Combined Prediction Model for High-Speed Railway Bridge Pier Settlement Based on Robust Weighted Total Least-Squares Autoregression and Adaptive Dynamic Cubic Exponential Smoothing
Publication: Journal of Surveying Engineering
Volume 149, Issue 2
Abstract
The prediction of high-speed railway bridge pier settlement is important for the safety of high-speed railway engineering. At present, a common method in settlement prediction is the curve fitting model in single prediction models. However, it may be difficult to describe the settlement rule of high-speed railway bridge piers using a curve fitting model with limited observation data during time-constrained construction periods. Moreover, relying on only a single prediction model usually does not allow for full exploration of the potential information in the data and poses the problem of poor stability and applicability. To solve this issue, a combined prediction model that uses the optimal nonnegative variable weight combination based on robust weighted total least-squares autoregression (RWTLS-AR) and adaptive dynamic cubic exponential smoothing (ADCES) is proposed to combine the advantages of two single prediction models. The RWTLS-AR model, using a robust weighted total least-squares method, has high prediction accuracy in the case of fewer observation data. At the same time, the adaptive dynamic judgment mechanism is established using the ADCES model to improve stability. The proposed model is applied to the settlement prediction of high-speed railway bridge pier, and three sets of observation data are used for evaluation. A comparison is made with two single prediction models and three other combined prediction models. The results show that the mean absolute error, root-mean-square error, and mean absolute percentage error of the proposed model are respectively 0.092 mm, 0.101, mm and 5.936% in the first set of observations, 0.099 mm, 0.118 mm, and 6.592% in the second set of observations, and 0.177 mm, 0.203 mm, and 15.914% in the third set of observations. This indicates that the proposed model is more accurate and stable than all the aforementioned prediction models.
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, and code that support the findings of this study are available from the corresponding author upon reasonable request. (The types of raw data, e.g., the IGG weight function matrix, the smoothing values, or the combined weights, can be provided as a MAT or CSV file.)
Acknowledgments
This work was jointly supported by the Natural Science Foundation of China (Nos. 42101457, 42061077, and 41974013). The authors are grateful to the two anonymous reviewers and editor for their careful review and valuable suggestions, which helped improve the original manuscript.
References
Amiri-Simkooei, A. R. 2017. “Weighted total least squares with singular covariance matrices subject to weighted and hard constraints.” J. Surv. Eng. 143 (4): 04017018. https://doi.org/10.1061/(ASCE)SU.1943-5428.0000239.
Chen, Z. 2020. “Evaluation of longitudinal connected track under combined action of running train and long-term bridge deformation.” J. Vib. Control 26 (7–8): 599–609. https://doi.org/10.1177/1077546319889855.
Chen, Z., L. Bi, and J. Zhao. 2021. “Comparison of single-pier settlement model and multi-pier settlement model in solving train-track-bridge interaction.” Veh. Syst. Dyn. 59 (10): 1484–1508. https://doi.org/10.1080/00423114.2020.1763406.
Chen, Z., and W. Zhai. 2020. “Theoretical method of determining pier settlement limit value for China’s high-speed railway bridges considering complete factors.” Eng. Struct. 209 (Apr): 109998. https://doi.org/10.1016/j.engstruct.2019.109998.
CR (China State Railway Group Co. Ltd). 2016. Observation and evaluation specification for settlement deformation of railway engineering. Q/CR 9230—2016. Beijing: CR.
Garg, H. 2016. “A hybrid PSO-GA algorithm for constrained optimization problems.” Appl. Math. Comput. 274 (Feb): 292–305. https://doi.org/10.1016/j.amc.2015.11.001.
Gong, X., and Z. Li. 2017. “A robust weighted total least-squares solution with Lagrange multipliers.” Surv. Rev. 49 (354): 176–185. https://doi.org/10.1080/00396265.2016.1150088.
Gong, X., and Z. Li. 2018. “Bridge pier settlement prediction in high-speed railway via autoregressive model based on robust weighted total least-squares.” Surv. Rev. 50 (359): 147–154. https://doi.org/10.1080/00396265.2016.1236162.
