基于动力特性和改进粒子群优化算法的桥梁冲刷深度识别

doi:10.13229/j.cnki.jdxbgxb.20230145

Abstract

Abstract:

A bridge scour identification method based on dynamic characteristics of bridge structure and improved particle swarm optimization algorithm was proposed， in order to identify the position and degree of bridge scour damage quickly and accurately. The dynamic characteristics of the bridge structure and the static displacement of the pier top are taken as the scour identification parameters of the bridge in this method， and the quantitative correlation between the scour identification parameters and the scour depth of different piers was obtained by using multiple nonlinear regression. Therefore， the uneven scour depth of the pier can be identified according to the dynamic characteristics obtained during the actual detection of the bridge. Finally， the numerical simulation analysis of a simply-supported beam bridge was carried out， and the concrete application steps of the proposed bridge scour identification method based on the improved particle swarm optimization algorithm were demonstrated. The results show that the proposed method can identify the bridge scour location and scour depth， which has a certain accuracy. In practical engineering， scour damage identification of bridge foundation can be realized by using this method through conventional bridge detection， which avoids operational difficulties brought by underwater operation， and can evaluate the scour state quickly and accurately.

Key words: bridge engineering, dynamic characteristics, identification method, improved particle swarm optimization algorithm, depth of scour

CLC Number:

U446

Guo-jin TAN,Qing-wen KONG,Xin HE,Pan ZHANG,Run-chao YANG,Yang-jun CHAO,Zhong YANG. Bridge scour depth identification based on dynamic characteristics and improved particle swarm optimization algorithm[J].Journal of Jilin University(Engineering and Technology Edition), 2023, 53(6): 1592-1600.

Figures/Tables 12

Fig.1

Fig.2

Fig.3

Fig.4

Table 1

Numerical analysis results of simple-supported girder bridge under 66 cases"

工况编号	h₁/m	h₂/m	c₂/c₁	φ₂/φ₁	f/Hz	工况编号	h₁/m	h₂/m	c₂/c₁	φ₂/φ₁	f/Hz
1	0.00	0.00	1.000 000	1.000 000	3.818 8	34	1.50	3.00	1.149 892	1.002 767	3.406 3
2	0.00	0.50	1.016 779	1.000 270	3.796 5	35	1.50	3.50	1.213 091	1.003 849	3.332 2
3	0.00	1.00	1.045 389	1.000 758	3.758 3	36	1.50	4.00	1.281 185	1.004 947	3.257 4
