Prediction of foundation pit and main body settlement of high-rise building based on SSA-LSTM recurrent neural network-Reference-Cited by-同舟云学术

Prediction of foundation pit and main body settlement of high-rise building based on SSA-LSTM recurrent neural network

Published:2023-10-20 Issue: Volume:53 Page:1628-1633
ISSN:
Container-title:Proceedings of the 2023 7th International Conference on Electronic Information Technology and Computer Engineering
language:
Short-container-title:

Author:

Jing Xin¹^ORCID,Li Weicheng²^ORCID

Affiliation:

1. Department of Science, Shenyang Jianzhu University, China

2. School of Computer Science and Engineering, Shenyang Jianzhu University, China

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3650400.3650672

Reference10 articles.

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3. CAI Qunqun. Research on deep foundation pit deformation prediction based on particle swarm optimization improved Support vector Machine [J]. Heilongjiang traffic science and technology, 2023, 46-48 (5) : 97-99 + 103. DOI: 10.16402 / j.carol carroll nki issn1008-3383.2023.05.032.

4. Ngoc D. M. Nu N. T. Tinh D. M. van Loi B. & Huong N. T. T. 2020. An analytical model for residual stress prediction in rebound deformation of the foundation pit. Journal of Applied Science and Engineering 23(4) 661–668. https://doi.org/10.6180/jase.202012_23(4).0010

5. Li Wan, Feng Fenling, Jiang Qiwei. Improved particle swarm algorithm to optimize LSTM neural network of railway passenger traffic prediction [J]. Journal of railway science and engineering, 2018, (12) : 3274-3280. The DOI: 10.19713 / j.carol carroll nki. 43-1423 / u 2018.12.033.