To enhance the prediction
accuracy of foundation pit dewatering level and strengthen the control of
dewatering level, three optimization strategies, Logistic chaos mapping,
adaptive dynamic weighting factor w and firefly disturbance optimal
position, are introduced to improve standard pelican optimization algorithm
(POA). Four benchmark test functions are selected to simulate and test the
improved POA (IPOA), and five optimization algorithms are selected for
comparison and analysis. Taking a foundation pit in Shenyang North Railway
Station as an example, the IPOA algorithm is used to optimize the number of
nodes in the hidden layer, the initial learning rate, and the L2 regularization
coefficient in the convolutional neural networks (CNN)-bidirectional
gated recurrent unit (BiGRU) model to form the IPOA-CNN-BiGRU water level prediction model. Additionally, the grey wolf
optimizer (GWO), whale optimization algorithm (WOA), and POA are selected to
optimize the CNN-BiGRU model and build the Midas GTS NX
finite element numerical simulation model. The comparative results among these
models in predicting dewatering level of the foundation pit reveal the
following: (1) The IPOA optimization algorithm shows better performance in both
single-peak and multi-peak
function optimization tasks, and its optimization speed is the fastest in the
four benchmark functions, and the optimization results are significantly better
than the remaining five algorithms. (2) In the water level prediction task, the EMAE, ERMSE, and EMAPE of the IPOA-CNN-BiGRU
model are 0.213 8, 0.253 9, and 0.771 3, respectively, and the prediction
performances are 32.36%, 20.96%, and 15.79% higher than those of the CNN-BiGRU model under GWO, WOA, and POA optimization, respectively. (3)
The IPOA-CNN-BiGRU model has a
low dependence on the parameters compared with the Midas GTS NX finite element
model, and its prediction accuracy is improved by 80.65%, which verifies the
feasibility of the IPOA-CNN-BiGRU
model in predicting dewatering level of the foundation pits.
YU Weihe
,
HUANG Zhiqiang
,
WANG Naiyi
,
ZHANG Ning
.
Application of Improved Pelican Optimization Algorithm-Convolutional Neural Networks-Bidirectional Gated Recurrent Unit Model in Predicting Foundation Pit Dewatering
[J]. Tunnel Construction, 2024
, 44(S2)
: 286
-299
.
DOI: 10.3973/j.issn.2096-4498.2024.S2.028