4	0.00	1.50	1.083 446	1.001 426	3.708 2	37	1.50	4.50	1.351 814	1.006 014	3.185 1
5	0.00	2.00	1.129 859	1.002 242	3.648 7	38	1.50	5.00	1.421 486	1.006 999	3.118 6
6	0.00	2.50	1.184 128	1.003 183	3.581 5	39	2.00	2.00	1.000 000	1.000 000	3.485 2
7	0.00	3.00	1.245 846	1.004 218	3.508 6	40	2.00	2.50	1.048 032	1.000 930	3.421 1
8	0.00	3.50	1.314 318	1.005 317	3.432 2	41	2.00	3.00	1.102 656	1.001 949	3.351 6
9	0.00	4.00	1.388 095	1.006 434	3.355 0	42	2.00	3.50	1.163 259	1.003 026	3.278 8
10	0.00	4.50	1.464 617	1.007 521	3.2803	43	2.00	4.00	1.228 556	1.004 116	3.205 4
11	0.00	5.00	1.540 103	1.008 526	3.211 5	44	2.00	4.50	1.296 283	1.005 177	3.134 4
12	0.50	0.50	1.000 000	1.000 000	3.774 3	45	2.00	5.00	1.363 094	1.006 153	3.069 2
13	0.50	1.00	1.028 138	1.000 487	3.736 3	46	2.50	2.50	1.000 000	1.000 000	3.358 2
14	0.50	1.50	1.065 566	1.001 153	3.686 4	47	2.50	3.00	1.052 120	1.001 017	3.290 1
15	0.50	2.00	1.111 213	1.001 969	3.627 1	48	2.50	3.50	1.109 946	1.002 089	3.218 9
16	0.50	2.50	1.164 587	1.002 907	3.560 3	49	2.50	4.00	1.172 250	1.003 175	3.147 1
17	0.50	3.00	1.225 286	1.003 940	3.487 8	50	2.50	4.50	1.236 873	1.004 227	3.077 7
18	0.50	3.50	1.292 629	1.005 035	3.411 9	51	2.50	5.00	1.300 622	1.005 197	3.013 8
19	0.50	4.00	1.365 188	1.006 146	3.335 1	52	3.00	3.00	1.000 000	1.000 000	3.223 7
20	0.50	4.50	1.440 447	1.007 229	3.260 9	53	3.00	3.50	1.054 961	1.001 069	3.154 2
21	0.50	5.00	1.514 688	1.008 228	3.192 5	54	3.00	4.00	1.114 179	1.002 151	3.084 1
22	1.00	1.00	1.000 000	1.000 000	3.698 6	55	3.00	4.50	1.175 600	1.003 198	3.016 3
23	1.00	1.50	1.036 404	1.000 665	3.649 1	56	3.00	5.00	1.236 191	1.004 162	2.953 9
24	1.00	2.00	1.080 802	1.001 479	3.590 4	57	3.50	3.50	1.000 000	1.000 000	3.086 5
25	1.00	2.50	1.132 715	1.002 414	3.524 2	58	3.50	4.00	1.056 133	1.001 079	3.018 2
26	1.00	3.00	1.191 753	1.003 441	3.452 5	59	3.50	4.50	1.114 355	1.002 121	2.952 1
27	1.00	3.50	1.257 252	1.004 530	3.377 3	60	3.50	5.00	1.171 789	1.003 080	2.891 4
28	1.00	4.00	1.327 826	1.005 635	3.301 4	61	4.00	4.00	1.000 000	1.000 000	2.951 7
29	1.00	4.50	1.401 025	1.006 710	3.228 0	62	4.00	4.50	1.055 127	1.001 041	2.887 4
30	1.00	5.00	1.473 234	1.007 703	3.160 4	63	4.00	5.00	1.109 509	1.001 996	2.828 3
31	1.50	1.50	1.000 000	1.000 000	3.600 3	64	4.50	4.50	1.000 000	1.000 000	2.824 9
32	1.50	2.00	1.042 838	1.000 811	3.542 3	65	4.50	5.00	1.051 540	1.000 953	2.767 3
33	1.50	2.50	1.092 928	1.001 744	3.477 0	66	5.00	5.00	1.000 000	1.000 000	2.711 2

