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

吉林大学学报(工学版) ›› 2023, Vol. 53 ›› Issue (6): 1592-1600.doi: 10.13229/j.cnki.jdxbgxb.20230145

• 交通运输工程·土木工程 • 上一篇

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

谭国金¹(),孔庆雯¹,何昕¹(),张攀²,杨润超³,朝阳军⁴,杨忠⁴

^1.吉林大学交通学院，长春 130022
^2.长春市建设工程安全监督站，长春 130012
^3.吉林省公路管理局，长春 130021
^4.吉林省交通规划设计院，长春 130021

收稿日期:2023-02-28 出版日期:2023-06-01 发布日期:2023-07-23
通讯作者: 何昕 E-mail:tgj@jlu.edu.cn;hexin@jlu.edu.cn
作者简介:谭国金（1981-），男，教授，博士.研究方向：桥梁智能检测与评定.E-mail：tgj@jlu.edu.cn
基金资助:
国家重点研发计划项目(2021YFB2600604);国家自然科学基金面上项目(51978309);黑龙江省交通运输厅科技项目(2021-1);吉林省交通运输科技计划项目(2021-01-02)

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

Guo-jin TAN¹(),Qing-wen KONG¹,Xin HE¹(),Pan ZHANG²,Run-chao YANG³,Yang-jun CHAO⁴,Zhong YANG⁴

^1.College of Transportation，Jilin University，Changchun 130022，China
^2.Changchun Construction Project Safety Supervision Station，Changchun 130012，China
^3.Jilin Provincial Highway Administration，Changchun 130021，China
^4.Jilin Traffic Planning and Design Institute，Changchun 130021，China

Received:2023-02-28 Online:2023-06-01 Published:2023-07-23
Contact: Xin HE E-mail:tgj@jlu.edu.cn;hexin@jlu.edu.cn

摘要/Abstract

摘要：

为了快速、准确地识别出桥梁冲刷损伤的位置和程度，提出了一种基于桥梁结构动力特性和改进粒子群优化算法的桥梁冲刷识别方法。该方法先将桥梁结构动力特性与单位力作用下墩顶静力位移作为桥梁冲刷识别参数；然后，利用多元非线性回归得到冲刷识别参数与不同桥墩冲刷深度之间的定量关联，根据桥梁实测时获取的动力特性值便可识别出桥墩左、右两侧不均匀冲刷深度；最后，对一座简支梁桥进行数值分析，演示了本文基于改进粒子群优化算法的桥梁冲刷识别方法的具体应用步骤。研究结果表明：本文方法可识别出桥梁冲刷位置和冲刷深度，且识别结果具有一定的准确性。在实际工程中，通过常规桥梁检测即可实现桥梁基础冲刷损伤识别，避免了水下作业带来的操作难题，且可快速对桥梁冲刷状态进行准确评估。

关键词: 桥梁工程, 动力特性, 识别方法, 改进粒子群优化算法, 冲刷深度

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

中图分类号:

U446

谭国金,孔庆雯,何昕,张攀,杨润超,朝阳军,杨忠. 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[J]. 吉林大学学报(工学版), 2023, 53(6): 1592-1600.

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.

图/表 12

图1

图2

图3

图4

表1

简支梁桥66组工况数值分析结果"

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

表1

表2

图5

图6

图7

表3

图8

表4

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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

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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

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

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工况	理论值		L-PSO 预测值			工况	理论值		L-PSO 预测值
工况	h₁/m	h₂/m	h₁/m	h₂/m		工况	h₁/m	h₂/m	h₁/m	h₂/m
1	0.0	0.0	0.00		0.20	34	1.5	3.0	1.50	3.01
2	0.0	0.5	0.01		0.56	35	1.5	3.5	1.50	3.54
3	0.0	1.0	0.05		0.92	36	1.5	4.0	1.51	4.04
4	0.0	1.5	0.07		1.36	37	1.5	4.5	1.51	4.49
5	0.0	2.0	0.07		1.87	38	1.5	5.0	1.52	4.91
6	0.0	2.5	0.05		2.39	39	2.0	2.0	2.00	1.83
7	0.0	3.0	0.03		2.92	40	2.0	2.5	2.00	2.42
8	0.0	3.5	0.00		3.43	41	2.0	3.0	2.00	3.00
9	0.0	4.0	0.00		3.92	42	2.0	3.5	2.00	3.54
10	0.0	4.5	0.00		4.44	43	2.0	4.0	2.00	4.04
11	0.0	5.0	0.00		4.98	44	2.0	4.5	2.01	4.49
12	0.5	0.5	0.46		0.59	45	2.0	5.0	2.01	4.88
13	0.5	1.0	0.49		0.95	46	2.5	2.5	2.51	2.35
14	0.5	1.5	0.50		1.40	47	2.5	3.0	2.50	2.95
15	0.5	2.0	0.51		1.91	48	2.5	3.5	2.50	3.53
16	0.5	2.5	0.51		2.44	49	2.5	4.0	2.50	4.04
17	0.5	3.0	0.50		2.97	50	2.5	4.5	2.50	4.49
18	0.5	3.5	0.49		3.50	51	2.5	5.0	2.50	4.87
19	0.5	4.0	0.49		4.01	52	3.0	3.0	3.01	2.88
20	0.5	4.5	0.49		4.50	53	3.0	3.5	3.00	3.49
21	0.5	5.0	0.51		5.00	54	3.0	4.0	3.00	4.02
22	1.0	1.0	0.98		0.94	55	3.0	4.5	3.00	4.49
23	1.0	1.5	1.00		1.40	56	3.0	5.0	3.00	4.87
24	1.0	2.0	1.00		1.92	57	3.5	3.5	3.51	3.42
25	1.0	2.5	1.01		2.47	58	3.5	4.0	3.50	3.99
26	1.0	3.0	1.00		3.01	59	3.5	4.5	3.50	4.49
27	1.0	3.5	1.00		3.53	60	3.5	5.0	3.50	4.89
28	1.0	4.0	1.00		4.03	61	4.0	4.0	4.01	3.94
29	1.0	4.5	1.01		4.50	62	4.0	4.5	4.01	4.47
30	1.0	5.0	1.02		4.96	63	4.0	5.0	4.00	4.89
31	1.5	1.5	1.50		1.36	64	4.5	4.5	4.51	4.43
32	1.5	2.0	1.50		1.90	65	4.5	5.0	4.50	4.89
33	1.5	2.5	1.50		2.46	66	5.0	5.0	5.01	4.87