{"id":"https://openalex.org/W4403709143","doi":"https://doi.org/10.1145/3690407.3690412","title":"Research on Rockburst Intensity Prediction Based on Grey Wolf Optimization Algorithm and Random Forest","display_name":"Research on Rockburst Intensity Prediction Based on Grey Wolf Optimization Algorithm and Random Forest","publication_year":2024,"publication_date":"2024-06-21","ids":{"openalex":"https://openalex.org/W4403709143","doi":"https://doi.org/10.1145/3690407.3690412"},"language":"en","primary_location":{"id":"doi:10.1145/3690407.3690412","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3690407.3690412","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2024 4th International Conference on Artificial Intelligence, Big Data and Algorithms","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/A5110263259","display_name":"Zheng Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Zheng Chen","raw_affiliation_strings":["Shandong High Speed Engineering Testing Co.,Ltd., Jinan, Shandong, China"],"affiliations":[{"raw_affiliation_string":"Shandong High Speed Engineering Testing Co.,Ltd., Jinan, Shandong, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5109692713","display_name":"Zhenhu Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhenhu Zhang","raw_affiliation_strings":["Shandong High Speed Engineering Testing Co.,Ltd., Jinan, Shandong, China"],"affiliations":[{"raw_affiliation_string":"Shandong High Speed Engineering Testing Co.,Ltd., Jinan, Shandong, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5113976269","display_name":"Wanli Tian","orcid":"https://orcid.org/0000-0002-4871-100X"},"institutions":[{"id":"https://openalex.org/I4210107055","display_name":"China Academy of Transportation Sciences","ror":"https://ror.org/012f3dh63","country_code":"CN","type":"other","lineage":["https://openalex.org/I4210107055"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wanli Tian","raw_affiliation_strings":["China Academy of Transportation Science, Beijing, China"],"affiliations":[{"raw_affiliation_string":"China Academy of Transportation Science, Beijing, China","institution_ids":["https://openalex.org/I4210107055"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5032617206","display_name":"Qingxia Li","orcid":"https://orcid.org/0009-0002-8160-9565"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Qingying Li","raw_affiliation_strings":["Shandong High Speed Engineering Testing Co.,Ltd., Jinan, Shandong, China"],"affiliations":[{"raw_affiliation_string":"Shandong High Speed Engineering Testing Co.,Ltd., Jinan, Shandong, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5111288284","display_name":"Xiang Li","orcid":"https://orcid.org/0000-0002-1395-5266"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xiang Li","raw_affiliation_strings":["Shandong High Speed Engineering Testing Co.,Ltd., Jinan, Shandong, China"],"affiliations":[{"raw_affiliation_string":"Shandong High Speed Engineering Testing Co.,Ltd., Jinan, Shandong, China","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5109725565","display_name":"Tao Zhang","orcid":"https://orcid.org/0009-0002-2454-7994"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tonghui Zhang","raw_affiliation_strings":["Shandong High Speed Engineering Testing Co.,Ltd., Jinan, Shandong, China"],"affiliations":[{"raw_affiliation_string":"Shandong High Speed Engineering Testing Co.,Ltd., Jinan, Shandong, China","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5110263259"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.7103,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.79253406,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":96,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"23","last_page":"30"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10535","display_name":"Landslides and related hazards","score":0.9968000054359436,"subfield":{"id":"https://openalex.org/subfields/2308","display_name":"Management, Monitoring, Policy and Law"},"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/T10535","display_name":"Landslides and related hazards","score":0.9968000054359436,"subfield":{"id":"https://openalex.org/subfields/2308","display_name":"Management, Monitoring, Policy and Law"},"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/T10161","display_name":"Rock Mechanics and Modeling","score":0.995199978351593,"subfield":{"id":"https://openalex.org/subfields/2211","display_name":"Mechanics of Materials"},"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/T14392","display_name":"Geoscience and Mining