{"id":"https://openalex.org/W4391454566","doi":"https://doi.org/10.1109/access.2024.3361287","title":"Building Damage Assessment Using Feature Concatenated Siamese Neural Network","display_name":"Building Damage Assessment Using Feature Concatenated Siamese Neural Network","publication_year":2024,"publication_date":"2024-01-01","ids":{"openalex":"https://openalex.org/W4391454566","doi":"https://doi.org/10.1109/access.2024.3361287"},"language":"en","primary_location":{"id":"doi:10.1109/access.2024.3361287","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2024.3361287","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/10418211.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/10418211.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5054781142","display_name":"Mgs M Luthfi Ramadhan","orcid":"https://orcid.org/0000-0001-8571-8924"},"institutions":[{"id":"https://openalex.org/I29617571","display_name":"University of Indonesia","ror":"https://ror.org/0116zj450","country_code":"ID","type":"education","lineage":["https://openalex.org/I29617571"]}],"countries":["ID"],"is_corresponding":true,"raw_author_name":"Mgs M. Luthfi Ramadhan","raw_affiliation_strings":["Faculty of Computer Science, University of Indonesia, Depok, Indonesia"],"raw_orcid":"https://orcid.org/0000-0001-8571-8924","affiliations":[{"raw_affiliation_string":"Faculty of Computer Science, University of Indonesia, Depok, Indonesia","institution_ids":["https://openalex.org/I29617571"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5030956546","display_name":"Grafika Jati","orcid":"https://orcid.org/0000-0003-4689-9843"},"institutions":[{"id":"https://openalex.org/I29617571","display_name":"University of Indonesia","ror":"https://ror.org/0116zj450","country_code":"ID","type":"education","lineage":["https://openalex.org/I29617571"]}],"countries":["ID"],"is_corresponding":false,"raw_author_name":"Grafika Jati","raw_affiliation_strings":["Faculty of Computer Science, University of Indonesia, Depok, Indonesia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Faculty of Computer Science, University of Indonesia, Depok, Indonesia","institution_ids":["https://openalex.org/I29617571"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5069933043","display_name":"Wisnu Jatmiko","orcid":"https://orcid.org/0000-0002-0530-7955"},"institutions":[{"id":"https://openalex.org/I29617571","display_name":"University of Indonesia","ror":"https://ror.org/0116zj450","country_code":"ID","type":"education","lineage":["https://openalex.org/I29617571"]}],"countries":["ID"],"is_corresponding":false,"raw_author_name":"Wisnu Jatmiko","raw_affiliation_strings":["Faculty of Computer Science, University of Indonesia, Depok, Indonesia"],"raw_orcid":"https://orcid.org/0000-0002-0530-7955","affiliations":[{"raw_affiliation_string":"Faculty of Computer Science, University of Indonesia, Depok, Indonesia","institution_ids":["https://openalex.org/I29617571"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5054781142"],"corresponding_institution_ids":["https://openalex.org/I29617571"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":2.8281,"has_fulltext":true,"cited_by_count":9,"citation_normalized_percentile":{"value":0.91213687,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":"12","issue":null,"first_page":"19100","last_page":"19116"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9962999820709229,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9962999820709229,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.987500011920929,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T13018","display_name":"Seismology and Earthquake Studies","score":0.9804999828338623,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/concatenation","display_name":"Concatenation (mathematics)","score":0.9053242206573486},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7699912190437317},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.6654130816459656},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.6457803249359131},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6181023716926575},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6063898801803589},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5831409692764282},{"id":"https://openalex.org/keywords/block","display_name":"Block (permutation group theory)","score":0.5600834488868713},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5371843576431274},{"id":"https://openalex.org/keywords/field","display_name":"Field (mathematics)","score":0.47341734170913696},{"id":"https://openalex.org/keywords/feature-vector","display_name":"Feature vector","score":0.4579443335533142},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.4276611804962158},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.41234689950942993},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3725009858608246},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.333553671836853},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.09186533093452454}],"concepts":[{"id":"https://openalex.org/C87619178","wikidata":"https://www.wikidata.org/wiki/Q126002","display_name":"Concatenation (mathematics)","level":2,"score":0.9053242206573486},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7699912190437317},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.6654130816459656},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.6457803249359131},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6181023716926575},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6063898801803589},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5831409692764282},{"id":"https://openalex.org/C2777210771","wikidata":"https://www.wikidata.org/wiki/Q4927124","display_name":"Block (permutation group theory)","level":2,"score":0.5600834488868713},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5371843576431274},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.47341734170913696},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.4579443335533142},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.4276611804962158},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.41234689950942993},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3725009858608246},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.333553671836853},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.09186533093452454},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","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},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2024.3361287","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2024.3361287","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/10418211.