The ventilation efficiency is low and energy
consumption is high in high-altitude tunnel construction. To
address these challenges, a ventilation data simulation and optimization system
based on generative adversarial networks (GANs) is developed. This system uses
GANs technology to generate high-precision construction
environment data, replacing traditional computational fluid dynamics simulation
methods. By learning the distribution characteristics of multidimensional data
in the construction environment, it generates simulated data in real-time. Subsequently, genetic algorithms and particle swarm
optimization are combined to optimize the parameter configuration of the
ventilation system, enhancing the system′s response
speed and stability. Experimental results show that: (1) The GANs-based ventilation optimization strategy significantly improves
ventilation efficiency and reduces energy consumption, with ventilation
efficiency increasing by 12% and energy consumption decreasing by 8%. (2) The
error between GANs-generated data and actual
measurement data is less than 15%, demonstrating high accuracy and
adaptability. (3) Tests conducted in various construction environments confirm
the effectiveness and robustness of the optimization strategy under complex
conditions, especially in extreme conditions. (4) By integrating GANs
technology, the optimization efficiency of the tunnel construction ventilation
system is significantly improved, reducing safety risks during construction and
providing new technological support for intelligent management of high-altitude tunnel construction.
ZHENG Xuting
,
SUN Sanxiang
,
CUI Shankun
. Analysis and Application of Tunnel Construction
Ventilation Based on Generative Adversarial Networks[J]. Tunnel Construction, 2025
, 45(S1)
: 330
-339
.
DOI: 10.3973/j.issn.2096-4498.2025.S1.032
