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第 46 卷             胡倩然,等: 基于人工神经网络的居民住宅燃气爆炸后果预测                                 第 5 期

                    CHEN Z W, WANG Z Q, ZENG L H. A method for predicting peak pressure in an explosion shock tube based on BP neural
                    network [J]. Explosion and Shock Waves, 2024, 44(5): 054101. DOI: 10.11883/bzycj-2023-0187.
               [15]   XU Y, HUANG Y, MA G. A beetle antennae search improved BP neural network model for predicting multi-factor-based gas
                    explosion  pressures  [J].  Journal  of  Loss  Prevention  in  the  Process  Industries,  2020,  65:  104117.  DOI:  10.1016/j.jlp.2020.
                    104117.
               [16]   VIANNA S S V, CANT R S. Explosion pressure prediction via polynomial mathematical correlation based on advanced CFD
                    modelling [J]. Journal of Loss Prevention in the Process Industries, 2012, 25(1): 81–89. DOI: 10.1016/j.jlp.2011.07.005.
               [17]   LIU L, LIU J, ZHOU Q, et al. An SVR-based machine learning model depicting the propagation of gas explosion disaster
                    hazards [J]. Arabian Journal for Science and Engineering, 2021, 46(10): 10205–10216. DOI: 10.1007/s13369-021-05616-5.
               [18]   XU Q, CHEN G, SU S, et al. Prediction of venting gas explosion overpressure based on a combination of explosive theory and
                    machine learning [J]. Expert Systems with Applications, 2023, 234: 121044. DOI: 10.1016/j.eswa.2023.121044.
               [19]   IDRIS  A  M,  RUSLI  R,  MOHAMED  M  E,  et  al.  Explosion  pressure  and  duration  prediction  using  machine  learning:  a
                    comparative  study  using  classical  models  with  adam-optimized  neural  network  [J].  Canadian  Journal  of  Chemical
                    Engineering, 2025, 103(1): 137–152. DOI: 10.1002/cjce.25258.
               [20]   HEMMATIAN  B,  CASAL  J,  PLANAS  E,  et  al.  Prediction  of  BLEVE  mechanical  energy  by  implementation  of  artificial
                    neural network [J]. Journal of Loss Prevention in the Process Industries, 2020, 63: 104021. DOI: 10.1016/j.jlp.2019.104021.
               [21]   庞磊, 胡倩然, 马菲菲, 等. 泄爆面特征参数对天然气爆炸超压峰值的影响规律 [J]. 中国安全生产科学技术, 2020, 16(4):
                    126–131. DOI: 10.11731/j.issn.1673-193x.2020.04.020.
                    PANG L, HU Q R, MA F F, et al. Effect of vent characteristic parameters on overpressure peaks of natural gas explosion [J].
                    Journal of Safety Science and Technology, 2020, 16(4): 126–131. DOI: 10.11731/j.issn.1673-193x.2020.04.020.
               [22]   MOLKOV  V  V,  GRIGORASH  A  V,  EBER  R  M.  Vented  gaseous  deflagrations:  modelling  of  spring-loaded  inertial  vent
                    covers [J]. Fire Safety Journal, 2005, 40(4): 307–319. DOI: 10.1016/j.firesaf.2005.01.004.
               [23]   JIANG H, CHI M, HOU D, et al. Numerical investigation and analysis of indoor gas explosion: a case study of “6·13”
                    major gas explosion accident in hubei province, china [J]. Journal of Loss Prevention in the Process Industries, 2023, 83:
                    105045. DOI: 10.1016/j.jlp.2023.105045.
               [24]   HU Q R, ZHANG Q, YUAN M Q, et al. Traceability and failure consequences of natural gas explosion accidents based on key
                    investigation technology [J]. Engineering Failure Analysis, 2022, 139: 106448. DOI: 10.1016/j.engfailanal.2022.106448.
               [25]   ZHANG Q, WANG Y, LIAN Z. Explosion hazards of LPG-air mixtures in vented enclosure with obstacles [J]. Journal of
                    Hazardous Materials, 2017, 334: 59–67. DOI: 10.1016/j.jhazmat.2017.03.065.
               [26]   PANG L, HU Q, ZHAO J, et al. Numerical study of the effects of vent opening time on hydrogen explosions [J]. International
                    Journal of Hydrogen Energy, 2019, 44(29): 15689–15701. DOI: 10.1016/j.ijhydene.2019.04.175.
               [27]   VYAZMINA  E,  JALLAIS  S.  Validation  and  recommendations  for  FLACS  CFD  and  engineering  approaches  to  model
                    hydrogen vented explosions: effects of concentration, obstruction vent area and ignition position [J]. International Journal of
                    Hydrogen Energy, 2016, 41(33): 15101–15109. DOI: 10.1016/j.ijhydene.2016.05.189.
               [28]   MA Q, HE Y, GUO Y, et al. Research on the effect of the vent area on the external deflagration process during the explosion
                    vent [J]. Fuel, 2022, 329: 125440. DOI: 10.1016/j.fuel.2022.125440.
               [29]   陈晔, 李毅, 李紫婷, 等. 受限空间氢泄爆外部超压特性研究 [J]. 消防科学与技术, 2022, 41(3): 310–315. DOI:
                    10.3969/j.issn.1009-0029.2022.03.005.
                    CHEN Y, LI Y, LI Z T, et al. Ignition characteristics of finite thick PMMA exposed to thermal radiation [J]. Fire Science and
                    Technology, 2022, 41(3): 310–315. DOI: 10.3969/j.issn.1009-0029.2022.03.005.
               [30]   于佳航. 基于机器学习的双燃料动力船舶机舱燃爆事故后果预测方法研究 [D]. 大连: 大连海事大学, 2022.
                    YU J H. Research on consequence prediction method of engine room combustion and explosion accident of dual fuel power
                    ship based on machine learning [D]. DaLian: Dalian Maritime University, 2022.
               [31]   SHI J, KHAN F, ZHU Y, et al. Robust data-driven model to study dispersion of vapor cloud in offshore facility [J]. Ocean
                    Engineering, 2018, 161: 98–110. DOI: 10.1016/j.oceaneng.2018.04.098.
               [32]   SHI  J,  LI  J,  HAO  H,  et  al.  Vented  gas  explosion  overpressure  prediction  of  obstructed  cubic  chamber  by  Bayesian
                    regularization artificial neuron network: Bauwens model [J]. Journal of Loss Prevention in the Process Industries, 2018, 56:
                    209–216. DOI: 10.1016/j.jlp.2018.05.016.
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