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:4d5e375ddc964a6cb2eb0ed36cabb59d","is_oa":true,"landing_page_url":"https://doaj.org/article/4d5e375ddc964a6cb2eb0ed36cabb59d","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 12, Pp 19100-19116 (2024)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2024.3361287","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2024.3361287","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/10418211.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[{"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16","score":0.6100000143051147},{"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10","score":0.44999998807907104}],"awards":[{"id":"https://openalex.org/G2983579501","display_name":null,"funder_award_id":"PUTI Q1","funder_id":"https://openalex.org/F4320323819","funder_display_name":"Universitas Indonesia"},{"id":"https://openalex.org/G3833462595","display_name":null,"funder_award_id":"RST/HKP.05.00/2022","funder_id":"https://openalex.org/F4320323819","funder_display_name":"Universitas Indonesia"},{"id":"https://openalex.org/G5816448351","display_name":null,"funder_award_id":"NKB-395/UN2.RST/HKP.05.00/2022.","funder_id":"https://openalex.org/F4320323819","funder_display_name":"Universitas Indonesia"},{"id":"https://openalex.org/G7230163141","display_name":null,"funder_award_id":"UN2.RST/HKP.05.00/2022","funder_id":"https://openalex.org/F4320323819","funder_display_name":"Universitas Indonesia"}],"funders":[{"id":"https://openalex.org/F4320323819","display_name":"Universitas Indonesia","ror":"https://ror.org/0116zj450"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4391454566.pdf","grobid_xml":"https://content.openalex.org/works/W4391454566.grobid-xml"},"referenced_works_count":35,"referenced_works":["https://openalex.org/W2073873648","https://openalex.org/W2078307930","https://openalex.org/W2089888558","https://openalex.org/W2098057602","https://openalex.org/W2161278885","https://openalex.org/W2171590421","https://openalex.org/W2222447337","https://openalex.org/W2285827343","https://openalex.org/W2286118103","https://openalex.org/W2518879039","https://openalex.org/W2585847175","https://openalex.org/W2625254017","https://openalex.org/W2665856277","https://openalex.org/W2739057980","https://openalex.org/W2739310573","https://openalex.org/W2766373199","https://openalex.org/W2781781489","https://openalex.org/W2903674301","https://openalex.org/W2909767284","https://openalex.org/W2942056403","https://openalex.org/W2945272354","https://openalex.org/W3005273529","https://openalex.org/W3023710712","https://openalex.org/W3034461553","https://openalex.org/W3080427797","https://openalex.org/W3086104743","https://openalex.org/W3111282395","https://openalex.org/W3119324282","https://openalex.org/W4211192415","https://openalex.org/W4226269702","https://openalex.org/W4286781802","https://openalex.org/W4308050263","https://openalex.org/W4312497524","https://openalex.org/W6930814773","https://openalex.org/W6982976890"],"related_works":["https://openalex.org/W2373577936","https://openalex.org/W3095575180","https://openalex.org/W2389596151","https://openalex.org/W4221148444","https://openalex.org/W4226054107","https://openalex.org/W4387678054","https://openalex.org/W4306784355","https://openalex.org/W2784004155","https://openalex.org/W2964954556","https://openalex.org/W1971646133"],"abstract_inverted_index":{"Fast":[0],"and":[1,16,131,151,164,170,176],"accurate":[2],"post-earthquake":[3],"building":[4,46,138],"damage":[5,47,139],"assessment":[6,48],"is":[7,96],"an":[8],"important":[9],"task":[10],"to":[11,13,24,65,99,145],"do":[12],"define":[14],"search":[15],"rescue":[17],"procedures.":[18],"Many":[19],"approaches":[20],"have":[21],"been":[22],"proposed":[23,44],"automate":[25],"this":[26,154],"process":[27],"by":[28,59],"using":[29],"artificial":[30],"intelligence,":[31],"some":[32],"of":[33,124,136,160,167],"which":[34],"use":[35],"handcrafted":[36],"features":[37,75],"that":[38,92],"are":[39,143],"considered":[40],"inefficient.":[41],"This":[42,70],"research":[43],"end-to-end":[45],"based":[49,76],"on":[50,77],"a":[51,61,88],"Siamese":[52,148],"neural":[53,149],"network.":[54],"We":[55],"modify":[56],"the":[57,67,81,93,109,133,146,173],"network":[58,150],"adding":[60],"feature":[62,94],"concatenation":[63,71],"mechanism":[64,72],"enrich":[66],"data":[68],"feature.":[69],"creates":[73],"different":[74],"each":[78],"output":[79],"from":[80],"convolution":[82],"block.":[83],"It":[84],"concatenates":[85],"them":[86],"into":[87],"high-dimensional":[89],"vector":[90],"so":[91],"representation":[95],"more":[97],"likely":[98],"be":[100],"linearly":[101],"separable,":[102],"resulting":[103],"in":[104,153],"better":[105],"discrimination":[106],"capability":[107],"than":[108],"standard":[110,147],"siamese.":[111],"Our":[112,141,156],"model":[113,157],"was":[114],"evaluated":[115],"through":[116],"three":[117],"experimental":[118],"scenarios":[119],"where":[120],"we":[121],"performed":[122],"classification":[123],"G1":[125],"or":[126,129],"G5,":[127,130],"G1-G4":[128],"all":[132],"five":[134],"grades":[135],"EMS-98":[137],"description.":[140],"models":[142],"superior":[144],"state-of-the-art":[152],"field.":[155],"obtains":[158],"f1-scores":[159],"79.47%,":[161],"54.09%,":[162],"40.64%":[163],"accuracy":[165],"scores":[166],"87.24%,":[168],"95.28%,":[169],"42.57%":[171],"for":[172],"first,":[174],"second,":[175],"third":[177],"experiments,":[178],"respectively.":[179]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":2}],"updated_date":"2026-05-25T08:39:21.599409","created_date":"2025-10-10T00:00:00"}
