Probabilistic Framework with Bayesian Optimization for Predicting Typhoon-Induced Dynamic Responses of a Long-Span Bridge
Publication: Journal of Structural Engineering
Volume 147, Issue 1
Abstract
The long-span bridge, characterized by slenderness and flexibility, is particularly sensitive to wind action. Extreme wind events, including typhoons and hurricanes, will threaten the safety and serviceability of long-span bridges. More specifically, long-span bridges usually experience significant vibrations under typhoon events, increasing the risk of serviceability failure and traffic accidents. An efficient way to mitigate the risk of such threats is to predict the typhoon-induced response (TIR). Traditionally, TIR prediction of long-span bridges is usually carried out based on finite-element (FE) simulations. Due to the assumptions in formulating the FE model and establishing the wind load (e.g., boundary conditions, simplified structural elements, stationary aerodynamic wind forces), prediction accuracy is inevitably undermined. In this work, as opposed to traditional FE-based analysis, TIR is predicted in real time from a data-driven perspective. Quantile random forest (QRF) with Bayesian optimization is presented as the data-driven method for probabilistic prediction. A long-span cable-stayed bridge with a main span of is used as a test bed to illustrate the effectiveness of the present method. Other optimization algorithms used with QRF (grid search and random search) and response surface methodology (RSM) are also implemented for comparison purposes. Results indicate that QRF with Bayesian optimization can provide reliable probabilistic estimations allowing for quantification of uncertainty in prediction. It shows superior performance compared with other optimization algorithms and models in terms of accuracy and computational expense. The analysis and predictive framework for TIR are expected to provide insight into data-driven structural wind engineering.
Get full access to this article
View all available purchase options and get full access to this article.
Data Availability Statement
Data analyzed during the study were provided by a third party. Requests for data should be directed to the provider indicated in the Acknowledgements.
Acknowledgments
The authors gratefully acknowledge support from the National Natural Science Foundation of China (Grant Nos. 51722804 and 51978155), the National Ten Thousand Talent Program for Young Top-Notch Talents (Grant No. W03070080), the Jiangsu Provincial Key Research and Development Program (Grant No. BE2018120), and the Jiangsu Health Monitoring Data Center for Long-Span Bridges. The first author would also like to acknowledge the support of the China Scholarship Council (201906090073) and the Scientific Research Foundation of the Graduate School of Southeast University (YBPY 2017).
References
Alauddin, M., M. El Baradie, and M. Hashmi. 1997. “Prediction of tool life in end milling by response surface methodology.” J. Mater. Process. Technol. 71 (3): 456–465. https://doi.org/10.1016/S0924-0136(97)00111-8.
Bendat, J. S., and A. G. Piersol. 2011. Random data: Analysis and measurement procedures. New York: Wiley.
Breiman, L. 2001. “Random forests.” Mach. Learn. 45 (1): 5–32. https://doi.org/10.1023/A:1010933404324.
Brochu, E., V. M. Cora, and N. De Freitas. 2010. “A tutorial on Bayesian optimization of expensive cost functions, with application to active user modeling and hierarchical reinforcement learning.” Preprint, submitted December 12, 2010. https://arxiv.org/abs/1012.2599.
Cai, C., and S. Chen. 2004. “Framework of vehicle–bridge–wind dynamic analysis.” J. Wind Eng. Ind. Aerodyn. 92 (7–8): 579–607. https://doi.org/10.1016/j.jweia.2004.03.007.
Chen, J., M. C. Hui, and Y. Xu. 2007. “A comparative study of stationary and non-stationary wind models using field measurements.” Boundary Layer Meteorol. 122 (1): 105–121. https://doi.org/10.1007/s10546-006-9085-1.
Chen, S., and C. Cai. 2003. “Evolution of long-span bridge response to wind-numerical simulation and discussion.” Comput. Struct. 81 (21): 2055–2066. https://doi.org/10.1016/S0045-7949(03)00261-X.
Chen, S., and F. Chen. 2010. “Simulation-based assessment of vehicle safety behavior under hazardous driving conditions.” J. Transp. Eng. 136 (4): 304–315. https://doi.org/10.1061/(ASCE)TE.1943-5436.0000093.
Chen, X., and A. Kareem. 2001. “Nonlinear response analysis of long-span bridges under turbulent winds.” J. Wind Eng. Ind. Aerodyn. 89 (14–15): 1335–1350. https://doi.org/10.1016/S0167-6105(01)00147-7.
Chen, X., A. Kareem, and M. Matsumoto. 2001. “Multimode coupled flutter and buffeting analysis of long span bridges.” J. Wind Eng. Ind. Aerodyn. 89 (7–8): 649–664. https://doi.org/10.1016/S0167-6105(01)00064-2.
