Multiple damage detection in complex bridges based on strain energy extracted from single point measurement

doi:10.1007/s11709-020-0624-5

Front. Struct. Civ. Eng.

2020, Vol. 14

Issue (3) : 722-730 https://doi.org/10.1007/s11709-020-0624-5

RESEARCH ARTICLE

Multiple damage detection in complex bridges based on strain energy extracted from single point measurement

Alireza ARABHA NAJAFABADI¹, Farhad DANESHJOO²(

), Hamid Reza AHMADI³

¹. Department of Structural Engineering, Road, Housing & Urban Development Research Center, Tehran 13145-1696, Iran
². Department of Structural Engineering, Faculty of Civil and Environmental Engineering, Tarbiat Modares University, Tehran 14115-397, Iran
³. Department of Civil Engineering, Faculty of Engineering, University of Maragheh, Maragheh 55136-553, Iran

Download: PDF(650 KB) HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks

Abstract

Strain Energy of the structure can be changed with the damage at the damage location. The accurate detection of the damage location using this index in a force system is dependent on the degree of accuracy in determining the structure deformation function before and after damage. The use of modal-based methods to identify damage in complex bridges is always associated with problems due to the need to consider the effects of higher modes and the adverse effect of operational conditions on the extraction of structural modal parameters. In this paper, the deformation of the structure was determined by the concept of influence line using the Betti-Maxwell theory. Then two damage detection indicators were developed based on strain energy variations. These indices were presented separately for bending and torsion changes. Finite element analysis of a five-span concrete curved bridge was done to validate the stated methods. Damage was simulated by decreasing stiffness at different sections of the deck. The response regarding displacement of a point on the deck was measured along each span by passing a moving load on the bridge at very low speeds. Indicators of the strain energy extracted from displacement influence line and the strain energy extracted from the rotational displacement influence line (SERIL) were calculated for the studied bridge. The results show that the proposed methods have well identified the location of the damage by significantly reducing the number of sensors required to record the response. Also, the location of symmetric damages is detected with high resolution using SERIL.

Keywords damage detection strain energy influence line complex bridges rotation displacement

Corresponding Author(s): Farhad DANESHJOO

Just Accepted Date: 22 April 2020 Online First Date: 21 May 2020 Issue Date: 13 July 2020

Cite this article:

Alireza ARABHA NAJAFABADI,Farhad DANESHJOO,Hamid Reza AHMADI. Multiple damage detection in complex bridges based on strain energy extracted from single point measurement[J]. Front. Struct. Civ. Eng., 2020, 14(3): 722-730.

URL:

https://academic.hep.com.cn/fsce/EN/10.1007/s11709-020-0624-5
https://academic.hep.com.cn/fsce/EN/Y2020/V14/I3/722

Fig.1 3D finite element model of the bridge.

Fig.2 The geometric specification of the bridge’s central pier and deck.

K	piers foundation stiffness (T/M)&(T.M)	abutment foundation abutment (T/M)&(T.M)
$K x$	131817	222
$K y$	138066	141953
$K z$	133842	146160
$K x x$	2819508	0
$K y y$	6709205	0
$K z z$	5209602	0

Tab.1 The equivalent stiffness of Intermediate piers and abutment

Tab.2 Location and damage characteristics on the bridge deck

effective stiffness recommended by Caltrans		simulated damage
$(E I e f f) min ?$	$(E I e f f) max ?$	$h damaged$	$E I damaged$
$0.5 E I g$	$0.75 E I g$	0.8h	$0.5 E I g$
$0.5 E I g$	$0.75 E I g$	0.7h	$0.35 E I g$

Tab.3 The intensity of the simulated damage in accordance with the effective inertia of the deck

Fig.3 The defined damage location and the deck response record points in the model.

Fig.4 Detecting the damages in the bridge's deck using SEDIL index.

Fig.5 Detecting the damages in the bridge’s deck using SERIL index.