Table 1

Table 2

Fig.5

Fig.6

Fig.7

Table 3

Fig.8

Table 4

References 18

1	熊文, 魏乐永, 张学峰, 等. 大跨度缆索支承桥梁基础冲刷动力识别方法[J]. 哈尔滨工业大学学报, 2019, 51(3): 92-98.
	Xiong Wen, Wei Le-yong, Zhang Xue-feng, et al. Dynamic-based bridge scour identification of super-span cable-supported bridges[J]. Journal of Harbin Institute of Technology, 2019, 51(3): 92-98.
2	Lu J Y, Shi Z Z, Hong J H, et al. Temporal variation of scour depth at nonuniform cylindrical piers[J]. Journal of Hydraulic Engineering, 2011, 137(1): 45-56.
3	Bao T, Liu Z, Bird K. Influence of soil characteristics on natural frequency-based bridge scour detection[J]. Journal of Sound and Vibration, 2019, 446(1): 195-210.
4	张佰战, 李付军. 桥墩局部冲刷计算研究[J]. 中国铁道科学, 2004, 25(2): 48-51.
	Zhang Bai-zhan, Li Fu-jun. Bridge pier local scour calculation[J]. China Railway Science, 2004, 25(2): 48-51.
5	詹义正, 王军, 谈广鸣, 等. 桥墩局部冲刷的试验研究[J]. 武汉大学学报, 2006, 39(6): 3-4.
	Zhan Yi-zheng, Wang Jun, Tan Guang-ming, et al. Experimental study on bridge pier local scour[J]. Engineering Journal of Wuhan University, 2006, 39(6): 3-4.
6	Hong J H, Guo W D, Chiew Y M, et al. A new practical method to simulate flood-induced bridge pier scour—a case study of mingchu bridge piers on the cho-shui river[J]. Water, 2016, 8(6): 238.
7	Deng L, Cai C S. Bridge scour: prediction, modeling, monitoring and countermeasures-review[J]. Practice Periodical on Structural Design & Construction, 2010, 15(2): 125-134.
8	Zounemat-Kermani A, Beheshti A, Ataie-Ashtiani B. Estimation of current-induced scour depth around pile groups using neural network and adaptive neuro-fuzzy inference system[J]. Applied Soft Computing, 2009, 9(2): 746-755.
9	熊文, 张愉, 李飞泉, 等. 基于时频分析与神经网络的桥梁冲刷动力评估[J]. 天津大学学报:自然科学与工程技术版, 2020, 53(4): 397-404.
	Xiong Wen, Zhang Yu, Li Fei-quan, et al. Dynamic assessment of bridge scour based on time-frequency analysis and neural network[J]. Journal of Tianjin University(Science and Technology), 2020, 53(4): 397-404.
10	张效忠, 孙延华, 孙国民. 基于支持向量机的桥梁冲刷深度识别方法研究[J]. 水利与建筑工程学报, 2016, 14(3): 206-210.
	Zhang Xiao-zhong, Sun Yan-hua, Sun Guo-min. Identification method of scour depth for bridge based on ERA and SVM[J]. Journal of Water Resources and Architectural Engineering, 2016, 14(3): 206-210.
11	Ju S H. Determination of scoured bridge natural frequencies with soil-structure interaction[J]. Soil Dynamics and Earthquake Engineering, 2013, 55: 247-254.
12	Kong X, Cai C S. Scour effect on bridge and vehicle responses under bridge-vehicle-wave interaction[J]. Journal of Bridge Engineering, 2016, 21(4): No.04015083.
13	熊文, 邹晨, 叶见曙. 基于动力特性识别的桥墩冲刷状态分析[J]. 中国公路学报, 2017, 30(5): 89-96.
	Xiong Wen, Zou Chen, Ye Jian-shu. Condition assessment of bridge scour by tracing dynamic performances of bridges[J]. China Journal of Highway and Transport, 2017, 30(5): 89-96.
14	于繁华, 刘寒冰. 基于支持向量机和粒子群算法的结构损伤识别[J]. 吉林大学学报: 工学版, 2008, 38(2): 434-439.
	Yu Fan-hua, Liu Han-bing. Structural damage identification by support vector machine and particle swarm algorithm[J]. Journal of Jilin University (Engineering and Technology Edition), 2008, 38(2): 434-439.
15	王学武, 严益鑫, 丁冬雁, 等. 基于Lévy-PSO算法的焊接机器人避障路径规划[J]. 上海交通大学学报, 2016, 50(10): 1517-1520.
	Wang Xue-wu, Yan Yi-xin, Ding Dong-yan, et al. Collision free path planning for welding robot based on Lévy-PSO[J]. Journal of Shanghai Jiaotong University, 2016, 50(10): 1517-1520.
16	孟宪猛, 蔡翠翠. 基于精英反向学习和Lévy飞行的鲸鱼优化算法[J]. 电子测量技术, 2021, 44(20): 82-87.
	Meng Xian-meng, Cai Cui-cui. Whale optimization algorithm based on elite reverse learning and Lévy flight[J]. Electronic Measurement Technology, 2021, 44(20): 82-87.
17	API. Recommended Practice for Planning, Designing and Constructing Fixed Offshore Platforms-Working Stress Design[M]. 20th ed. Washington DC: API, 2000.
18	Meidani K, Mirjalili S, Barati F A, et al. Grey Wolf Optimizer[J]. Advances in Engineering Software, 2014, 69: 46-61.

