Elevation error prediction of continuous beam cantilever construction phase based on LS-SVM

• Autorzy: Zheng Xilong , Hu Yunfeng , Sun Baitao

Identyfikatory

DOI

10.24425/ace.2025.154129

• Języki publikacji: EN

Abstrakty

EN

According to the construction of long-link continuous beam bridge, I choose field measured data as research samples and establish an elevation error prediction model by dealing samples, selecting the kernel function, selecting parameters, training, and predicting. I compare the Least Squares-Support Vector Machines (LS-SVM) prediction value with the measured value, the SVM model predictions, the BP neural network model predictions and the dimensionality reduction model predictions, so that predict elevation errors during cantilever construction phases by established models. According to the results of the comparison, the elevation error prediction model is highly accurate and has more high efficiency, good stability, and strong learning ability. Under the verification of the elevation control results in the cantilever stage, LS-SVM elevation error prediction is used for controlling the elevation of the bridge and solves the problem-predictive control successfully which is caused for few beam blocks in the cantilever phase of acontinuous girder bridge.

• Wydawca: Komitet Inżynierii Lądowej i Wodnej PAN ; Politechnika Warszawska, Wydział Inżynierii Lądowej
• Czasopismo: Archives of Civil Engineering
• Rocznik: 2025
• Tom: Vol. 71, nr 2
• Strony: 413--427
• Opis fizyczny: Bibliogr. 15 poz., il., tab.

Twórcy

autor

Zheng Xilong

• Key Laboratory of Earthquake Engineering and Engineering Vibration, Institute of Engineering Mechanics, China Earthquake Administration, Harbin, China

• https://orcid.org/0009-0001-7945-1562

autor

Hu Yunfeng

• Engineering Department, Ningbo Traffic Engineering Construction Group Co., LTD, Ningbo, China

• https://orcid.org/0009-0001-2509-5910

autor

Sun Baitao

• Key Laboratory of Earthquake Engineering and Engineering Vibration, Institute of Engineering Mechanics, China Earthquake Administration,Harbin, China

• https://orcid.org/0009-0002-2548-194X

Bibliografia

• [1] H. Bingtao, W. Qiusheng, and Q. Yunpeng, “Prediction model of local scour depth of bridge piers based on LS-SVM”, Journal of Civil Engineering and Urban Planning, vol. 5, no. 4, pp. 88-97, 2023, doi: 10.23977/jceup.2023.050410.
• [2] C. Min and L. Wenjie, “Cracking control technique for continuous steel-concrete composite girders under negative bending moment”, Archives of Civil Engineering, vol. 69, no. 3, pp. 239-251, 2023, doi: 10.24425/ace.2023.146078.
• [3] Z. Jingang, J. Hongyu, Z. Yulin, et al., “Combination of LS-SVM algorithm and JC method for fragility analysis of deep-water high piers subjected to near-field ground motions”, Structures, vol. 24, pp. 282-295, 2020, doi: 10.1016/j.istruc.2020.01.025.
• [4] Z. Xu, Y. Li, C. Rizos, and X. Xu, “Novel hybrid of LS-SVM and Kalman filter for GPS/INS integration”, The Journal of Navigation, vol. 63, no. 2, pp. 289-299, 2010, doi: 10.1017/S0373463309990361.
• [5] C. Juisheng and P. Anh Duc, “Nature-inspired metaheuristic optimization in least squares support vector regression for obtaining bridge scour information”, Information Sciences, vol. 399, pp. 64-80, 2017, doi: 10.1016/j.ins.2017.02.051.
• [6] L. Kuowei and C. Dirong, “Restoration of the distorted color to detect the discoloration status of a steel bridge coating using digital image measurements”, Advanced Engineering Informatics, vol. 33, pp. 96-111, 2017, doi: 10.1016/j.aei.2017.04.005.
• [7] C. Xiyuan, X. Yuan, L. Qinghua, T. Jian, and S. Chong, “Improving ultrasonic-based seamless navigation for indoor mobile robots utilizing EKF and LS-SVM”, Measurement, vol. 92, pp. 243-251, 2016, doi: 10.1016/j.measurement.2016.06.025.
• [8] G. Yuefang, S. Xin, H. Zexin, et al., “Extended compressed tracking via random projection based on MSERs and online LS-SVM learning”, Pattern Recognition, vol. 59, pp. 245-254, 2016, doi: 10.1016/j.patcog.2016.02.012.
• [9] D. Prayogo, Y. Tadeus, and T. Susanto, “Optimizing the prediction accuracy of friction capacity of driven piles in cohesive soil using a novel self-tuning least squares support vector machine”, Advances in Civil Engineering, vol. 2018, 2018, doi: 10.1155/2018/6490169.
• [10] P. Kemal and S. Günes, “Detection of ECG Arrhythmia using a differential expert system approach based on principal component analysis and least square support vector machine”, Applied Mathematics and Computation, vol. 186, no. 1, pp. 898-906, 2007, doi: 10.1016/j.amc.2006.08.020.
• [11] Y. Xiaolin, Z. Hengbin, and Y. Quansheng, “A least square support vector machine approach based on uniform design method for structural reliability analysis”, Advanced Materials Research, vol. 163-167, pp. 3348-3353, 2011, doi: 10.4028/www.scientific.net/AMR.163-167.3348.
• [12] Z. Cheng, D, Lieyun, Z. Ying, et al., “Hybrid support vector machine optimization model for prediction of energy consumption of cutter head drives in shield tunneling”, Journal of Computing in Civil Engineering, vol. 33, no. 3, 2019.
• [13] L. Xinjiang, F. Bi, and H. Minghui, “A novel LS-SVM modeling method for a hydraulic press forging process with multiple localized solutions”, IEEE Transactions on Industrial Informatics, vol. 11, no. 3. pp. 663-670, 2015, doi: 10.1109/TII.2015.2422614.
• [14] R. Langone, C. Alzate, B.D. Ketelaere, et al., “LS-SVM based spectral clustering and regression for predicting maintenance of industrial machines”, Engineering Applications of Artificial Intelligence, vol. 37, pp. 268-278, 2015, doi: 10.1016/j.engappai.2014.09.008.
• [15] D.A. Sachindra, F. Huang, A. Barton, and B.J.C. Perera, “Least square support vector and multi-linear regression for statistically downscaling general circulation model outputs to catchment streamflows”, International Journal of Climatology, vol. 33, no. 5, pp. 1087-1106, 2013, doi: 10.1002/joc.3493.