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.
Fenerci, A., and O. Øiseth. 2018a. “Site-specific data-driven probabilistic wind field modeling for the wind-induced response prediction of cable-supported bridges.” J. Wind Eng. Ind. Aerodyn. 181 (Oct): 161–179. https://doi.org/10.1016/j.jweia.2018.09.002.
Fenerci, A., and O. Øiseth. 2018b. “Strong wind characteristics and dynamic response of a long-span suspension bridge during a storm.” J. Wind Eng. Ind. Aerodyn. 172 (Jan): 116–138. https://doi.org/10.1016/j.jweia.2017.10.030.
Fenerci, A., O. Øiseth, and A. Rønnquist. 2017. “Long-term monitoring of wind field characteristics and dynamic response of a long-span suspension bridge in complex terrain.” Eng. Struct. 147 (Sep): 269–284. https://doi.org/10.1016/j.engstruct.2017.05.070.
Guo, W., and Y. Xu. 2006. “Safety analysis of moving road vehicles on a long bridge under crosswind.” J. Eng. Mech. 132 (4): 438–446. https://doi.org/10.1061/(ASCE)0733-9399(2006)132:4(438).
Gyamerah, S. A., P. Ngare, and D. Ikpe. 2020. “Probabilistic forecasting of crop yields via quantile random forest and Epanechnikov kernel function.” Agric. For. Meteorol. 280 (Jan): 107808. https://doi.org/10.1016/j.agrformet.2019.107808.
Haque, A. U., M. H. Nehrir, and P. Mandal. 2014. “A hybrid intelligent model for deterministic and quantile regression approach for probabilistic wind power forecasting.” IEEE Trans. Power Syst. 29 (4): 1663–1672. https://doi.org/10.1109/TPWRS.2014.2299801.
He, Y., R. Liu, H. Li, S. Wang, and X. Lu. 2017. “Short-term power load probability density forecasting method using kernel-based support vector quantile regression and copula theory.” Appl. Energy 185 (Jan): 254–266. https://doi.org/10.1016/j.apenergy.2016.10.079.
Li, Q., X. Li, Y. He, and J. Yi. 2017. “Observation of wind fields over different terrains and wind effects on a super-tall building during a severe typhoon and verification of wind tunnel predictions.” J. Wind Eng. Ind. Aerodyn. 162 (Mar): 73–84. https://doi.org/10.1016/j.jweia.2017.01.008.
Li, S., S. Laima, and H. Li. 2018. “Data-driven modeling of vortex-induced vibration of a long-span suspension bridge using decision tree learning and support vector regression.” J. Wind Eng. Ind. Aerodyn. 172 (Jan): 196–211. https://doi.org/10.1016/j.jweia.2017.10.022.
Li, Z., T. H. Chan, and J. M. Ko. 2002. “Evaluation of typhoon induced fatigue damage for Tsing Ma Bridge.” Eng. Struct. 24 (8): 1035–1047. https://doi.org/10.1016/S0141-0296(02)00031-7.
Mao, J. X., H. Wang, D. M. Feng, T. Y. Tao, and W. Z. Zheng. 2018. “Investigation of dynamic properties of long-span cable-stayed bridges based on one-year monitoring data under normal operating condition.” Struct. Control Health Monit. 25 (5): e2146. https://doi.org/10.1002/stc.2146.
Mao, J. X., H. Wang, Y. G. Fu, and B. F. Spencer Jr. 2019. “Automated modal identification using principal component and cluster analysis: Application to a long-span cable-stayed bridge.” Struct. Control Health Monit. 26 (10): e2430. https://doi.org/10.1002/stc.2430.
Meinshausen, N. 2006. “Quantile regression forests.” J. Mach. Learn. Res. 7 (Jun): 983–999.
Mostafaei, M., H. Javadikia, and L. Naderloo. 2016. “Modeling the effects of ultrasound power and reactor dimension on the biodiesel production yield: Comparison of prediction abilities between response surface methodology (RSM) and adaptive neuro-fuzzy inference system (ANFIS).” Energy 115 (Nov): 626–636. https://doi.org/10.1016/j.energy.2016.09.028.
Mouloodi, S., H. Rahmanpanah, C. Burvill, and H. M. Davies. 2020. “Prediction of load in a long bone using an artificial neural network prediction algorithm.” J. Mech. Behav. Biomed. Mater. 102 (Feb): 103527. https://doi.org/10.1016/j.jmbbm.2019.103527.
