The invention relates to the technical field of
tunnel engineering, and discloses a tunnel soft rock
large deformation assessment method based on multi-source
parameter analysis, and the method comprises the steps: firstly constructing an assessment model based on a
grey clustering theory, and carrying out the preliminary
risk classification of tunnel surrounding rock; after a tunnel face is revealed through tunnel excavation, apparent characteristic data are quantitatively collected, a comprehensive
score is calculated, and an apparent
large deformation level is determined; meanwhile, a BP neural
network model optimized through a
particle swarm optimization algorithm is utilized, and the cumulative deformation of the surrounding rock is predicted based on multi-source input parameters; calculating a strength-
stress ratio by combining ground stress and
rock mass strength data actually measured on site, verifying and correcting the preliminary grade, and determining a final comprehensive
large deformation grade; and finally, mapping the grades into support parameters, and executing dynamic adjustment based on
monitoring data. According to the method, through the fusion of theoretical calculation, field quantitative scoring and intelligent prediction, accurate judgment and closed-
loop control of large deformation of the soft rock are realized, and the construction risk is effectively reduced.