He, J., D. W. Wanik, B. M. Hartman, E. N. Anagnostou, M. Astitha, and M. E. Frediani. 2017. “Nonparametric tree-based predictive modeling of storm outages on an electric distribution network.” Risk Anal. 37 (3): 441–458. https://doi.org/10.1111/risa.12652.
He, Y., D. Li, Z. Pan, G. Ma, L. Yu, H. Yuan, and J. Le. 2020. “Dynamic modeling of weld bead geometry features in thick plate GMAW based on machine vision and learning.” Sensors 20 (24): 7104. https://doi.org/10.3390/s20247104.
Huang, Y., and Z. He. 2020. “Carbon price forecasting with optimization prediction method based on unstructured combination.” Sci. Total Environ. 725 (Jul): 138350. https://doi.org/10.1016/j.scitotenv.2020.138350.
Ke, H., S. Ai, B. Yan, C. Zhou, Z. Wang, Y. Yang, T. Liu, J. An, and Y. Chen. 2022. “Iceberg-induced tsunamis Near Dålk Glacier, Antarctica.” J. Surv. Eng. 148 (1): 04021027. https://doi.org/10.1061/(ASCE)SU.1943-5428.0000385.
Li, B., W. Yang, and X. Li. 2018a. “Application of combined model with DGM(1,1) and linear regression in grain yield prediction.” Grey Syst. Theory Appl. 8 (1): 25–34. https://doi.org/10.1108/GS-07-2017-0020.
Li, S., X. Yang, and R. Li. 2018b. “Forecasting China’s coal power installed capacity: A comparison of MGM, ARIMA, GM-ARIMA, and NMGM models.” Sustainability 10 (2): 506. https://doi.org/10.3390/su10020506.
Li, W., Y. Wei, D. An, Y. Jiao, and Q. Wei. 2022. “LSTM-TCN: Dissolved oxygen prediction in aquaculture, based on combined model of long short-term memory network and temporal convolutional network.” Environ. Sci. Pollut. Res. 29 (26): 39545–39556. https://doi.org/10.1007/s11356-022-18914-8.
Ma, Q., H. Wang, P. Luo, Y. Peng, and Q. Li. 2022. “Ultra-short-term railway traction load prediction based on DWT-TCN-PSO_SVR combined model.” Int. J. Electr. Power Energy Syst. 135 (Feb): 107595. https://doi.org/10.1016/j.ijepes.2021.107595.
Osada, E., K. Karsznia, and I. Karsznia. 2019. “Method of optimal fitting of existing lower-class leveling control networks to modernized national higher-class networks.” J. Surv. Eng. 145 (2): 04019003. https://doi.org/10.1061/(ASCE)SU.1943-5428.0000273.
Tang, L., and Y. Lu. 2020. “Study of the grey Verhulst model based on the weighted least square method.” Physica A 545 (May): 123615. https://doi.org/10.1016/j.physa.2019.123615.
Wang, F., Y. Li, F. Liao, and H. Yan. 2020a. “An ensemble learning based prediction strategy for dynamic multi-objective optimization.” Appl. Soft Comput. 96 (Nov): 106592. https://doi.org/10.1016/j.asoc.2020.106592.
Wang, J., Y. Wang, Z. Li, H. Li, and H. Yang. 2020b. “A combined framework based on data preprocessing, neural networks and multi-tracker optimizer for wind speed prediction.” Sustainable Energy Technol. Assess. 40 (Aug): 100757. https://doi.org/10.1016/j.seta.2020.100757.
Wang, J., W. Yan, Q. Zhang, and L. Chen. 2021. “Enhancement of computational efficiency for weighted total least squares.” J. Surv. Eng. 147 (4): 04021019. https://doi.org/10.1061/(ASCE)SU.1943-5428.0000373.
Wang, L., and R. Xu. 2021. “Multistart Nelder–Mead neural network algorithm for earthquake source parameter inversion of 2017 Bodrum–Kos earthquake.” J. Surv. Eng. 147 (3): 04021014. https://doi.org/10.1061/(ASCE)SU.1943-5428.0000368.
