Dam Settlement Prediction Based on Random Error Extraction and Multi-Input LSTM Network
Publication: Journal of Surveying Engineering
Volume 148, Issue 3
Abstract
The prediction of dam settlement data plays an important role in analyzing whether the dam is in a safe operation state. But in the field of surveying engineering, the original data measured by instruments will inevitably have random and unpredictable random errors, and the settlement of dams usually has a strong correlation with environmental parameters. In this study, the influence of random error and environmental parameters on dam settlement prediction is discussed, and a prediction model based on multi-input long short-term memory (LSTM) network and random error extraction is proposed. Through the settlement data of a concrete face rockfill dam, the analysis shows that removing random errors can significantly improve the short-term prediction performance and considering environmental parameters can significantly improve the long-term prediction performance. In addition, through comparison and generalization experiments, this method not only has higher prediction accuracy, but also can be applied to other surveying and mapping engineering fields.
Get full access to this article
View all available purchase options and get full access to this article.
Data Availability Statement
Some or all data, models, or code generated or used during the study are proprietary or confidential in nature and may only be provided with restrictions. Models and codes are available from the corresponding author upon reasonable request. However, the settlement data and environment data is restricted.
Acknowledgments
The authors are grateful to the editor and the anonymous reviewers for their valuable suggestions, corrections, and comments that helped improve the original manuscript.
References
Allan, P. 1999. “Approximation theory of the MLP model in neural networks.” Acta Numer. 8 (Jan): 143–195.
Bengio, Y. 2002. “Learning long-term dependencies with gradient descent is difficult.” IEEE Trans. Neural Netw. 5 (2): 157–166. https://doi.org/10.1109/72.279181.
Boudraa, A. O., and J. C. Cexus. 2007. “Emd-based signal filtering.” IEEE Trans. Instrum. Meas. 56 (6): 2196–2202. https://doi.org/10.1109/TIM.2007.907967.
Deng, D., J. Li, Z. Zhang, Y. Teng, and Q. Huang. 2020. “Short-term electric load forecasting based on EEMD-GRU-MLR.” [In Chinese.] Power Syst. Technol. 44 (2): 593–602.
Fei, J., Z. Wu, X. Sun, D. Su, and X. Bao. 2020. “Research on tunnel engineering monitoring technology based on BPNN neural network and mars machine learning regression algorithm.” Neural Comput. Appl. 33 (1): 239–255. https://doi.org/10.1007/s00521-020-04988-3.
Gao, Y., H. Liu, and M. S. Won. 2020. “Behavior of rockfill dam under complex terrain condition.” Arabian J. Geosci. 13 (19): 996. https://doi.org/10.1007/s12517-020-06040-z.
Garabedian, A., A. Bagchi, A. Joshi, and J. Dong. 2006. “Developing an intelligent system for modelling the dam behaviour based on statistical pattern matching of sensory data.” In Proc., Int. Conf. on Computing and Decision Making in Civil and Building Engineering. Reston, VA: ASCE.
Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. Cambridge, MA: MIT Press.
Gu, C.-S., Y. Li, and J. X. Song. 2010. “Study on safety monitoring model for deformation of RCCD.” Chin. J. Comput. Mech. 27 (2): 286–290.
Guo, A., A. Jiang, J. Lin, and X. Li. 2020. “Data mining algorithms for bridge health monitoring: Kohonen clustering and lstm prediction approaches.” J. Supercomput. 76 (2): 932–947. https://doi.org/10.1007/s11227-019-03045-8.
Hinton, G. E., S. Osindero, and Y.-W. Teh. 2006. “A fast learning algorithm for deep belief nets.” Neural Comput. 18 (7): 1527–1554. https://doi.org/10.1162/neco.2006.18.7.1527.
Hochreiter, S., and J. Schmidhuber. 1997. “Long short-term memory.” Neural Comput. 9 (8): 1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735.
Huang, N. E., Z. Shen, S. R. Long, M. C. Wu, H. H. Shih, Q. Zheng, N. C. Yen, C. C. Tung, and H. H. Liu. 1998. “The empirical mode decomposition and the hilbert spectrum for nonlinear and non-stationary time series analysis.” Proc. Math. Phys. Eng. Sci. 454 (1971): 903–995. https://doi.org/10.1098/rspa.1998.0193.
Huang, N. E., M. L. Wu, W. Qu, S. R. Long, and S. Shen. 2010. “Applications of hilbert-huang transform to non-stationary financial time series analysis.” Appl. Stochastic Models Bus. Ind. 19 (3): 245–268. https://doi.org/10.1002/asmb.501.
Li, X., Z. Wen, and H. Su. 2019. “An approach using random forest intelligent algorithm to construct a monitoring model for dam safety.” Eng. Comput. 37 (1): 39–56. https://doi.org/10.1007/s00366-019-00806-0.
Liu, W., J. Pan, Y. Ren, Z. Wu, and J. Wang. 2020. “Coupling prediction model for long-term displacements of arch dams based on long short-term memory network.” Struct. Control Health Monit. 27 (7): e2548.
Ning, Z. A., B. Az, C. Yp, and D. Sls. 2021. “Measurement and prediction of tunnelling-induced ground settlement in karst region by using expanding deep learning method.” Measurement 183 (Oct): 109700.
Penot, I., B. Daumas, and J.-P. Fabre. 2005. “Monitoring behaviour.” Int. Water Power Dam. Const. 57 (12): 24–27.
Rankovic, V., N. Grujovic, D. Divac, and N. Milivojevic. 2014. “Development of support vector regression identification model for prediction of dam structural behaviour.” Struct. Saf. 48 (May): 33–39.
Rumelhart, D. E., G. E. Hinton, and R. J. Williams. 1986. “Learning internal representation by back-propagation errors.” Nature 323 (6088): 533–536. https://doi.org/10.1038/323533a0.
Salazar, F., M. A. Toledo, E. Oñate, and R. Morán. 2015. “An empirical comparison of machine learning techniques for dam behaviour modeling.” Struct. Saf. 56 (Sep): 9–17. https://doi.org/10.1016/j.strusafe.2015.05.001.
Shu, X., T. Bao, Y. Li, J. Gong, and K. Zhang. 2021. “VAE-TALSTM: A temporal attention and variational autoencoder-based long short-term memory framework for dam displacement prediction.” Eng. Comput. 1–16. https://doi.org/10.1007/s00366-021-01362-2.
Tatin, M., M. Briffaut, F. Dufour, A. Simon, and J. Fabre. 2013. “Thermal displacements of concrete dams: Finite element and statistical modelling.” In Proc., 9th ICOLD European Club Symp. Rome: Italian Committee on Large Dams.
Vapnik, V. N. 1995. The nature of statistical learning theory. New York: Springer.
Willm, G., and N. Beaujoint. 1967. “Les méthodes de surveillance des barrages au service de la production hydraulique d’Electricité de France, problèmes anciens et solutions nouvelles.” In Proc., IXth Int. Congress on Large Dams. Oxfordshire, UK: Routledge.
Zaremba, W., I. Sutskever, and O. Vinyals. 2014. “Recurrent neural network regularization.” Preprint, submitted September 8, 2014. https://arxiv.org/abs/1409.2329.
Information & Authors
Information
Published In
Journal of Surveying Engineering
Volume 148 • Issue 3 • August 2022
Copyright
© 2022 American Society of Civil Engineers.
History
Received: Dec 1, 2021
Accepted: Mar 24, 2022
Published online: Apr 29, 2022
Published in print: Aug 1, 2022
Discussion open until: Sep 29, 2022
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.