{"doi":"10.2166/ws.2023.170","title":"Tunnel water burst disaster management engineering based on artificial intelligence technology – taking Yonglian Tunnel in Jiangxi Province as the object in China","abstract":"Abstract\n Due to the influence of the groundwater system, mountain rock layers, climate rainfall, and tunnel length and depth, underground tunnels (UT) are prone to water inrush (WI) disasters, thus leading to delays and obstacles in construction projects. This paper takes the Yonglian Tunnel as the research objective and explores the water and mud inrush disasters that occurred from July to August 2012. The Yonglian Tunnel is a control project of the Jilian Expressway in Jiangxi Province. This paper aims to study and analyze the WI disaster management of the UT using artificial intelligence technology, and to deepen the understanding of its causes. It will affect the factors, hazards, and related disaster management engineering methods of the UT WI disaster. By establishing a back-propagation neural network model and a radial basis function neural network model, the risk of WI disasters in tunnels, the degree of harm caused by WI, and the ability to control them were predicted and analyzed, and the stability and error values of the models were compared.","journal":"Water Supply","year":2023,"id":678318,"datarank":0.32958368660043297,"base_score":2.1972245773362196,"endowment":2.1972245773362196,"self_citation_contribution":0.32958368660043297,"citation_network_contribution":0.0,"self_endowment_contribution":0.32958368660043297,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":8,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1772300,"name":"Haowen Xu","orcid":null,"position":1,"is_corresponding":false},{"id":162269,"name":"Ting Jiang","orcid":null,"position":2,"is_corresponding":false},{"id":114856,"name":"Hong Ding","orcid":null,"position":3,"is_corresponding":false},{"id":441823,"name":"Yong Xiang","orcid":"0000-0001-5757-9023","position":4,"is_corresponding":false},{"id":89445,"name":"Dandan Li","orcid":"0000-0002-3527-3522","position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"Tunnel water burst disaster management engineering based on artificial intelligence technology – taking Yonglian Tunnel in Jiangxi Province as the object in China","abstract":"Abstract\n Due to the influence of the groundwater system, mountain rock layers, climate rainfall, and tunnel length and depth, underground tunnels (UT) are prone to water inrush (WI) disasters, thus leading to delays and obstacles in construction projects. This paper takes the Yonglian Tunnel as the research objective and explores the water and mud inrush disasters that occurred from July to August 2012. The Yonglian Tunnel is a control project of the Jilian Expressway in Jiangxi Province. This paper aims to study and analyze the WI disaster management of the UT using artificial intelligence technology, and to deepen the understanding of its causes. It will affect the factors, hazards, and related disaster management engineering methods of the UT WI disaster. By establishing a back-propagation neural network model and a radial basis function neural network model, the risk of WI disasters in tunnels, the degree of harm caused by WI, and the ability to control them were predicted and analyzed, and the stability and error values of the models were compared.","is_dataset_classified":null,"base_score":2.1972245773362196,"endowment":2.1972245773362196,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"26207759","pmcid":null,"openalex_id":"https://openalex.org/W4384030712","authors":[],"funders":[],"total_grants":0,"fwci":1.0709,"citation_percentile":0.73407347,"influential_citations":0,"citation_trend":[{"year":2023,"count":1},{"year":2024,"count":2},{"year":2025,"count":3},{"year":2026,"count":2}],"oa_status":"hybrid","license":"cc-by","oa_locations":[{"url":"https://iwaponline.com/ws/article-pdf/doi/10.2166/ws.2023.170/1250967/ws2023170.pdf","host_type":"journal"},{"url":"https://iwaponline.com/ws/article-pdf/doi/10.2166/ws.2023.170/1250967/ws2023170.pdf","host_type":"publisher"},{"url":"https://iwaponline.com/ws/article-pdf/23/8/3377/1281454/ws023083377.pdf","host_type":"publisher"},{"url":"https://doi.org/10.2166/ws.2023.170","host_type":"journal"},{"url":"https://doaj.org/article/60f3c5ccfd3c40e985594a3b64bb2f3c","host_type":"repository"}],"fields_of_study":["Tunneling and Rock Mechanics","Rock Mechanics and Modeling","Geotechnical Engineering and Analysis"],"mesh_terms":[],"keywords":["Inrush current","Emergency management","Civil engineering","Research Object","China","Harm","Artificial neural network","Environmental science","Computer science","Engineering","Mining engineering","Geography","Artificial intelligence"],"sdg_mappings":[{"sdg_number":0,"sdg_label":"Climate action"}],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-08-17T05:16:40.961649Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}