1	H R Ahmadi, F Daneshjoo, N Khaji. New damage indices and algorithm based on square time-frequency distribution for damage detection in concrete piers of railroad bridges. Structural Control and Health Monitoring, 2015, 22(1): 91–106 https://doi.org/10.1002/stc.1662
2	C Gentile, A Saisi. Continuous dynamic monitoring of a centenary iron bridge for structural modification assessment. Frontiers of Structural and Civil Engineering, 2015, 9(1): 26–41 https://doi.org/10.1007/s11709-014-0284-4
3	A Miyamoto, R Kiviluoma, A Yabe. Frontier of continuous structural health monitoring system for short & medium span bridges and condition assessment. Frontiers of Structural and Civil Engineering, 2018, 13(3): 569–604
4	H R Ahmadi, D Anvari. Health monitoring of pedestrian truss bridges using cone-shaped kernel distribution. Smart Structures and Systems, 2018, 22(6): 699–709
5	H R Ahmadi, D Anvari. New damage index based on least squares distance for damage diagnosis in steel girder of bridge’s deck. Structural Control and Health Monitoring, 2018, 25(10): e2232 https://doi.org/10.1002/stc.2232
6	J R Casas. European standardization of quality specifications for roadway bridges: An overview. In: Proceedings of the 8th International Conference on Bridge Maintenance, Safety and Management. Foz do Iguaçu, 2016
7	J C Matos, J R Casas, S Fernandes. COST Action TU 1406 quality specifications for roadway bridges (BridgeSpec). In: IABMAS Conference. CRC Press, 2016
8	T Vo-Duy, N Nguyen-Minh, H Dang-Trung, A Tran-Viet, T Nguyen-Thoi. Damage assessment of laminated composite beam structures using damage locating vector (DLV) method. Frontiers of Structural and Civil Engineering, 2015, 9(4): 457–465 https://doi.org/10.1007/s11709-015-0303-0
9	X Zhu, H Hao. Development of an integrated structural health monitoring system for bridge structures in operational conditions. Frontiers of Structural and Civil Engineering, 2012, 6(3): 321–333 https://doi.org/10.1007/s11709-012-0161-y
10	N G Pnevmatikos, G D Hatzigeorgiou. Damage detection of framed structures subjected to earthquake excitation using discrete wavelet analysis. Bulletin of Earthquake Engineering, 2017, 15(1): 227–248 https://doi.org/10.1007/s10518-016-9962-z
11	S W Doebling, C R Farrar, M B Prime. A summary review of vibration-based damage identification methods. Shock and Vibration Digest, 1998, 30(2): 91–105
12	E P Carden, P Fanning. Vibration based condition monitoring: A review. Structural Health Monitoring, 2004, 3(4): 355–377 https://doi.org/10.1177/1475921704047500
13	A Alvandi, C Cremona. Assessment of vibration-based damage identification techniques. Journal of Sound and Vibration, 2006, 292(1–2): 179–202 https://doi.org/10.1016/j.jsv.2005.07.036
14	Y J Yan, L Cheng, Z Y Wu, L H Yam. Development in vibration-based structural damage detection technique. Mechanical Systems and Signal Processing, 2007, 21(5): 2198–2211 https://doi.org/10.1016/j.ymssp.2006.10.002
15	A Gharighoran, F Daneshjoo, N Khaji. Use of Ritz method for damage detection of reinforced and post-tensioned concrete beams. Construction & Building Materials, 2009, 23(6): 2167–2176 https://doi.org/10.1016/j.conbuildmat.2008.12.017
16	Z Wu, G Liu, Z Zhang. Experimental study of structural damage identification based on modal parameters and decay ratio of acceleration signals. Frontiers of Architecture and Civil Engineering in China, 2011, 5(1): 112–120 https://doi.org/10.1007/s11709-010-0069-3
17	G Ghodrati Amiri, A Z Hosseinzadeh, A Bagheri, K Y Koo. Damage prognosis by means of modal residual force and static deflections obtained by modal flexibility based on the diagonalization method. Smart Materials and Structures, 2013, 22(7): 075032 https://doi.org/10.1088/0964-1726/22/7/075032
18	S H Sung, H J Jung. A new damage quantification approach for shear-wall buildings using ambient vibration data. Frontiers of Structural and Civil Engineering, 2015, 9(1): 17–25 https://doi.org/10.1007/s11709-014-0278-2
19	AA Najafabadi, F Daneshjoo, M Bayat. A novel index for damage detection of deck and dynamic behavior of horizontally curved bridges under moving load. Journal of Vibroengineering, 2017, 19(7): 5421–33
20	M Mohamadi Dehcheshmeh, G G Amiri, A Zare Hosseinzadeh, V Torbatinejad. Structural damage detection based on modal data using moth-flame optimization algorithm. Proceedings of the Institution of Civil Engineers, Structures and Buildings, 2019, 18: 1–43 https://doi.org/10.1680/jstbu.18.00121
21	I Talebinejad, C Fischer, F Ansari. Numerical evaluation of vibration-based methods for damage assessment of cable-stayed bridges. Computer-Aided Civil and Infrastructure Engineering, 2011, 26(3): 239–251 https://doi.org/10.1111/j.1467-8667.2010.00684.x
22	J M Ndambi, J Vantomme, K Harri. Damage assessment in reinforced concrete beams using eigenfrequencies and mode shape derivatives. Engineering Structures, 2002, 24(4): 501–515 https://doi.org/10.1016/S0141-0296(01)00117-1
