基于机器学习的中小跨径公路梁桥抗震设计与评价方法展望

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摘要 This study summarized the application field of machine learning to explore the basic seismic thinking of machine learning methods used for bridges. The development and actuality of bridge seismic analysis theories and technology were briefly reviewed, particularly in relation to the field of civil engineering. This study introduced the concept of machine learning, summarized its key factors and current software platforms, and illustrated the common methods and representative algorithms of machine learning using simple examples. First, the normal types of simply supported and continuous girder bridges, which are used for highway bridges with small and medium spans in China, were summarized. The earthquake damage phenomena had been observed for bridge types during the Wenchuan earthquake. Data from these phenomena were assessed, including the damage grade divisions of piers, bearings, shear keys, and abutments. Second, a series of seismic performance tests had been conducted by international and domestic academics for bearings, shear keys, piers, and abutments. These tests were summarized in this study to obtain the constitutive relationships for seismic analysis and determine the seismic design parameters of bridge components (including foundations). Finally, an overall analysis methodology based on machine learning was introduced into the bridge seismic analysis. This methodology explained that machine learning for bridge seismic tasks had two aspects. The first was the collection of considerable bridge design data and set up data sets, and the second was data reduction, including raw data processing and debugging or developing a reasonable machine learning algorithm model. This study also discussed the shortcomings of existing performance-based probabilistic seismic design and evaluation methods that are currently used in analyzing bridges in China. Results indicated the potential future major concerns for bridge seismic analysis technology. The establishment of computational simulations based on artificial intelligence method was also recommended. In addition, the mutual integration between disciplines and mutual communication among different professionals were advocated in this study.

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	鲁冠亚
	王克海
	张盼盼

关键词 ： bridge engineering, seismic design and evaluation, review, small-to-medium-span girder bridge, machine learning

Abstract：This study summarized the application field of machine learning to explore the basic seismic thinking of machine learning methods used for bridges. The development and actuality of bridge seismic analysis theories and technology were briefly reviewed, particularly in relation to the field of civil engineering. This study introduced the concept of machine learning, summarized its key factors and current software platforms, and illustrated the common methods and representative algorithms of machine learning using simple examples. First, the normal types of simply supported and continuous girder bridges, which are used for highway bridges with small and medium spans in China, were summarized. The earthquake damage phenomena had been observed for bridge types during the Wenchuan earthquake. Data from these phenomena were assessed, including the damage grade divisions of piers, bearings, shear keys, and abutments. Second, a series of seismic performance tests had been conducted by international and domestic academics for bearings, shear keys, piers, and abutments. These tests were summarized in this study to obtain the constitutive relationships for seismic analysis and determine the seismic design parameters of bridge components (including foundations). Finally, an overall analysis methodology based on machine learning was introduced into the bridge seismic analysis. This methodology explained that machine learning for bridge seismic tasks had two aspects. The first was the collection of considerable bridge design data and set up data sets, and the second was data reduction, including raw data processing and debugging or developing a reasonable machine learning algorithm model. This study also discussed the shortcomings of existing performance-based probabilistic seismic design and evaluation methods that are currently used in analyzing bridges in China. Results indicated the potential future major concerns for bridge seismic analysis technology. The establishment of computational simulations based on artificial intelligence method was also recommended. In addition, the mutual integration between disciplines and mutual communication among different professionals were advocated in this study.

Key words： bridge engineering seismic design and evaluation review small-to-medium-span girder bridge machine learning

收稿日期: 2019-01-11

基金资助:Supported by Highway Engineering Industry Standard Project, MOT (No.JTG-C-201012) and Basic Scientific Research Service Project of Central-level Public Welfare Research Institute (No.2016-9018).

通讯作者: LU Guan-ya E-mail: lookuanya@126.com

引用本文:

鲁冠亚, 王克海, 张盼盼. 基于机器学习的中小跨径公路梁桥抗震设计与评价方法展望[J]. Journal of Highway and Transportation Research and Development, 2019, 13(1): 24-37.
LU Guan-ya, WANG Ke-hai, ZHANG Pan-pan. Seismic Design and Evaluation Methods for Small-to-Medium-Span Highway Girder Bridges Based on Machine Learning and Earthquake Damage Experience. Journal of Highway and Transportation Research and Development, 2019, 13(1): 24-37.