Roy, A., R. Manna, and S. Chakraborty. 2019. “Support vector regression based metamodeling for structural reliability analysis.” Probab. Eng. Mech. 55 (Jan): 78–89. https://doi.org/10.1016/j.probengmech.2018.11.001.
Sameen, M. I., B. Pradhan, and S. Lee. 2019. “Self-learning random forests model for mapping groundwater yield in data-scarce areas.” Nat. Resour. Res. 28 (3): 757–775. https://doi.org/10.1007/s11053-018-9416-1.
Sano, S., T. Kadowaki, K. Tsuda, and S. Kimura. 2019. “Application of Bayesian optimization for pharmaceutical product development.” J. Pharm. Innovation 15 (3): 333–343. https://doi.org/10.1007/s12247-019-09382-8.
Shahriari, B., K. Swersky, Z. Wang, R. P. Adams, and N. De Freitas. 2015. “Taking the human out of the loop: A review of Bayesian optimization.” Proc. IEEE 104 (1): 148–175. https://doi.org/10.1109/JPROC.2015.2494218.
Stoppiglia, H., G. Dreyfus, R. Dubois, and Y. Oussar. 2003. “Ranking a random feature for variable and feature selection.” J. Mach. Learn. Res. 3 (Mar): 1399–1414.
Su, Y., and M. Li. 2019. “Integrated transfer function for buffeting response evaluation of long-span bridges.” J. Wind Eng. Ind. Aerodyn. 189 (Jun): 231–242. https://doi.org/10.1016/j.jweia.2019.03.025.
Swersky, K., J. Snoek, and R. P. Adams. 2014. “Freeze-thaw Bayesian optimization.” Preprint, submitted June 16, 2014. https://arxiv.org/abs/1406.3896.
Tao, T., H. Wang, and T. Wu. 2017. “Comparative study of the wind characteristics of a strong wind event based on stationary and nonstationary models.” J. Struct. Eng. 143 (5): 04016230 . https://doi.org/10.1061/(ASCE)ST.1943-541X.0001725.
Tuv, E., A. Borisov, G. Runger, and K. Torkkola. 2009. “Feature selection with ensembles, artificial variables, and redundancy elimination.” J. Mach. Lear. Res. 10: 1341–1366.
Vaysse, K., and P. Lagacherie. 2017. “Using quantile regression forest to estimate uncertainty of digital soil mapping products.” Geoderma 291 (Apr): 55–64. https://doi.org/10.1016/j.geoderma.2016.12.017.
Wan, H.-P., and Y.-Q. Ni. 2018. “Bayesian modeling approach for forecast of structural stress response using structural health monitoring data.” J. Struct. Eng. 144 (9): 04018130. https://doi.org/10.1061/(ASCE)ST.1943-541X.0002085.
Wan, H.-P., and Y.-Q. Ni. 2019. “Bayesian multi-task learning methodology for reconstruction of structural health monitoring data.” Struct. Health Monit. 18 (4): 1282–1309. https://doi.org/10.1177/1475921718794953.
Wan, H.-P., and Y.-Q. Ni. 2020. “A new approach for interval dynamic analysis of train-bridge system based on Bayesian optimization.” J. Eng. Mech. 146 (5): 04020029. https://doi.org/10.1061/(ASCE)EM.1943-7889.0001735.
Wang, H., R. Hu, J. Xie, T. Tong, and A. Li. 2013a. “Comparative study on buffeting performance of Sutong Bridge based on design and measured spectrum.” J. Bridge Eng. 18 (7): 587–600. https://doi.org/10.1061/(ASCE)BE.1943-5592.0000394.
Wang, H., A. Li, and R. Hu. 2011. “Comparison of ambient vibration response of the Runyang Suspension Bridge under skew winds with time-domain numerical predictions.” J. Bridge Eng. 16 (4): 513–526. https://doi.org/10.1061/(ASCE)BE.1943-5592.0000168.
Wang, H., A. Li, J. Niu, Z. Zong, and J. Li. 2013b. “Long-term monitoring of wind characteristics at Sutong Bridge site.” J. Wind Eng. Ind. Aerodyn. 115 (Apr): 39–47. https://doi.org/10.1016/j.jweia.2013.01.006.
Wang, H., J.-X. Mao, and B. F. Spencer Jr. 2019a. “A monitoring-based approach for evaluating dynamic responses of riding vehicle on long-span bridge under strong winds.” Eng. Struct. 189 (Jun): 35–47. https://doi.org/10.1016/j.engstruct.2019.03.075.