Wang, L., and C. Zou. 2021. “Accuracy estimation of earthquake source geometry parameters by the Sterling interpolation method in nonlinear inversion.” J. Surv. Eng. 147 (1): 04020019. https://doi.org/10.1061/(ASCE)SU.1943-5428.0000331.
Wei, Q., M. Chen, and C. Y. Ruan. 2021. “Research and development investment combination forecasting model of high-tech enterprises based on uncertain information.” Math. Probl. Eng. 2021 (Jan): 1–8. https://doi.org/10.1155/2021/6684711.
Wu, F., R. Jing, X. P. Zhang, F. Wang, and Y. Bao. 2021. “A combined method of improved grey BP neural network and MEEMD-ARIMA for day-ahead wave energy forecast.” IEEE Trans. Sustainable Energy 12 (4): 2404–2412. https://doi.org/10.1109/TSTE.2021.3096554.
Xiao, C., W. Xia, and J. Jiang. 2020. “Stock price forecast based on combined model of ARI-MA-LS-SVM.” Neural Comput. Appl. 32 (10): 5379–5388. https://doi.org/10.1007/s00521-019-04698-5.
Xie, Y., M. Jin, Z. Zou, G. Xu, D. Feng, W. Liu, and D. Long. 2020. “Real-time prediction of Docker container resource load based on a hybrid model of ARIMA and triple exponential smoothing.” IEEE Trans. Cloud Comput. 2020 (Apr): 1–17. https://doi.org/10.1109/TCC.2020.2989631.
Yang, Z., and J. Wang. 2018. “A combination forecasting approach applied in multistep wind speed forecasting based on a data processing strategy and an optimized artificial intelligence algorithm.” Appl. Energy 230 (Nov): 1108–1125. https://doi.org/10.1016/j.apenergy.2018.09.037.
Yu, F., S. Li, Z. Dai, J. Li, and S. Chen. 2020. “Stability control of staged filling construction on soft subsoil using hyperbolic settlement prediction method: A case study of a tidal flat in China.” Adv. Civ. Eng. 2020 (Aug): 1–11. https://doi.org/10.1155/2020/8899843.
Zhang, K., H. M. Lyu, S. L. Shen, A. Zhou, and Z. Y. Yin. 2020. “Evolutionary hybrid neural network approach to predict shield tunneling-induced ground settlements.” Tunnelling Underground Space Technol. 106 (Dec): 103594. https://doi.org/10.1016/j.tust.2020.103594.
Zhang, N., A. Zhou, Y. Pan, and S. L. Shen. 2021. “Measurement and prediction of tunnelling-induced ground settlement in karst region by using expanding deep learning method.” Measurement 183 (Oct): 109700. https://doi.org/10.1016/j.measurement.2021.109700.
Zhou, Q., Q. Hu, M. Ai, C. Xiong, and H. Jin. 2020a. “An improved GM (1, 3) model combining terrain factors and neural network error correction for urban land subsidence prediction.” Geomatics Nat. Hazards Risk 11 (1): 212–229. https://doi.org/10.1080/19475705.2020.1716860.
Zhou, Q., C. Wang, and G. Zhang. 2020b. “A combined forecasting system based on modified multi-objective optimization and sub-model selection strategy for short-term wind speed.” Appl. Soft Comput. 94 (Sep): 106463. https://doi.org/10.1016/j.asoc.2020.106463.
Zhu, Z., X. Song, G. Li, Z. Xu, S. Zhu, X. Yao, and S. Jing. 2021. “Prediction of the settling velocity of the rod-shaped proppant in vertical fracture using artificial neural network.” J. Pet. Sci. Eng. 200 (May): 108158. https://doi.org/10.1016/j.petrol.2020.108158.
Information & Authors
Information
Published In
Journal of Surveying Engineering
Volume 149 • Issue 2 • May 2023
Copyright
© 2023 American Society of Civil Engineers.
History
Received: May 31, 2022
Accepted: Dec 16, 2022
Published online: Feb 13, 2023
Published in print: May 1, 2023
Discussion open until: Jul 13, 2023
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.