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[1] PANG H, WANG C. Deep Learning Model for Diabetic Retinopathy Detection[J]. Journal of Software, 2017, 28(11):3018-3029. (in Chinese)
[2] LESMANA I P D, PURNAMA I K E, PURNOMO M H. Abnormal Condition Detection of Pancreatic Beta Cells as the Cause of Diabetes Mellitus Based on Iris Image[C]//2011 International Conference on Instrumentation, Communication, Information Technology and Biomedical Engineering, Bandung, Indonesia:IEEE, 2011:135-141.
[3] ZHAO Jian-ming, LI Chun-hui, YAO Nian-min. Classification of SonCi Style Using Machine Learning Algorithms[J]. Computer Engineering and Applications, 2018, 54(1):186-190. (in Chinese)
[4] SHEN Min, YANG Xin-ya, WANG Kai. Research on User Preference Retrieval System of University Library Based on Machine Learning[J]. Library and Information Science, 2015, 59(11):143-148. (in Chinese)
[5] GUO Lin, Zhou Ji-biao, DONG Sheng, et al. Analysis of Urban Road Traffic Accidents Based on Improved K-means Algorithm[J]. China Journal of Highway and Transport, 2018, 31(4):270-279. (in Chinese)
[6] GREGORIADES A, MOUSKOS K C. Black Spots Identification through a Bayesian Networks Quantification of Accident Risk Index[J]. Transportation Research Part C:Emerging Technologies, 2013, 28:28-43.
[7] GOU Cheng-cheng, QIN Yu-jun, TIAN Tian, et al. Social Messages Outbreak Prediction Model Based on Recurrent Neural Network[J]. Journal of Software, 2017, 28(11):3030-3042. (in Chinese)
[8] KONONENKO I. Machine Learning for Medical Diagnosis:History, State of the Art and Perspective[J]. Artificial Intelligence in Medicine, 2001, 23(1):89-109.
[9] LUCIANI D, MARCHESI M, BERTOLINI G. The Role of Bayesian Networks in the Diagnosis of Pulmonary Embolism[J]. Journal of Thrombosis and Haemostasis, 2003, 1(4):698-707.
[10] SHA Ai-min, TONG Zheng, GAO Jie. Recognition and Measurement of Pavement Disasters Based on Convolution Neutral Networks[J]. China Journal of Highway and Transport, 2018, 31(1):1-10. (in Chinese)
[11] WORDEN K, MANSON G. The Application of Machine Learning to Structural Health Monitoring[J]. Philosophical Transactions, 2007, 365(1851):515-537.
[12] NAEEJ M, BALI M, NAEEJ MR, et al. Prediction of Lateral Confinement Coefficient in Reinforced Concrete Columns Using M5' Machine Learning Method[J]. KSCE Journal of Civil Engineering, 2013, 17(7):1714-1719.
[13] SADOWSKI L, HOLA, J. Neural Prediction of the Pull-off Adhesion of the Concrete Layers in Floors on the Basis of Nondestructive Tests[J]. Procedia Engineering, 2013, 57(3):986-995.
[14] CHOU J S, TSAI C F, PHAM A D, et al. Machine Learning in Concrete Strength Simulations:Multi-nation Data Analytics[J]. Construction and Building Materials, 2014, 73:771-780.
[15] VANLUCHENE R D, SUN R. Neural Networks in Structural Engineering[J]. Microcomputers in Civil Engineering, 1990, 5(3):207-215.
[16] HUNG S L, JAN J C. MS_CMAC Neural Network Learning Model in Structural Engineering[J]. Journal of Computing in Civil Engineering, 1999, 13:1(1):1-11.
[17] KICINGER R, ARCISZEWSKI T, DE JONG K. Evolutionary Computation and Structural Design:A Survey of the State-of-the-art[J]. Computers and Structures, 2005, 83(23-24):1943-1978.
[18] KICINGER R, ARCISZEWSKI T, DE JONG K. Evolutionary Design of Steel Structures in Tall Buildings[J]. Journal of Computing in Civil Engineering, 2005, 19:3(223):223-238.
[19] JOOTOO A, LATTANZI D. Bridge Type Classification:Supervised Learning on a Modified NBI Data Set[J]. Journal of Computing in Civil Engineering, 2017, 31(6):04017063, 1-11.
