Please wait a minute...
购物车 申请加入邮件组 English
高级检索 图表检索
Frontiers of Structural and Civil Engineering

ISSN 2095-2430

ISSN 2095-2449(Online)

CN 10-1023/X

邮发代号 80-968

2019 Impact Factor: 1.68

热点文章  |  下载排行  |  浏览排行
在线投稿 免费RSS订阅
Frontiers of Structural and Civil Engineering  2018, Vol. 12 Issue (1): 92-108   https://doi.org/10.1007/s11709-016-0379-1
  本期目录
A combination of damage locating vector method (DLV) and differential evolution algorithm (DE) for structural damage assessment
T. NGUYEN-THOI1,3(), A. TRAN-VIET2,3, N. NGUYEN-MINH1,3, T. VO-DUY1,3, V. HO-HUU1,3
1. Division of Computational Mathematics and Engineering, Institute for Computational Science, Ton Duc Thang University, Hochiminh City, Vietnam
3. Faculty of Civil Engineering, Ton Duc Thang University, Hochiminh City, Vietnam
 全文: PDF(1843 KB)   HTML
Abstract：

In this study, a two-stage method is presented for identifying multiple damage scenarios. In the first stage, the damage locating vector (DLV) method using normalized cumulative energy (nce) is employed for damage localization in structures. In the second stage, the differential evolution algorithm (DE) is used for damage severity of the structures. In addition, in the second stage, a modification of an available objective function is made for handing the issue of symmetric structures. To verify the effectiveness of the present technique, numerical examples of a 72-bar space truss and a one-span steel portal frame are considered. In addition, the effect of noise on the performance of the identification results is also investigated. The numerical results show that the proposed combination gives good assessment of damage location and extent for multiple structural damage cases.
Key words： damage assessment    damage locating vector method (DLV)    differential evolution (DE)    multiple damage location assurance criterion (MDLAC)    mode shape error function
收稿日期: 2016-07-04      出版日期: 2018-03-08
Corresponding Author(s): T. NGUYEN-THOI   
 引用本文:   
. [J]. Frontiers of Structural and Civil Engineering, 2018, 12(1): 92-108.
T. NGUYEN-THOI, A. TRAN-VIET, N. NGUYEN-MINH, T. VO-DUY, V. HO-HUU. A combination of damage locating vector method (DLV) and differential evolution algorithm (DE) for structural damage assessment. Front. Struct. Civ. Eng., 2018, 12(1): 92-108.
 链接本文:  
https://academic.hep.com.cn/fsce/CN/10.1007/s11709-016-0379-1
https://academic.hep.com.cn/fsce/CN/Y2018/V12/I1/92
Fig.1  
parameter DE PSO
mutation strategy DE / Rand / 1 –
crossover scheme binomial –
population size (NP) 20 (two damaged elements)
30 (three damaged elements)		 20 (two damaged elements)
30 (three damaged elements)
mutation constant (F) 0.5 –
crossover constant (CR) 1.0 –
maximum number of iterations 200 200
cognitive parameters (C1) - 1.49
social parameters (C2) - 1.49
inertia range - [0.1,1.1]
tolerance 1e-5 1e-5
Tab.1  
property/unit value
E (Young’s modulus)/(N·m–2) 6.98e+10
ρ (mass density)/(kg·m–3) 2770
add mass/kg 2270
Tab.2  
element group cross – sectional area element group cross – sectional area
1–4 2.854 37–40 16.328
5–12 8.301 41–48 8.299
13–16 0.645 49–52 0.645
17–18 0.645 53–54 0.645
19–22 8.202 55–58 15.048
23–30 7.043 59–66 8.268
31–34 0.645 67–70 0.645
35–36 0.645 71–72 0.645
Tab.3  
Case 1 case 2
element No. extent (%) element No. extent (%)
