Statistically Optimized Back-Propagation Neural-Network Model and Its Application for Deformation Monitoring and Prediction of Concrete-Face Rockfill Dams
Publication: Journal of Performance of Constructed Facilities
Volume 34, Issue 4
Abstract
Concrete-face rockfill dams (CFRDs) are widely used in hydropower engineering. Deformation monitoring and safety operation of CFRDs is of great significance in ensuring the safety of human life and property in downstream areas. A new prediction model for the horizontal displacement of CFRDs, the statistically optimized back-propagation neural network model, was proposed by combining a statistical model and a back-propagation neural network (BPNN) model, which was applied to Deze Dam, Yunnan Province, Southwest China. First, the thermometer selection method is improved based on the correlation coefficients between the measured values of thermometers and the horizontal displacement. Further, combined with water level and time factors, three thermometers with large correlation coefficients were selected and applied to Deze Dam’s model training. On this basis, an improved statistical model for the horizontal displacement of CFRDs is proposed. Subsequently, prediction results of the improved statistical model are taken as an input vector of the traditional BPNN model. Then the statistically optimized BPNN model, a combination of the improved statistical model and BPNN model, is proposed to predict the horizontal displacement of CFRDs. Compared with the improved statistical model and the BPNN model, the statistically optimized BPNN model has a higher prediction accuracy and a strong nonlinear prediction capability, which can compensate for the errors of statistical models and overcome the defects of overfitting and local minima. In addition, the statistically optimized BPNN model proved to have a strong generalization capability by changing the training sample sizes.
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Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
This work was supported by the National Natural Science Foundation of China (Grant No. 51979155), the Natural Science Foundation of Shandong Province (Grant No. ZR2018QEE008), the Shandong Provincial Key Research and Development Program (Grant Nos. 2019GHY112078, 2019JZZY010429, and 2019GSF11040), Key Laboratory of Geotechnical and Underground Engineering of the Ministry of Education (Tongji University) (Grant No. KLE-TJGE-B1902), and the Fundamental Research Funds of Shandong University.
References
Cao, M., P. Qiao, and Q. Ren. 2009. “Improved hybrid wavelet neural network methodology for time-varying behavior prediction of engineering structures.” Neural Comput. Appl. 18 (7): 821–832. https://doi.org/10.1007/s00521-009-0240-8.
Chen, S., C. Gu, C. Lin, E. Zhao, and J. Song. 2018. “Safety monitoring model of a super-high concrete dam by using RBF neural network coupled with kernel principal component analysis.” Math. Problems Eng. 2018: 1–13. https://doi.org/10.1155/2018/1712653.
Dai, B., C. Gu, E. Zhao, and X. Qin. 2018. “Statistical model optimized random forest regression model for concrete dam deformation monitoring.” Struct. Control Health Monit. 25 (6): e2170. https://doi.org/10.1002/stc.2170.
Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. Cambridge, MA: MIT Press.
Gu, C. S., B. Li, G. L. Xu, and H. Yu. 2010. “Back analysis of mechanical parameters of roller compacted concrete dam.” Sci. China Ser. E: Technol. Sci. 53 (3): 848–853. https://doi.org/10.1007/s11431-010-0053-0.
Han, B., L. Zdravković, S. Kontoe, and D. M. G. Taborda. 2016. “Numerical investigation of the response of the Yele rockfill dam during the 2008 Wenchuan Earthquake.” Soil Dyn. Earthquake Eng. 88 (Sep): 124–142. https://doi.org/10.1016/j.soildyn.2016.06.002.
Hu, D. X., Z. Q. Zhou, Y. Li, and X. L. Wu. 2011. “Dam safety analysis based on stepwise regression model.” Adv. Mater. Res. 204-210: 2158–2161. https://doi.org/10.4028/www.scientific.net/AMR.204-210.2158.
Huang, H., B. Chen, and C. Liu. 2015. “Safety monitoring of a super-high dam using optimal kernel partial least squares.” Math. Problems Eng. 2015: 1–13. https://doi.org/10.1155/2015/571594.
Kao, C., and C. Loh. 2013. “Monitoring of long-term static deformation data of Fei-Tsui arch dam using artificial neural network-based approaches.” Struct. Control Health Monit. 20 (3): 282–303. https://doi.org/10.1002/stc.492.
Kim, P. 2018. Deep learning for beginners: With MATLAB examples. Beijing: Beijing University of Aeronautics and Astronautics Press.
Kim, Y., and B. Kim. 2008. “Prediction of relative crest settlement of concrete-faced rockfill dams analyzed using an artificial neural network model.” Comput. Geotech. 35 (3): 313–322. https://doi.org/10.1016/j.compgeo.2007.09.006.
