Journal of Jilin University(Engineering and Technology Edition) ›› 2020, Vol. 50 ›› Issue (2): 557-564.doi: 10.13229/j.cnki.jdxbgxb20190082

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Urban bridge performance decay model based on survival analysis

Yu FANG(),Li-jun SUN()   

  1. The Key Laboratory of Road and Traffic Engineering, Ministry of Education, Tongji University, Shanghai 201804, China
  • Received:2019-01-18 Online:2020-03-01 Published:2020-03-08
  • Contact: Li-jun SUN E-mail:1610049@tongji.edu.cn;ljsun@tongji.edu.cn

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Abstract:

By using bridge inspection records in Shanghai urban bridge management system, this paper established a probabilistic prediction model of bridge performance deterioration based on survival analysis. At the same time, Weibull distribution functions are used to describe the performance decay behavior of various bridge components in different condition states. The results show that the current urban bridges in Shanghai have a relatively short duration at condition rating A and B, and the decay speed of the deck system and superstructure is higher than the substructure. Applied a practical example of network-level bridge decay prediction from 2016 to 2018, the differences between the regression analysis, Markov chain and survival analysis method are compared. It is verified that the survival analysis model has higher prediction accuracy.

Key words: road engineering, bridge performance, prediction model, survival analysis, Weibull-distribution

CLC Number: 

  • U418

Table 1

Classification of urban bridge intact state"

等级状态BCI范围养护对策
A级完好[90,100]日常养护
B级良好[80,90)保养小修
C级合格[66,80)针对性小修或中修工程
D级不合格[50,66)检测评估后进行中修、大修或加固工程
E级危险[0,50)检测评估后进行大修、加固、或改建工程

Fig.1

BCI state change trend in Shanghai city"

Fig.2

Duration of bridge performance decay process"

Table 2

Inspection records in BMS database since 2004"

年份桥梁数量检测记录数有效记录数
20041 3901 091652
20051 5501 357839
20061 6011 447936
20071 6441 4861 035
20081 7481 6081 151
20091 8041 5901 193
20101 8991 6861 267
20111 9721 7271 331
20121 9111 7561 458
20131 9821 8011 569
20142 1771 9921 709
20152 3052 0271 820

Table 3

Estimated parameters of Weibull distribution"

等级部位βγ
A全桥1.272 114.121 6
桥面系1.193 713.176 1
上部结构1.281 814.421 2
下部结构1.244 314.222 4
B全桥1.205 715.901 6
桥面系1.371 215.833 9
上部结构1.084 415.118 5
下部结构1.367 521.437 8
C全桥1.211 022.841 9
桥面系1.379 519.291 7
上部结构1.116 315.407 2
下部结构1.166 721.839 4
D全桥1.251 622.158 4
桥面系1.574 923.934 5
上部结构1.385 227.225 3
下部结构1.391 229.411 2

Fig.3

Survival function for different intact states"

Table 4

Mean durations at different intact state"

状态等级全桥桥面系上部结构下部结构
均值累计均值累计均值累计均值累计
A1313121213131313
B1528152714272033
C2149184514422053
D2170226725672781

Table 5

Proportion of urban bridges at different intact state"

年份状态等级
ABCDE
201569.6024.365.560.490.00
201649.7541.557.441.170.09
201752.4039.617.150.800.04
201852.2441.275.820.590.08

Table 6

Model prediction results and relative errors"

年份模型A级B级C级
预测误差预测误差预测误差
2016回归51.43316.316121.32187
马氏链42.351529.822819.71165
生存57.741631.01259.3326
2017回归46.341213.736528.22295
马氏链37.692829.012722.29212
生存56.57831.63209.7036
2018回归38.472620.845028.99398
马氏链33.543627.94324.37319
生存55.28632.282210.1474
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