[20] CHEN Yu-ren, FU Yun-tian, WANG Fan. Establishment and Application of Slight Distance Computing Model Based on Support Vector Regression[J]. China Journal of Highway and Transport, 2018, 31(4):105-113. (in Chinese)
[21] ABDULHAI B, PRINGLE R, KARAKOULAS G J. Reinforcement Learning for True Adaptive Traffic Signal Control[J]. Journal of Transportation Engineering, 2003, 129:3(278):278-285.
[22] CHIEN S I J, DING Y, WEI C. Dynamic Bus Arrival Time Prediction with Artificial Neural Networks[J]. Journal of Transportation Engineering, 2002, 128:429-438.
[23] WANG Jue, SHI Chun-yi. Investigations on Machine Learning[J]. Journal of Guangxi Normal University:Natural Science Edition, 2003, 21(2):1-15. (in Chinese)
[24] ZHOU Zhi-hua. Machine Learning[M]. Beijing:Tsinghua University Press, 2016. (in Chinese)
[25] JIAO Jia-feng, LI Yun. Review of Typical Machine Learning Platforms for Big Data[J]. Journal of Computer Applications, 2017, 37(11):3039-3047, 3052. (in Chinese)
[26] TANG Zhen-kun. Design and Implementation of Machine Learning Platform Based on Spark[D]. Xiamen:Xiamen University, 2014. (in Chinese)
[27] CHEN Le-sheng. Report on Highways' Damage in the Wenchuan Earthquake[M]. Beijing:China Communications Press, 2012. (in Chinese)
[28] WANG Ke-hai, WEI Han, LI Qian, et al. Philosophies on Seismic Design of Highway Bridges of Small or Medium Spans[J]. China Civil Engineering Journal, 2012, 45(9):115-121. (in Chinese)
[29] WANG Ke-hai, LI Chong, LI Qian, et al. Seismic Design Method of Small and Medium Span Bridge Considering Bearing Friction Slipping[J]. Engineering Mechanics, 2014, 31(6):85-92. (in Chinese)
[30] LI Chong, WANG Ke-hai, LI Yue, et al. Experimental Study on Seismic Performance of Laminated Rubber Bearings with Friction Slipping[J]. Journal of Southeast University:Natural Science Edition, 2014, 44(1):162-167. (in Chinese)
[31] TANG Hu, LI Jian-zhong, SHAO Chang-yu. Seismic Performance of Small and Medium Span Girder Bridges with Plate Type Elastomeric Pad Bearings in the Transverse Direction[J]. China Journal of Highway and Transport, 2016, 29(3):55-65. (in Chinese)
[32] WU Gang, WANG Quan-lu, WANG Ke-hai, et al. Seismic Response Analysis of Bridges in Transverse Direction Considering the Mechanical Degradation of Bearings and Shear keys[J]. Journal of Vibration and Shock, 2018, 37(2):189-196. (in Chinese)
[33] LI Zhi-jun, GE Fei, XU Xiu-li, et al. Finite Element Simulation and Experimental Study of Property for Elastomeric Pad Bearing[J]. Journal of Southeast University:Natural Science Edition, 2013, 43(6):1299-1304. (in Chinese)
[34] XIANG N L, LI J Z. Experimental and Numerical Study on Seismic Sliding Mechanism of Laminated Rubber Bearings[J]. Engineering Structures, 2017, 141(6):159-174.
[35] ZHANG Guo-zhen, LU Zhi-hong, LIU Guang-yan. Displacement-based Design for Highway Bridges with Functional Bearing system[R]. Taiwan:National Center for Research on Earthquake Engineering, 2011.
[36] JOSHUA S, FAHNESTOCK L, FILIPOV T, et al. Shear and Friction Response of Nonseismic Laminated Elastomeric Bridge Bearings Subject to Seismic Demands[J]. Journal of Bridge Engineering, 2013, 18(7):612-623.
[37] KONSTANTINIDIS D, KELLY J M, MAKRIS N. Experimental Investigations on the Seismic Response of Bridge Bearings[R]. Berkeley:Earthquake Engineering Research Center, College of Engineering, University of California, 2008.
[38] WANG Yang, CAO Jia-liang, SHI Wei-xing. Shaking Table Test Study of Base-isolated Structure with Pot Bearings[J]. Building Structure, 2013, 43(7):9-13. (in Chinese)
[39] ZHANG Shi-chen, LI Guo-qing, ZHUANG Jun-sheng. Test Study and Design of QPZ Pot Bearings[J]. Railway Engineering, 1992(9):13-17. (in Chinese)
