{"id":"https://openalex.org/W2783548009","doi":"https://doi.org/10.1109/bigdata.2017.8258381","title":"Track geometry big data analysis: A machine learning approach","display_name":"Track geometry big data analysis: A machine learning approach","publication_year":2017,"publication_date":"2017-12-01","ids":{"openalex":"https://openalex.org/W2783548009","doi":"https://doi.org/10.1109/bigdata.2017.8258381","mag":"2783548009"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata.2017.8258381","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata.2017.8258381","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE International Conference on Big Data (Big Data)","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/A5008928182","display_name":"Emmanuel Nii Martey","orcid":"https://orcid.org/0000-0001-6653-4329"},"institutions":[{"id":"https://openalex.org/I86501945","display_name":"University of Delaware","ror":"https://ror.org/01sbq1a82","country_code":"US","type":"education","lineage":["https://openalex.org/I86501945"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Emmanuel Nii Martey","raw_affiliation_strings":["Department of Civil and Environmental Engineering, University of Delaware, Newark, DE, USA"],"affiliations":[{"raw_affiliation_string":"Department of Civil and Environmental Engineering, University of Delaware, Newark, DE, USA","institution_ids":["https://openalex.org/I86501945"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5029624021","display_name":"Ahmed Lasisi","orcid":"https://orcid.org/0000-0001-6890-1591"},"institutions":[{"id":"https://openalex.org/I86501945","display_name":"University of Delaware","ror":"https://ror.org/01sbq1a82","country_code":"US","type":"education","lineage":["https://openalex.org/I86501945"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Lasisi Ahmed","raw_affiliation_strings":["Department of Civil and Environmental Engineering, University of Delaware, Newark, DE, USA"],"affiliations":[{"raw_affiliation_string":"Department of Civil and Environmental Engineering, University of Delaware, Newark, DE, USA","institution_ids":["https://openalex.org/I86501945"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5035496450","display_name":"Nii Attoh-Okine","orcid":"https://orcid.org/0000-0001-5328-5538"},"institutions":[{"id":"https://openalex.org/I86501945","display_name":"University of Delaware","ror":"https://ror.org/01sbq1a82","country_code":"US","type":"education","lineage":["https://openalex.org/I86501945"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Nii Attoh-Okine","raw_affiliation_strings":["Department of Civil and Environmental Engineering, University of Delaware, Newark, DE, USA"],"affiliations":[{"raw_affiliation_string":"Department of Civil and Environmental Engineering, University of Delaware, Newark, DE, USA","institution_ids":["https://openalex.org/I86501945"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5008928182"],"corresponding_institution_ids":["https://openalex.org/I86501945"],"apc_list":null,"apc_paid":null,"fwci":2.7683,"has_fulltext":false,"cited_by_count":31,"citation_normalized_percentile":{"value":0.89963847,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"3800","last_page":"3809"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10842","display_name":"Railway Engineering and Dynamics","score":0.9945999979972839,"subfield":{"id":"https://openalex.org/subfields/2210","display_name":"Mechanical 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/T10842","display_name":"Railway Engineering and Dynamics","score":0.9945999979972839,"subfield":{"id":"https://openalex.org/subfields/2210","display_name":"Mechanical 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/T14446","display_name":"Civil and Geotechnical Engineering Research","score":0.9688000082969666,"subfield":{"id":"https://openalex.org/subfields/2200","display_name":"General 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/T11046","display_name":"Geotechnical Engineering and Analysis","score":0.9483000040054321,"subfield":{"id":"https://openalex.org/subfields/2213","display_name":"Safety, Risk, Reliability and Quality"},"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/subgrade","display_name":"Subgrade","score":0.726544201374054},{"id":"https://openalex.org/keywords/track-geometry","display_name":"Track geometry","score":0.7122495770454407},{"id":"https://openalex.org/keywords/track","display_name":"Track (disk drive)","score":0.5656624436378479},{"id":"https://openalex.org/keywords/regression-analysis","display_name":"Regression analysis","score":0.48587292432785034},{"id":"https://openalex.org/keywords/ballast","display_name":"Ballast","score":0.4803146719932556},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.47723081707954407},{"id":"https://openalex.org/keywords/unsupervised-learning","display_name":"Unsupervised learning","score":0.4586491584777832},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.45624393224716187},{"id":"https://openalex.org/keywords/decision-tree","display_name":"Decision tree","score":0.44593361020088196},{"id":"https://openalex.org/keywords/supervised-learning","display_name":"Supervised learning","score":0.4265061020851135},{"id":"https://openalex.org/keywords/geometry","display_name":"Geometry","score":0.4143630266189575},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.41390857100486755},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.40026095509529114},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3459168076515198},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.32242512702941895},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.32053107023239136},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2842307388782501},{"id":"https://openalex.org/keywords/structural-engineering","display_name":"Structural engineering","score":0.2643667161464691},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.23935163021087646},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.22609776258468628}],"concepts":[{"id":"https://openalex.org/C182377489","wikidata":"https://www.wikidata.org/wiki/Q337829","display_name":"Subgrade","level":2,"score":0.726544201374054},{"id":"https://openalex.org/C2780899546","wikidata":"https://www.wikidata.org/wiki/Q10676734","display_name":"Track