Technology","score":0.9758999943733215,"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/computer-science","display_name":"Computer science","score":0.5874115824699402},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.577036440372467},{"id":"https://openalex.org/keywords/intensity","display_name":"Intensity (physics)","score":0.5352694988250732},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.5175437331199646},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.32286784052848816},{"id":"https://openalex.org/keywords/optics","display_name":"Optics","score":0.079474538564682},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.07033157348632812}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5874115824699402},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.577036440372467},{"id":"https://openalex.org/C93038891","wikidata":"https://www.wikidata.org/wiki/Q1061524","display_name":"Intensity (physics)","level":2,"score":0.5352694988250732},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5175437331199646},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.32286784052848816},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.079474538564682},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.07033157348632812}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3690407.3690412","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3690407.3690412","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2024 4th International Conference on Artificial Intelligence, Big Data and Algorithms","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/13","score":0.5600000023841858,"display_name":"Climate action"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":13,"referenced_works":["https://openalex.org/W1971891346","https://openalex.org/W2020124992","https://openalex.org/W2023010951","https://openalex.org/W2066813588","https://openalex.org/W2461475580","https://openalex.org/W2762138843","https://openalex.org/W2791165267","https://openalex.org/W2806312888","https://openalex.org/W2807536986","https://openalex.org/W2925700532","https://openalex.org/W2941242975","https://openalex.org/W3043233051","https://openalex.org/W3205923397"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W2051487156","https://openalex.org/W3193043704","https://openalex.org/W4386259002","https://openalex.org/W1546989560","https://openalex.org/W2073681303","https://openalex.org/W3171520305","https://openalex.org/W3135126032"],"abstract_inverted_index":{"In":[0],"order":[1],"to":[2,29,98,121,128],"improve":[3],"the":[4,17,23,31,42,44,78,91,102,105,113,117,122,130,135,141],"accuracy":[5,107,146],"of":[6,41,48,81,125,134],"rockburst":[7,33,79,94,148,159],"prediction,":[8,149],"this":[9],"paper":[10],"proposes":[11],"a":[12,154],"GWO-RF":[13,103],"model,":[14,104],"which":[15,150],"combines":[16],"random":[18],"forest":[19],"classifier":[20],"(RF)":[21],"and":[22,67,85,100,110,132,174],"grey":[24],"wolf":[25],"optimization":[26],"algorithm":[27],"(GWO)":[28],"obtain":[30],"best":[32],"prediction":[34,106],"performance.":[35],"First,":[36],"select":[37,77],"six":[38],"key":[39],"parameters":[40],"rock,":[43],"maximum":[45],"shear":[46],"stress":[47,60],"surrounding":[49],"rock":[50],"\u03c3\u03b8,":[51],"uniaxial":[52,56],"compressive":[53],"strength":[54,58],"\u03c3c,":[55],"tensile":[57],"\u03c3t,":[59],"concentration":[61],"coefficient":[62,65],"\u03c3\u03b8/\u03c3c,":[63],"brittleness":[64],"\u03c3c/\u03c3t":[66],"elastic":[68],"energy":[69],"index":[70],"Wet":[71],"are":[72],"used":[73,97],"as":[74,88,153],"input":[75],"data,":[76],"intensity":[80],"no,":[82],"light,":[83],"medium":[84],"strong":[86],"categories":[87],"output;":[89],"then,":[90],"collected":[92],"249":[93],"cases":[95],"were":[96],"train":[99],"test":[101,129],"reached":[108],"90.14%,":[109],"compared":[111],"with":[112],"traditional":[114],"model;":[115],"finally,":[116],"method":[118],"was":[119],"applied":[120],"Wulaofeng":[123],"Tunnel":[124],"Jiangeyuan":[126],"Expressway":[127],"practicability":[131],"effectiveness":[133],"model.":[136],"The":[137],"results":[138],"indicate":[139],"that":[140],"model":[142],"has":[143,162],"demonstrated":[144],"better":[145],"in":[147],"can":[151],"serve":[152],"reference":[155],"for":[156,165],"deep":[157],"tunnel":[158],"prediction.":[160],"This":[161],"profound":[163],"implications":[164],"ensuring":[166],"project":[167,170],"safety,":[168],"reducing":[169],"delays,":[171],"environmental":[172],"protection,":[173],"advancing":[175],"engineering":[176],"technology.":[177]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}
