In urban building areas, accurate prediction and
control of surface settlement caused by earth pressure balance (EPB) shield
tunneling is essential to construction safety and environmental impacts. Thus,
the authors propose a surface settlement prediction method integrating random
forest (RF), genetic algorithms (GA), and the multi-objective
particle swarm optimization algorithm based on crowding distance (MOPSO-CD) to optimize critical construction parameters. First, data
collected from EPB shield tunneling projects are preprocessed and analyzed to
establish a comprehensive database. A RF regression model is then trained on
this database, with GA used to optimize hyperparameters, resulting in an
intelligent surface settlement prediction model. Feature importance analysis
identifies construction parameters with significant influence on settlement
prediction, reducing the parameter set to eight key factors for constructing
fitness functions. Constraints for each parameter are determined, and the MOPSO-CD algorithm is applied to achieve dual optimization objectives:
minimizing surface settlement and maximizing driving speed. The proposed
settlement prediction model achieves an R2 value of up to 0.937 and a root mean
square error of 11.7 mm, enabling real-time, accurate
surface settlement predictions during shield tunneling. The multi-objective optimization model provides optimal parameter ranges,
reducing average surface settlement to －4.28 mm, only
3.5% of the original value, while maintaining an average driving speed at 58%
of the initial level.
FU Lei
,
WU Huiming
,
HUANG Hongwei
,
ZHANG Dongming
,
CHEN Gang
,
LI Zhanglin
.
Settlement Prediction and Construction Parameters
Optimization Induced by Shield Tunneling Based on Random Forest and Multi-Objective Particle Swarm
Optimization Algorithm-Crowding Distance
[J]. Tunnel Construction, 2025
, 45(S1)
: 318
-329
.
DOI: 10.3973/j.issn.2096-4498.2025.S1.031