[40] ZHU Wen-jun. Researches on the Seismic Response of Bridge Pot Bearings[D]. Harbin:Institute of Engineering Mechanics, China Earthquake Administration, 2015. (in Chinese)
[41] JT/T 391-2009 Pot Bearings for Highway Bridges[S]. (in Chinese)
[42] JTG/T B02-01-2008, Guidelines for Seismic Design of Highway Bridges[S]. (in Chinese)
[43] MEGALLY S H, SILVA P F, SEIBLE F. Seismic Response of Sacrificial Shear Keys in Bridge Abutments[R]. San Diego:Structural Systems Research Project, University of California, 2001.
[44] XU Lue-qin, LI Jian-zhong. Experiment Seismic Performance and Its Improvement of Reinforced Concrete Retainers[J]. China Journal of Highway and Transport, 2014, 27(9):41-48. (in Chinese)
[45] XU Lue-qin, LI Jian-zhong. Effect of Retainers on Transverse Seismic Response of a Standard Continuous Girder Bridge[J]. Journal of Highway and Transportation Research and Development, 2013, 30(4):53-59. (in Chinese)
[46] XU Lue-qin, LI Jian-zhong. Design and Experimental Investigation of a New Type Sliding Retainer and Its Efficacy in Seismic Fortification[J]. Engineering Mechanics, 2016, 33(2):111-118. (in Chinese)
[47] PAULAY T, PRISTLEY M J N. Seismic Design of Reinforced Concrete and Masonry Buildings[M]. New York:Wiley-Inter Science, 1992.
[48] PRISTLEY M J N, PARK R. Strength and Ductility of Concrete Bridge Columns under Seismic Loading[J]. ACI Structural Journal Proceedings, 1987, 1(1):61-76.
[49] WATSON S, ZAHN F A, PARK R. Confining Reinforcement for Concrete Columns[J]. Journal of Structural Engineering, 1994, 120(6):1798-1824.
[50] WATSON S, PARK R. Simulated Seismic Load Tests on Reinforced Concrete Columns[J]. Journal of Structural Engineering, 1994, 120(6):1825-1849.
[51] FAN Li-chu, ZHUO Wei-dong. Ductile Seismic Design of Bridge[M]. Beijing:China Communications Press, 2001. (in Chinese)
[52] SUN Zhi-guo. Researches on the Seismic Deformation Capacity of RC Bridge Columns[D]. Harbin:Institute of Engineering Mechanics,China Earthquake Administration, 2012. (in Chinese)
[53] KENT D C, PARK R. Flexural Members with Confined Concrete[J]. Journal of the Structural Division, 1971, 97:1969-1990.
[54] SCOTT B D, PARK R, PRIESTLEY M J. Stress-Strain Behavior of Concrete by Overlapping Hoops at Low and High Strain Rates[J]. ACI Journal, 1982, 79(1):13-27.
[55] MANDER J B, PRIESTLEY M J, PARK R. Theoretical Stress-strain Model for Confined Concrete[J]. Journal of Structural Engineering, 1988, 114(8):1804-1826.
[56] Caltrans Seismic Design Criteria[S]. Version 1.6. Sacramento:California Department of Transportation, 2016.
[57] WANG Chang-feng, CHEN Xing-chong. Analytical Model and Experimental Study of Nonlinear Seismic Response of Bridge with Pile Foundations[J]. Bridge Construction, 2014, 44(3):57-62. (in Chinese)
[58] NIELSON B G. Analytical Fragility Curves for Highway Bridges in Moderate Seismic Zones[D]. Atlanta:Georgia Institute of Technology, 2005.
[59] RAMANATHAN KN. Next generation Seismic Fragility Curves for California Bridges Incorporating the Evolution in Seismic Design[D]. Atlanta:Georgia Institute of Technology, 2012.
[60] WANG Ke-hai, LI Chong, LI Yue. Problems in Chinese highway bridge seismic specifications and suggestion for improvement[J]. Journal of Architecture and Civil Engineering, 2013, 30(2):55-103. (in Chinese)
[61] SONG Shuai, QIAN Yong-jiu, WU Gang. Research on Seismic Fragility Method of Bridge System Based on Copula Function[J]. Engineering Mechanics, 2016, 33(11):193-200. (in Chinese)
[62] WANG Ke-hai. Bridge Seismic Research (2nd Edition)[M]. Beijing:China Railway Publishing House, 2015. (in Chinese)