Wang, H., J.-X. Mao, and Z.-D. Xu. 2020a. “Investigation of dynamic properties of a long-span cable-stayed bridge during typhoon events based on structural health monitoring.” J. Wind Eng. Ind. Aerodyn. 201 (Jun): 104172. https://doi.org/10.1016/j.jweia.2020.104172.
Wang, H., T. Tao, Y. Gao, and F. Xu. 2018. “Measurement of wind effects on a kilometer-level cable-stayed bridge during Typhoon Haikui.” J. Struct. Eng. 144 (9): 04018142. https://doi.org/10.1061/(ASCE)ST.1943-541X.0002138.
Wang, H., T. Tao, A. Li, and Y. Zhang. 2016a. “Structural health monitoring system for Sutong cable-stayed bridge.” Smart Struct. Syst. 18 (2): 317–334. https://doi.org/10.12989/sss.2016.18.2.317.
Wang, H., T. Wu, T. Tao, A. Li, and A. Kareem. 2016b. “Measurements and analysis of non-stationary wind characteristics at Sutong Bridge in Typhoon Damrey.” J. Wind Eng. Ind. Aerodyn. 151 (Apr): 100–106. https://doi.org/10.1016/j.jweia.2016.02.001.
Wang, H., Y.-M. Zhang, J.-X. Mao, and H.-P. Wan. 2020b. “A probabilistic approach for short-term prediction of wind gust speed using ensemble learning.” J. Wind Eng. Ind. Aerodyn. 202 (Jul): 104198. https://doi.org/10.1016/j.jweia.2020.104198.
Wang, H., Y.-M. Zhang, J.-X. Mao, H.-P. Wan, T.-Y. Tao, and Q.-X. Zhu. 2019b. “Modeling and forecasting of temperature-induced strain of a long-span bridge using an improved Bayesian dynamic linear model.” Eng. Struct. 192 (Aug): 220–232. https://doi.org/10.1016/j.engstruct.2019.05.006.
Xu, S., R. Ma, D. Wang, A. Chen, and H. Tian. 2019. “Prediction analysis of vortex-induced vibration of long-span suspension bridge based on monitoring data.” J. Wind Eng. Ind. Aerodyn. 191 (Aug): 312–324. https://doi.org/10.1016/j.jweia.2019.06.016.
Xu, Y., and L. Zhu. 2005. “Buffeting response of long-span cable-supported bridges under skew winds. Part 2: Case study.” J. Sound Vib. 281 (3–5): 675–697. https://doi.org/10.1016/j.jsv.2004.01.025.
Xu, Y. L., and Y. Xia. 2011. Structural health monitoring of long-span suspension bridges. Boca Raton, FL: CRC Press.
Yuchi, W., E. Gombojav, B. Boldbaatar, J. Galsuren, S. Enkhmaa, B. Beejin, G. Naidan, C. Ochir, B. Legtseg, and T. Byambaa. 2019. “Evaluation of random forest regression and multiple linear regression for predicting indoor fine particulate matter concentrations in a highly polluted city.” Environ. Pollut. 245 (Feb): 746–753. https://doi.org/10.1016/j.envpol.2018.11.034.
Zhang, J., G. Ma, Y. Huang, F. Aslani, and B. Nener. 2019. “Modelling uniaxial compressive strength of lightweight self-compacting concrete using random forest regression.” Constr. Build. Mater. 210 (Jun): 713–719. https://doi.org/10.1016/j.conbuildmat.2019.03.189.
Zhang, Y., K. Liu, L. Qin, and X. An. 2016. “Deterministic and probabilistic interval prediction for short-term wind power generation based on variational mode decomposition and machine learning methods.” Energy Convers. Manage. 112 (Mar): 208–219. https://doi.org/10.1016/j.enconman.2016.01.023.
Zhou, Y., S. Li, C. Zhou, and H. Luo. 2019. “Intelligent approach based on random forest for safety risk prediction of deep foundation pit in subway stations.” J. Comput. Civ. Eng. 33 (1): 05018004. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000796.
Zhou, Y., and L. Sun. 2018. “Effects of high winds on a long-span sea-crossing bridge based on structural health monitoring.” J. Wind Eng. Ind. Aerodyn. 174 (Mar): 260–268. https://doi.org/10.1016/j.jweia.2018.01.001.
Information & Authors
Information
Published In
Journal of Structural Engineering
Volume 147 • Issue 1 • January 2021
Copyright
© 2020 American Society of Civil Engineers.
History
Received: Mar 26, 2020
Accepted: Aug 19, 2020
Published online: Oct 22, 2020
Published in print: Jan 1, 2021
Discussion open until: Mar 22, 2021
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.