Li, F., Z. Z. Wang, and G. Liu. 2013. “Towards an error correction model for dam monitoring data analysis based on Cointegration Theory.” Struct. Saf. 43 (Jul): 12–20. https://doi.org/10.1016/j.strusafe.2013.02.005.
Mata, J. 2011. “Interpretation of concrete dam behaviour with artificial neural network and multiple linear regression models.” Eng. Struct. 33 (3): 903–910. https://doi.org/10.1016/j.engstruct.2010.12.011.
Mata, J., A. Tavares De Castro, and J. Sá Da Costa. 2014. “Constructing statistical models for arch dam deformation.” Struct. Control Health Monit. 21 (3): 423–437. https://doi.org/10.1002/stc.1575.
Ranković, V., A. Novaković, N. Grujović, D. Divac, and N. Milivojević. 2014. “Predicting piezometric water level in dams via artificial neural networks.” Neural Comput. Appl. 24 (5): 1115–1121. https://doi.org/10.1007/s00521-012-1334-2.
Schölkopf, B., A. Smola, and K. Müller. 1998. “Nonlinear component analysis as a kernel eigenvalue problem.” Neural Comput. 10 (5): 1299–1319. https://doi.org/10.1162/089976698300017467.
Shen, W. W., and J. M. Ren. 2013. “Multiple stepwise regression analysis crack open degree data in gravity dam.” Appl. Mech. Mater. 477–478: 888–891. https://doi.org/10.4028/www.scientific.net/AMM.477-478.888.
Sigtryggsdóttir, F. G., J. T. Snæbjörnsson, and L. Grande. 2018. “Statistical model for dam-settlement prediction and structural-health assessment.” J. Geotech. Geoenviron. Eng. 144 (9): 04018059. https://doi.org/10.1061/(ASCE)GT.1943-5606.0001916.
Stojanovic, B., M. Milivojevic, M. Ivanovic, N. Milivojevic, and D. Divac. 2013. “Adaptive system for dam behavior modeling based on linear regression and genetic algorithms.” Adv. Eng. Software 65 (Nov): 182–190. https://doi.org/10.1016/j.advengsoft.2013.06.019.
Worden, K., C. R. Farrar, G. Manson, and G. Park. 2007. “The fundamental axioms of structural health monitoring.” Proc. R. Soc. A 463 (2082): 1639–1664. https://doi.org/10.1098/rspa.2007.1834.
Wu, Z. R. 2003. Safety monitoring theory and its application of hydraulic structures. Beijing: Higher Education Press.
Xie, Q., J. Liu, B. Han, H. Li, Y. Li, and X. Li. 2019a. “Experimental investigation of interfacial erosion on culvert-soil interface in earth dams.” Soils Found. 59 (3): 671–686. https://doi.org/10.1016/j.sandf.2019.02.004.
Xie, Q., J. Liu, B. Han, H. Li, Y. Li, and X. Li. 2019b. “Experimental and numerical investigation of bottom outlet leakage in earth-fill dams.” J. Perform. Constr. Facil. 33 (3): 04019037. https://doi.org/10.1061/(ASCE)CF.1943-5509.0001302.
Yang, G., T. Yu, X. Yang, and B. Han. 2017. “Seismic resistant effects of composite reinforcement on rockfill dams based on shaking table tests.” J. Earthquake Eng. 21 (6): 1010–1022. https://doi.org/10.1080/13632469.2016.1190800.
Yu, H., Z. R. Wu, T. F. Bao, and L. Zhang. 2010. “Multivariate analysis in dam monitoring data with PCA.” Sci. China Series E: Technol. Sci. 53 (4): 1088–1097. https://doi.org/10.1007/s11431-010-0060-1.
Zhang, F., and W. Hu. 2013. “Application of neural network merging model in dam deformation analysis.” J. Southeast Univ. (English Edition) 29 (4): 441–444. https://doi.org/10.3969/j.issn.1003-7985.2013.04.016.
Zhu, Y., C. Gu, E. Zhao, J. Song, and Z. Guo. 2016. “Structural safety monitoring of high arch dam using improved ABC-BP model.” Math. Problems Eng. 2016: 1–9. https://doi.org/10.1155/2016/6858697.
Zou, J., K. T. Bui, Y. Xiao, and C. V. Doan. 2018. “Dam deformation analysis based on BPNN merging models.” Geo-spatial Inf. Sci. 21 (2): 149–157. https://doi.org/10.1080/10095020.2017.1386848.
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Published In
Journal of Performance of Constructed Facilities
Volume 34 • Issue 4 • August 2020
Copyright
©2020 American Society of Civil Engineers.
History
Received: Nov 20, 2019
Accepted: Mar 9, 2020
Published online: May 20, 2020
Published in print: Aug 1, 2020
Discussion open until: Oct 20, 2020
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