[1]乔力伟,桑琮辉,程元.施工隧道浮尘初始释放量的求解与耦合模型训练*[J].中国安全生产科学技术,2026,22(3):84-91.[doi:10.11731/j.issn.1673-193x.2026.03.011]
QIAO Liwei,SANG Conghui,CHENG Yuan.Solution of initial emission amount of floating dust in construction tunnels and training of coupled model[J].Journal of Safety Science and Technology,2026,22(3):84-91.[doi:10.11731/j.issn.1673-193x.2026.03.011]
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施工隧道浮尘初始释放量的求解与耦合模型训练*
QIAO Liwei,SANG Conghui,CHENG Yuan.Solution of initial emission amount of floating dust in construction tunnels and training of coupled model[J].Journal of Safety Science and Technology,2026,22(3):84-91.[doi:10.11731/j.issn.1673-193x.2026.03.011]
《中国安全生产科学技术》[ISSN:1673-193X/CN:11-5335/TB]
- 卷:
- 22
- 期数:
- 2026年3期
- 页码:
- 84-91
- 栏目:
- 安全工程技术
- 出版日期:
- 2026-03-30
文章信息/Info
- Title:
- Solution of initial emission amount of floating dust in construction tunnels and training of coupled model
- 文章编号:
- 1673-193X(2026)-03-0084-08
- Keywords:
- construction tunnel; ventilation and dust removal; suspended dust emission calculation; high-precision simulation
- 分类号:
- X947
- 文献标志码:
- A
- 摘要:
- 为解决钻爆法施工隧道浮尘初始释放量无法直接测量而导致主观赋值的关键问题，以某在建双线铁路客运隧道为依托，基于弥散衰减模型构建浮尘质量浓度拟合算法后求解浮尘释放量，同时采用智能神经网络训练得出浮尘影响的特征参数耦合模型，并结合ANSYS-Fluent仿真模拟验证模型精度。研究结果表明：拟合结果可用于隧道实际浮尘初始排放量的表述，通风30 min后,作业Ⅰ~Ⅳ中浮尘质量浓度实测值与模拟值的最大相对误差率分别为9.42%,12.92%,12.12%和11.26%，精度满足工程要求，将其导入神经网络模型训练后可对不同工况下的浮尘释放量进行快速预测，简化了释放量测算，其结果更具鲁棒性，进一步提升了初始参数的确定效率。研究结果可为施工隧道通风布局与设计提供参考。
- Abstract:
- In order to address the critical problem of subjective value assignment arising from the inability to directly measure the initial release amount of suspended dust in drill-and-blast tunnels,a dust mass concentration fitting algorithm is established based on a dispersion attenuation model using a under-construction double-track railway passenger tunnel as the study object,from which the dust release amount is derived.An intelligent neural network is simultaneously trained to obtain a coupled model of characteristic parameters governing dust behavior,and the model accuracy is verified through ANSYS-Fluent numerical simulation.The results show that the fitting results are capable of representing the actual initial dust emission in tunnels.After 30 minutes of ventilation,the maximum relative errors between the measured and simulated dust mass concentrations under working conditions Ⅰ~Ⅳ are 9.42%,12.92%,12.12%,and 11.26%,respectively,with accuracy satisfying engineering requirements.When these results are imported into the neural network model for training,rapid prediction of dust release amounts under different operating conditions becomes achievable,simplifying the calculation of release amounts and yielding more robust results,thereby further improving the efficiency of initial parameter determination.These findings provide a reference for the ventilation layout and design of construction tunnels.
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备注/Memo
- 备注/Memo:
-
收稿日期： 2025-12-14
* 基金项目： 博士科研启动基金项目(BSJ2021001)；安阳市科技发展计划科技攻关项目(2022C01SF005)
作者简介： 乔力伟，博士，讲师，主要研究方向为交通安全与环境。
