org/wiki/Q16773055","display_name":"Data aggregator","level":3,"score":0.27410000562667847},{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.2705000042915344},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2621000111103058},{"id":"https://openalex.org/C2776459999","wikidata":"https://www.wikidata.org/wiki/Q2119376","display_name":"Fidelity","level":2,"score":0.25760000944137573}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/smc58881.2025.11343215","is_oa":false,"landing_page_url":"https://doi.org/10.1109/smc58881.2025.11343215","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Conference on Systems, Man, and Cybernetics (SMC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Industry, innovation and infrastructure","score":0.6624244451522827,"id":"https://metadata.un.org/sdg/9"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":26,"referenced_works":["https://openalex.org/W2998674325","https://openalex.org/W3009524343","https://openalex.org/W3045754217","https://openalex.org/W3107241777","https://openalex.org/W3135035860","https://openalex.org/W3202176755","https://openalex.org/W3215711751","https://openalex.org/W4220909006","https://openalex.org/W4221041888","https://openalex.org/W4288096079","https://openalex.org/W4290422682","https://openalex.org/W4313251168","https://openalex.org/W4316372765","https://openalex.org/W4320352275","https://openalex.org/W4387268030","https://openalex.org/W4391898975","https://openalex.org/W4396859409","https://openalex.org/W4398179618","https://openalex.org/W4399156490","https://openalex.org/W4400228496","https://openalex.org/W4400311768","https://openalex.org/W4400663336","https://openalex.org/W4401504972","https://openalex.org/W4402376356","https://openalex.org/W4409649191","https://openalex.org/W4411111269"],"related_works":[],"abstract_inverted_index":{"With":[0],"the":[1,69,126,164],"growth":[2],"of":[3,24,59,129,166],"smart":[4,132,176],"cities,":[5],"data-driven":[6],"approaches":[7],"are":[8,56],"transforming":[9],"infrastructure":[10,133,177],"management":[11],"by":[12],"enabling":[13],"efficient":[14],"and":[15,35,45,83,115,120,155,173,182],"accurate":[16],"decision-making.":[17],"Pavement":[18,71],"condition":[19],"evaluation,":[20],"a":[21,28],"critical":[22],"aspect":[23],"transportation":[25],"infrastructure,":[26],"plays":[27],"vital":[29],"role":[30],"in":[31,131,175],"ensuring":[32],"mobility,":[33],"safety,":[34],"economic":[36],"productivity.":[37],"This":[38],"study":[39],"compares":[40],"traditional":[41],"machine":[42,76],"learning":[43,47,77],"(ML)":[44],"deep":[46],"(DL)":[48],"models":[49,168],"for":[50,96],"predicting":[51],"pavement":[52,152],"layer":[53],"moduli,":[54],"which":[55,123],"key":[57],"indicators":[58],"structural":[60],"health,":[61],"using":[62],"Falling":[63],"Weight":[64],"Deflectometer":[65],"(FWD)":[66],"data":[67,130],"from":[68],"Long-Term":[70],"Performance":[72],"(LTPP)":[73],"database.":[74],"Ensemble":[75],"models,":[78],"such":[79],"as":[80],"Random":[81],"Forest":[82],"XGBoost,":[84],"offer":[85],"high":[86],"predictive":[87,121],"accuracy":[88],"with":[89,98,109,125,151],"minimal":[90],"training":[91],"time,":[92],"making":[93],"them":[94],"suitable":[95],"scenarios":[97],"limited":[99],"data.":[100],"In":[101],"contrast,":[102],"advanced":[103],"DL":[104],"models\u2014particularly":[105],"hybrid":[106],"ResRNN":[107],"architectures":[108],"Wide":[110],"&":[111],"Deep":[112],"(W&D),":[113],"LSTM,":[114],"GRU":[116],"components\u2014demonstrate":[117],"superior":[118],"scalability":[119],"power,":[122],"aligns":[124],"growing":[127],"availability":[128],"systems.":[134,178],"To":[135,179],"enhance":[136],"interpretability,":[137],"SHapley":[138],"Additive":[139],"exPlanations":[140],"(SHAP)":[141],"analysis":[142],"is":[143],"employed,":[144],"uncovering":[145],"feature":[146],"importance":[147],"patterns":[148],"that":[149,169],"align":[150],"engineering":[153],"principles":[154],"informing":[156],"future":[157,183],"sensor":[158],"placement":[159],"strategies.":[160],"The":[161],"findings":[162],"support":[163],"selection":[165],"AI":[167],"balance":[170],"accuracy,":[171],"efficiency,":[172],"transparency":[174],"facilitate":[180],"reproducibility":[181],"research,":[184],"we":[185],"make":[186],"our":[187],"code":[188],"publicly":[189],"available":[190],"on":[191],"GitHub:":[192],"https://github.com/Gxy9527/LTPP.":[193]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2026-01-29T00:00:00"}