Related Articles 15

[1]	Hui JIANG,Xin LI,Xiao-yu BAI. Review on development of bridge seismic structural systems： from ductility to resilience [J]. Journal of Jilin University(Engineering and Technology Edition), 2023, 53(6): 1550-1565.
[2]	Yue ZHANG,Chuan-sen LIU,Fei SONG. Influence of abutment back wall on continuous girder bridge's seismic fragility [J]. Journal of Jilin University(Engineering and Technology Edition), 2023, 53(5): 1372-1380.
[3]	Shu-wei LAN,Dong-hua ZHOU,Xu CHEN,Nan-ming MO. Practical calculation method for the critical bearing capacity of double column bridge with high piers [J]. Journal of Jilin University(Engineering and Technology Edition), 2023, 53(4): 1105-1111.
[4]	Qi-kai SUN,Nan ZHANG,Xiao LIU,Zi-ji ZHOU. Dynamic reduction coefficients of steel⁃concrete composite beam based on Timoshenko beam theory [J]. Journal of Jilin University(Engineering and Technology Edition), 2023, 53(2): 488-495.
[5]	Rang-lin FAN,Zi-jian LIU,Fu-liang MA,Quan-fa WU. Mid⁃high⁃frequency active and passive dynamic characteristics of active hydraulic mounts with oscillating coil actuator [J]. Journal of Jilin University(Engineering and Technology Edition), 2023, 53(1): 50-60.
[6]	Hua-wen YE,Zhi-chao DUAN,Ji-lin LIU,Yu ZHOU,Bing HAN. Wheel⁃load diffusion effect on orthotropic steel⁃concrete composite bridge deck [J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(8): 1808-1816.
[7]	Li-feng WANG,Zi-wang XIAO,Sai-sai YU. New risk analysis method based on Bayesian network for hanging basker system of multi-tower cable-stayed bridge [J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(4): 865-873.
[8]	Yan-ling ZHANG,Can WANG,Xu ZHANG,Ang-yang WANG,Yun-sheng LI. Human⁃induced vibration analysis and pedestrian comfort evaluation for suspension footbridge with different hunger systems [J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(11): 2644-2652.
[9]	Chang-jun ZHONG,Zhong-bin WANG,Chen-yang LIU. Influencing factors and structural optimization of main cable saddle bearing capacity of suspension bridge [J]. Journal of Jilin University(Engineering and Technology Edition), 2021, 51(6): 2068-2078.
[10]	Wei CHEN,Tian-bao WAN,Zhong-bin WANG,Xuan LI,Rui-li SHEN. Design and performance of internal air supply conduit for dehumidification in main cables of suspension bridges [J]. Journal of Jilin University(Engineering and Technology Edition), 2021, 51(5): 1749-1755.
[11]	Shu-lun GUO,Tie-yi ZHONG,Zhi-gang YAN. Calculation method of buffeting response for stay cables of long⁃span cable⁃stayed bridge [J]. Journal of Jilin University(Engineering and Technology Edition), 2021, 51(5): 1756-1762.
[12]	Kai GAO,Gang LIU. Effective strength improvement of global critical strength branch and bound method [J]. Journal of Jilin University(Engineering and Technology Edition), 2021, 51(2): 597-603.
[13]	Xiao-tao LI,Jin-peng REN,Xiao-xu LI,Hang YANG. Mechanism of a linear actuator driven by dual piezoelectric oscillators in inverse mode [J]. Journal of Jilin University(Engineering and Technology Edition), 2021, 51(2): 468-471.
[14]	Ya-feng GONG,Jia-xiang SONG,Guo-jin TAN,Hai-peng BI,Yang LIU,Cheng-xin SHAN. Multi⁃vehicle bridge weigh⁃in⁃motion algorithm [J]. Journal of Jilin University(Engineering and Technology Edition), 2021, 51(2): 583-596.
[15]	Qing-wen KONG,Guo-jin TAN,Long-lin WANG,Yong WANG,Zhi-gang WEI,Han-bing LIU. Analysis of free vibration characteristics of cracked box girder bridge based on finite element method [J]. Journal of Jilin University(Engineering and Technology Edition), 2021, 51(1): 225-232.

Metrics

Viewed

Full text

Abstract

Cited

Shared

Discussed

Comments

Recommended 0

No Suggested Reading articles found!

系数	拟合值	系数	拟合值
t₁	0.006 197	p₁	0.000 004
t₂	0.074 700	p₂	-0.000 010
t₃	-0.060 807	p₃	-0.000 042
t₄	0.214 000	p₄	0.005 620
t₅	0.041 420	p₅	0.000 009
t₆	-2.044 000	p₆	0.030 260
t₇	-0.114 580	p₇	0.000 122
t₈	5.578 000	p₈	0.975 290

算法	工况11		工况30
算法	适应度	收敛次数	适应度	收敛次数
GWO	1.669 7e^-5	73	0.001 303 4	61
PSO	0.005 063 5	70	4.440 9e^-16	92
L-PSO	4.440 9e^-16	51	2.220 4e^-16	75

Bridge scour depth identification based on dynamic characteristics and improved particle swarm optimization algorithm

RICH HTML

PDF (PC)