23	P J Cruz, R Salgado. Performance of vibration-based damage detection methods in bridges. Computer-Aided Civil and Infrastructure Engineering, 2009, 24(1): 62–79 https://doi.org/10.1111/j.1467-8667.2008.00546.x
24	W Fan, P Qiao. Vibration-based damage identification methods: A review and comparative study. Structural Health Monitoring, 2011, 10(1): 83–111 https://doi.org/10.1177/1475921710365419
25	S S Nanthakumar, T Lahmer, X Zhuang, G Zi, T Rabczuk. Detection of material interfaces using a regularized level set method in piezoelectric structures. Inverse Problems in Science and Engineering, 2016, 24(1): 153–176 https://doi.org/10.1080/17415977.2015.1017485
26	C Anitescu, E Atroshchenko, N Alajlan, T Rabczuk. Artificial neural network methods for the solution of second order boundary value problems. Computers, Materials & Continua, 2019, 59(1): 345–359 https://doi.org/10.32604/cmc.2019.06641
27	H Guo, X Zhuang, T Rabczuk. A Deep Collocation Method for the Bending Analysis of Kirchhoff Plate. Computers, Materials & Continua, 2019, 59(2): 433–456 https://doi.org/10.32604/cmc.2019.06660
28	T Rabczuk, H Ren, X Zhuang. A nonlocal operator method for partial differential equations with application to electromagnetic waveguide problem. Computers, Materials & Continua, 2019, 59(1): 31–55 https://doi.org/10.32604/cmc.2019.04567
29	A Meixedo, R A Calçada, V Alves, D Ribeiro, A Cury. Damage identification of a railway bridge based on genetic algorithms. Maintenance, Monitoring, Safety, Risk and Resilience of Bridges and Bridge Networks, 2016, 17: 311
30	M P Limongelli. The interpolation damage detection method for frames under seismic excitation. Journal of Sound and Vibration, 2011, 330(22): 5474–5489 https://doi.org/10.1016/j.jsv.2011.06.012
31	M Dilena, M P Limongelli, A Morassi. Damage localization in bridges via the FRF interpolation method. Mechanical Systems and Signal Processing, 2015, 52–53: 162–180 https://doi.org/10.1016/j.ymssp.2014.08.014
32	N Vu-Bac, T X Duong, T Lahmer, X Zhuang, R A Sauer, H S Park, T Rabczuk. A NURBS-based inverse analysis for reconstruction of nonlinear deformations of thin shell structures. Computer Methods in Applied Mechanics and Engineering, 2018, 331: 427–455 https://doi.org/10.1016/j.cma.2017.09.034
33	C Anitescu, M N Hossain, T Rabczuk. Recovery-based error estimation and adaptivity using high-order splines over hierarchical T-meshes. Computer Methods in Applied Mechanics and Engineering, 2018, 328: 638–662 https://doi.org/10.1016/j.cma.2017.08.032
34	H Ghasemi, H S Park, T Rabczuk. A level-set based IGA formulation for topology optimization of flexoelectric materials. Computer Methods in Applied Mechanics and Engineering, 2017, 313: 239–258 https://doi.org/10.1016/j.cma.2016.09.029
35	H Ghasemi, H S Park, T Rabczuk. A multi-material level set-based topology optimization of flexoelectric composites. Computer Methods in Applied Mechanics and Engineering, 2018, 332: 47–62 https://doi.org/10.1016/j.cma.2017.12.005
36	T A Duffey, S W Doebling, C R Farrar, W E Baker, W H Rhee. Vibration-based damage identification in structures exhibiting axial and torsional response. Journal of Vibration and Acoustics, 2001, 123(1): 84–91 https://doi.org/10.1115/1.1320445
37	A Žnidarič, I Lavrič, J Kalin. Measurements of bridge dynamics with a bridge weigh-in-motion system. In: International Conference on Heavy Vehicles HVParis 2008: Weigh-In-Motion (ICWIM 5). Hoboken, NJ: John Wiley & Sons, Inc., 2009
38	C W Kim, M Kawatani, J Hao. Modal parameter identification of short span bridges under a moving vehicle by means of multivariate AR model. Structure and Infrastructure Engineering, 2012, 8(5): 459–472 https://doi.org/10.1080/15732479.2010.539061
39	J C Anderson, F Naeim. Basic structural dynamics. Los Angeles, CA: John Wiley & Sons, 2012
40	N Stubbs, J T Kim, C R Farrar. Field verification of a nondestructive damage localization and severity estimation algorithm. In: Proceedings-SPIE the International Society for Optical Engineering. Nashville, TN: SPIE International Society for Optical, 1995
41	M Bayat, I Pakar, M Bayat. On the large amplitude free vibrations of axially loaded Euler-Bernoulli beams. Steel and Composite Structures, 2013, 14(1): 73–83 https://doi.org/10.12989/scs.2013.14.1.073
42	P Cornwell, S W Doebling, C R Farrar. Application of the strain energy damage detection method to plate-like structures. Journal of Sound and Vibration, 1999, 224(2): 359–374 https://doi.org/10.1006/jsvi.1999.2163
43	F P Beer, E R Johnston, J T DeWolf, D F Mazurek. Mechanics of Materials. 7th ed. McGraw-Hill Education, 2015
44	B L Wahalathantri. Damage assessment in reinforced concrete flexural members using modal strain energy based method. Dissertation for the Doctoral Degree. Brisbane: Queensland University of Technology, 2012
45	ASCE. Global Topics Report on the Prestandard and Commentary for the Seismic Rehabilitation of Buildings (FEMA357). Washington, D.C.: Federal Emergency Management Agency, 2000
46	S D Caltrans. Caltrans Seismic Design Criteria. Version 1.7. Sacramento, CA: California Department of Transportation, 2013

