Herein, a method characterizing the spatial effects of
deep large-diameter shaft structures in water resource allocation projects is
established. Based on a three-dimensional finite
element model, a database is constructed to capture the influence of typical
geological conditions and structural dimensions on shaft deformation. To
further predict the overall structural deformation during the construction
process, a multi-output prediction model for shaft
displacement is proposed. Two algorithms, gradient boosting trees and random
forest, are selected, and three combinations of single-target
models, multi-output models, and regressor chain models
are employed. This results in the creation of six prediction models: single-target gradient boosting, multi-output
gradient boosting (MO-GB), regressor chain gradient
boosting, single-target random forest, multi-output random forest, and regressor chain random forest. The results
indicate that: (1) The MO-GB model considers multiple
prediction indicators, and its corresponding maximum deformation ERMS is 0.457, smaller than other models; it
performs the best in terms of predictive accuracy, with a determination value R2 for maximum displacement and its
corresponding location exceeds 0.98. (2) The maximum deformation value and its corresponding location
using MO-GB model align well with three-dimensional finite element model results, providing guidance for
design of shaft structure.
LIN Huasheng
,
TANG Xinwei
,
NIE Ding
,
HUANG Wenmin
,
SONG Danqing
. Prediction of Deep and Large Shaft Deformation Based
on Multi-Output Machine Learning Model[J]. Tunnel Construction, 2025
, 45(S1)
: 113
-124
.
DOI: 10.3973/j.issn.2096-4498.2025.S1.013