geometry","level":3,"score":0.7122495770454407},{"id":"https://openalex.org/C89992363","wikidata":"https://www.wikidata.org/wiki/Q5961558","display_name":"Track (disk drive)","level":2,"score":0.5656624436378479},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.48587292432785034},{"id":"https://openalex.org/C125907379","wikidata":"https://www.wikidata.org/wiki/Q4851537","display_name":"Ballast","level":2,"score":0.4803146719932556},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.47723081707954407},{"id":"https://openalex.org/C8038995","wikidata":"https://www.wikidata.org/wiki/Q1152135","display_name":"Unsupervised learning","level":2,"score":0.4586491584777832},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.45624393224716187},{"id":"https://openalex.org/C84525736","wikidata":"https://www.wikidata.org/wiki/Q831366","display_name":"Decision tree","level":2,"score":0.44593361020088196},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.4265061020851135},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.4143630266189575},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.41390857100486755},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.40026095509529114},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3459168076515198},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.32242512702941895},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.32053107023239136},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2842307388782501},{"id":"https://openalex.org/C66938386","wikidata":"https://www.wikidata.org/wiki/Q633538","display_name":"Structural engineering","level":1,"score":0.2643667161464691},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.23935163021087646},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.22609776258468628},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bigdata.2017.8258381","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata.2017.8258381","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","score":0.4699999988079071,"display_name":"Sustainable cities and communities"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":29,"referenced_works":["https://openalex.org/W41767822","https://openalex.org/W368789077","https://openalex.org/W1963882072","https://openalex.org/W1988975502","https://openalex.org/W1990642378","https://openalex.org/W1998083428","https://openalex.org/W2059050510","https://openalex.org/W2095795470","https://openalex.org/W2118179191","https://openalex.org/W2257789489","https://openalex.org/W2419263299","https://openalex.org/W2468016995","https://openalex.org/W2474319957","https://openalex.org/W2532937681","https://openalex.org/W2560815904","https://openalex.org/W2569890902","https://openalex.org/W2613985007","https://openalex.org/W2616651926","https://openalex.org/W2621909493","https://openalex.org/W2623163150","https://openalex.org/W2627079952","https://openalex.org/W2728599373","https://openalex.org/W2734786621","https://openalex.org/W2737447746","https://openalex.org/W2785778260","https://openalex.org/W6601705716","https://openalex.org/W6720107014","https://openalex.org/W6728596896","https://openalex.org/W6738762299"],"related_works":["https://openalex.org/W2362869222","https://openalex.org/W561758300","https://openalex.org/W570407364","https://openalex.org/W4381988252","https://openalex.org/W2914732806","https://openalex.org/W4283721501","https://openalex.org/W3020868777","https://openalex.org/W287043150","https://openalex.org/W2791887634","https://openalex.org/W582348172"],"abstract_inverted_index":{"Track":[0],"geometry":[1,24,85,103,130,140,168,210],"has":[2],"a":[3],"considerable":[4],"effect":[5,124,203],"on":[6,77,128,171,207],"rail":[7],"travel":[8],"comfort":[9],"and":[10,12,16,33,62,81,96,115,150,191,200,226],"safety":[11],"deteriorates":[13],"with":[14,142],"age":[15],"tonnage.":[17],"In":[18],"order":[19],"to":[20,121,136,146,164,198,217],"maintain":[21],"the":[22,45,68,72,75,78,107,123,138,166,172,202,208,218,224],"track":[23,49,84,102,129,139,167,209],"quality,":[25],"maintenance":[26],"activities":[27,41],"such":[28,51,59,180],"as":[29,52,60,158,181],"tamping,":[30],"stone":[31],"blowing":[32],"ballast":[34],"undercutting":[35],"are":[36,42,90],"usually":[37],"employed.":[38],"However,":[39],"these":[40],"ineffective":[43],"if":[44],"underlying":[46],"cause":[47],"of":[48,98,101,110,125,174,204],"deformation":[50],"subgrade":[53,80],"failure":[54],"is":[55],"not":[56],"addressed.":[57],"Geosyn-thetics":[58],"geocells":[61],"geogrids":[63],"can":[64],"be":[65],"placed":[66],"in":[67,94],"subballast":[69],"which":[70,105],"strengthens":[71],"layer,":[73],"lowers":[74],"stresses":[76],"weak":[79],"invariably":[82],"enhances":[83],"quality.":[86,131,211],"Machine":[87],"learning":[88,117,178],"techniques":[89,118,179],"becoming":[91],"increasingly":[92],"imperative":[93],"processing":[95],"analyzing":[97],"large":[99],"volumes":[100],"data":[104,141,169],"exhibit":[106],"classical":[108],"attributes":[109],"big":[111],"data.":[112,228],"Several":[113],"unsupervised":[114],"supervised":[116],"were":[119,195],"used":[120,135,197],"analyze":[122],"geocell":[126,205],"installation":[127,206],"Cluster":[132],"analysis":[133,155],"was":[134,156,215],"group":[137],"major":[143],"clusters":[144],"found":[145,216],"differ":[147],"by":[148],"surface":[149],"alignment":[151],"features.":[152],"Principal":[153],"component":[154],"employed":[157],"an":[159],"effective":[160],"dimension":[161],"reduction":[162],"tool":[163],"simplify":[165],"based":[170],"proportion":[173],"variance":[175],"explained.":[176],"Supervised":[177],"multiple":[182],"linear":[183],"regression,":[184,187],"decision":[185],"tree":[186],"random":[188],"forest":[189,213],"regression":[190,194,214],"support":[192],"vector":[193],"subsequently":[196],"estimate":[199],"predict":[201],"Random":[212],"best":[219],"performing":[220],"model":[221],"for":[222],"both":[223],"original":[225],"dimensionally-reduced":[227]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":6},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":9},{"year":2018,"cited_by_count":4}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