7 10 11 10
9 30 22 20
– – 52 25
Tab.4  
mode Kaveh and Zolghadr [50] undamaged model
(present)		 damaged model (present)
case 1 case 2
1 4.000 4.0003 3.9798 3.9702
2 4.000 4.0003 3.9949 3.9977
3 6.004 6.0002 5.9532 5.9904
4 6.2491 6.2496 6.2425 6.2306
5 8.9726 8.9728 8.9080 8.9378
Tab.5  
DE PSO
No. damaged element and damage extent No. of
iterations		 damaged element and damage extent No. of
iterations
7 9 7 9
1 10 30 46 10 30 53
2 10 30 44 10 30 70
3 10 30 48 10 30 55
4 10 30 42 10 30 60
5 10 30 44 10 30 47
6 10 30 48 10 30 50
7 10 30 51 10 30 63
8 10 30 44 10 30 56
9 10 30 41 10 30 58
10 10 30 49 10 30 53
average 10 30 45.7 10 30 56.5
Tab.6  
DE PSO
No. damaged element and damage extent No. of iterations damaged element and damage extent No. of iterations
7 9 7 9
1 30.02 10.02 30 30.00 10.00 47
2 30.02 10.00 27 10.00 30.00 33
3 9.95 29.93 38 10.00 30.00 42
4 30.02 10.00 43 30.00 10.00 37
5 10.06 30.08 29 10.02 30.01 38
6 30.06 10.03 40 30.00 10.01 33
7 9.93 29.97 32 10.00 30.00 36
8 29.94 9.95 31 10.06 30.08 50
9 10.00 30.00 42 10.00 30.00 34
10 10.09 30.06 26 10.00 30.00 34
average 20.01 20.00 33.8 16.01 24.01 38.4
Tab.7  
Fig.2  
Fig.3  
Fig.4  
DE PSO
No. damaged element and damage extent No. of
iterations		 damaged element and damage extent No. of
iterations
11 22 52 11 22 52
1 10 20 25 79 10 20 25 93
2 10 20 25 77 10 20 25 78
3 10 20 25 75 10 20 25 109
4 10 20 25 71 10 20 25 98
5 10 20 25 77 10 20 25 93
6 10 20 25 79 10 20 25 93
7 10 20 25 64 10 20 25 96
8 10 20 25 78 10 20 25 60
9 10 20 25 77 10 20 25 83
10 10 20 25 76 10 20 25 121
average 10 20 25 75.3 10 20 25 92.4
Tab.8  
Fig.5  
Fig.6  
Fig.7  
case 1 case 2
element No. extent (%) element No. extent (%)
4 15 4 25
25 25 19 10
– – 25 20
Tab.9  
mode Hao and Xia [39] undamaged model
(present)		 damaged model (present)
case 1 case 2
1 4.69 4.69 4.68 4.67
2 18.22 18.23 18.16 18.16
3 28.93 28.95 28.59 28.54
4 31.48 31.49 31.24 31.20
5 64.53 64.57 64.19 63.92
Tab.10  
Fig.8  
refined mesh 1
(mesh size of 60)		 refined mesh 2
(mesh size of 90)
element No. extent (%) element No. extent (%)
7 15 10 15
8 15 11 15
49 25 22 15
50 25 73 25
– – 74 25
– – 75 25
Tab.11  
DE PSO
No. damaged element and damage extent No. of
iterations		 damaged element and damage extent No. of
iterations
4 25 4 25
1 15 20 49 15 20 63
2 15 20 51 15 20 63
3 15 20 48 15 20 51
4 15 20 43 15 20 58
5 15 20 52 15 20 47
6 15 20 47 15 20 63
7 15 20 43 15 20 62
8 15 20 52 15 20 52
9 15 20 47 15 20 52
10 15 20 46 15 20 46
average 15 20 47.8 15 20 55.7
Tab.12  
Fig.9  
Fig.10  
Fig.11  
DE PSO
No. damaged element and damage extent No. of
iterations		 damaged element and damage extent No. of
iterations
4 19 25 4 19 25
1 25 10 20 59 25 10 20 83
2 25 10 20 69 25 10 20 69
3 24.94 10.87 20 58 25 10 20 63
4 25 10 20 60 25 10 20 73
5 25 10 20 60 25 10 20 67
6 25 10 20 59 25 10 20 64
7 25 10 20 74 25 10 20 66
8 25.07 10.28 20 58 25 10 20 78
9 25 10 20 53 25 10 20 67
10 25 9.98 20 61 25 10 20 72
average 25 10.11 20 59 25 10 20 70.2
Tab.13  
Fig.12  
Fig.13  
DE PSO No. of iterations
No. damaged element and damage extent No. of iterations damaged element and damage extent
7 9 7 9
1 10.03 30.05 35 10.01 30.03 48
2 10.03 30.05 35 10.01 30.03 42
3 10.03 30.05 39 10.01 30.03 42
4 10.03 30.05 39 10.01 30.03 42
5 10.32 30.52 62 10.01 30.03 45