[1]	Hanli WU, Hua ZHAO, Jenny LIU, Zhentao HU. A filtering-based bridge weigh-in-motion system on a continuous multi-girder bridge considering the influence lines of different lanes[J]. Front. Struct. Civ. Eng., 2020, 14(5): 1232-1246.
[2]	Hamed FATHNEJAT, Behrouz AHMADI-NEDUSHAN. An efficient two-stage approach for structural damage detection using meta-heuristic algorithms and group method of data handling surrogate model[J]. Front. Struct. Civ. Eng., 2020, 14(4): 907-929.
[3]	João Pedro SANTOS,Christian CREMONA,André D. ORCESI,Paulo SILVEIRA,Luis CALADO. Static-based early-damage detection using symbolic data analysis and unsupervised learning methods[J]. Front. Struct. Civ. Eng., 2015, 9(1): 1-16.
[4]	Jong-Woong PARK,Sung-Han SIM,Jin-Hak YI,Hyung-Jo JUNG. Development of temperature-robust damage factor based on sensor fusion for a wind turbine structure[J]. Front. Struct. Civ. Eng., 2015, 9(1): 42-47.
[5]	ZHU Jinsong, XIAO Rucheng. Damage identification of a large-span concrete cable-stayed bridge based on genetic algorithm[J]. Front. Struct. Civ. Eng., 2007, 1(2): 170-175.

Viewed

Full text

Abstract

Cited

Shared

Discussed