PMID- 37015985
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20230405
LR  - 20230407
IS  - 2045-2322 (Electronic)
IS  - 2045-2322 (Linking)
VI  - 13
IP  - 1
DP  - 2023 Apr 4
TI  - Prediction method of surface settlement of rectangular pipe jacking tunnel based 
      on improved PSO-BP neural network.
PG  - 5512
LID - 10.1038/s41598-023-32189-0 [doi]
LID - 5512
AB  - To provide theoretical support for the safety control of rectangular pipe jacking 
      tunnels crossing an existing expressway, a method for predicting the surface 
      settlement of a rectangular pipe jacking tunnel is proposed in this study. 
      Therefore, based on the high approximation of the BP neural network to any 
      function under the multiparameter input, the PSO-BP mixed prediction model of the 
      ground subsidence of the ultrashallow buried large section rectangular pipe 
      jacking tunnel is established by taking into account the adaptive mutation 
      method, adopting the improved particle swarm optimization (IPSO) algorithm with 
      adaptive inertia weight and mutation particles in the later stage to determine 
      the optimal hyperparameters of the prediction model. Through the case study of an 
      ultrashallow large cross-section rectangular pipe jacking tunnel, this algorithm 
      is compared with the traditional algorithm and combined with field monitoring 
      data for analysis and prediction. The prediction results show that compared with 
      the traditional BP neural network prediction model, AWPSO-BP model and PWPSO-BP 
      model, the improved PSO-BP mixed prediction model shows a more stable prediction 
      effect when the change in surface subsidence is gentle and the concavity and 
      convexity are large. The predicted subsidence value is close to the actual value, 
      and the accuracy and robustness of the prediction are significantly improved.
CI  - (c) 2023. The Author(s).
FAU - Hu, Da
AU  - Hu D
AD  - Hunan Engineering Research Center of Structural Safety and Disaster Prevention 
      for Urban Underground Infrastructure, Hunan City University, Yiyang, 413000, 
      People's Republic of China. huda@hncu.edu.cn.
AD  - College of Civil Engineering, Hunan City University, Yiyang, 413000, People's 
      Republic of China. huda@hncu.edu.cn.
AD  - Hunan Provincial Key Laboratory of Key Technology on Hydropower Development, 
      Power China Zhongnan Engineering Co. Ltd., Changsha, 410014, People's Republic of 
      China. huda@hncu.edu.cn.
FAU - Hu, Yongjia
AU  - Hu Y
AD  - College of Civil Engineering, Hunan City University, Yiyang, 413000, People's 
      Republic of China.
FAU - Yi, Shun
AU  - Yi S
AD  - College of Civil Engineering, Hunan City University, Yiyang, 413000, People's 
      Republic of China.
FAU - Liang, Xiaoqiang
AU  - Liang X
AD  - Hunan Engineering Research Center of Structural Safety and Disaster Prevention 
      for Urban Underground Infrastructure, Hunan City University, Yiyang, 413000, 
      People's Republic of China.
AD  - College of Civil Engineering, Hunan City University, Yiyang, 413000, People's 
      Republic of China.
FAU - Li, Yongsuo
AU  - Li Y
AD  - Hunan Engineering Research Center of Structural Safety and Disaster Prevention 
      for Urban Underground Infrastructure, Hunan City University, Yiyang, 413000, 
      People's Republic of China.
FAU - Yang, Xian
AU  - Yang X
AD  - Hunan Engineering Research Center of Structural Safety and Disaster Prevention 
      for Urban Underground Infrastructure, Hunan City University, Yiyang, 413000, 
      People's Republic of China.
AD  - College of Civil Engineering, Hunan City University, Yiyang, 413000, People's 
      Republic of China.
LA  - eng
GR  - 2019YR02/Science and Technology Innovation Project of Yiyang City/
GR  - 2020YR02/Science and Technology Innovation Project of Yiyang City/
GR  - PKLHD202005/Open Research Foundation of Hunan Provincial Key Laboratory of Key 
      Technology on Hydropower Development/
GR  - 2021JJ50147/Natural Science Foundation of Hunan Province/
GR  - 51678226/National Natural Science Foundation of China/
GR  - 2021JJ30078/Natural Science Foundation of Hunan Province/
PT  - Journal Article
DEP - 20230404
PL  - England
TA  - Sci Rep
JT  - Scientific reports
JID - 101563288
SB  - IM
PMC - PMC10073122
COIS- The authors declare no competing interests.
EDAT- 2023/04/05 06:00
MHDA- 2023/04/05 06:01
PMCR- 2023/04/04
CRDT- 2023/04/04 23:19
PHST- 2022/12/12 00:00 [received]
PHST- 2023/03/23 00:00 [accepted]
PHST- 2023/04/05 06:01 [medline]
PHST- 2023/04/04 23:19 [entrez]
PHST- 2023/04/05 06:00 [pubmed]
PHST- 2023/04/04 00:00 [pmc-release]
AID - 10.1038/s41598-023-32189-0 [pii]
AID - 32189 [pii]
AID - 10.1038/s41598-023-32189-0 [doi]
PST - epublish
SO  - Sci Rep. 2023 Apr 4;13(1):5512. doi: 10.1038/s41598-023-32189-0.