6 10.03 30.05 42 10.01 30.03 42
7 10.03 30.05 41 10.01 30.03 52
8 10.03 30.05 35 10.01 30.03 59
9 10.03 30.05 36 10.01 30.03 55
10 10.03 30.05 38 10.01 30.03 42
average 10.06 30.10 40.2 10.01 30.03 46.9
Tab.14  
Fig.14  
Fig.15  
1 Yuen M M F. A numerical study of the eigenparameters of a damaged cantilever. Journal of Sound and Vibration, 1985, 103(3): 301–310
2 Pandey A K, Biswas  M, Samman M M. Damage detection from changes in curvature mode shapes. Journal of Sound and Vibration, 1991, 145(2): 321–332
3 Salawu O, Williams  C. Bridge Assessment Using Forced-Vibration Testing. Journal of Structural Engineering, 1995, 121(2): 161–173
4 Pandey A K, Biswas  M. Damage detection in structures using changes in flexibility. Journal of Sound and Vibration, 1994, 169(1): 3–17
5 Pandey A K, Biswas  M. Experimental verification of flexibility difference method for locating damage in structures. Journal of Sound and Vibration, 1995, 184(2): 311–328
6 Stubbs N, Kim  J T, Topole  K. An efficient and robust algorithm for damage localization in offshore platforms. Proc. ASCE Tenth Struct. Congr., 1992, 543–546
7 Bernal D. Load vectors for damage localization. Journal of Engineering Mechanics, 2002, 128(1): 7–14
8 Areias P, Msekh  M A, Rabczuk  T. Damage and fracture algorithm using the screened Poisson equation and local remeshing. Engineering Fracture Mechanics, 2016, 158: 116–143
9 Areias P, Rabczuk  T, Camanho P P. Initially rigid cohesive laws and fracture based on edge rotations. Computational Mechanics, 2013, 52(4): 931–947
10 Areias P, Rabczuk  T, Camanho P P. Finite strain fracture of 2D problems with injected anisotropic softening elements. Theoretical and Applied Fracture Mechanics, 2014, 72(0): 50–63
11 Areias P, Rabczuk  T, Dias-da-Costa D. Element-wise fracture algorithm based on rotation of edges. Engineering Fracture Mechanics, 2013, 110(0): 113–137
12 Rabczuk T, Areias  P M A. A new approach for modelling slip lines in geological materials with cohesive models. International Journal for Numerical and Analytical Methods in Geomechanics, 2006, 30(11): 1159–1172
13 Rabczuk T, Belytschko  T. Cracking particles: a simplified meshfree method for arbitrary evolving cracks. International Journal for Numerical Methods in Engineering, 2004, 61(13): 2316–2343
14 Rabczuk T, Belytschko  T. Application of Particle Methods to Static Fracture of Reinforced Concrete Structures. International Journal of Fracture, 2006, 137(1): 19–49
15 Rabczuk T, Zi  G, Bordas S,  Nguyen-Xuan H. A geometrically non-linear three-dimensional cohesive crack method for reinforced concrete structures. Engineering Fracture Mechanics, 2008, 75(16): 4740–4758
16 Zhu H, Zhuang  X, Cai Y,  Ma G. High rock slope stability analysis using the enriched meshless shepard and least squares method. International Journal of Computational Methods, 2011, 08(02): 209–228
17 Zhuang X, Augarde  C E, Mathisen  K M. Fracture modeling using meshless methods and level sets in 3D: Framework and modeling. International Journal for Numerical Methods in Engineering, 2012, 92(11): 969–998
18 Zhuang X, Zhu  H, Augarde C. An improved meshless Shepard and least squares method possessing the delta property and requiring no singular weight function. Computational Mechanics, 2014, 53(2): 343–357
19 Nanthakumar S S,  Lahmer T,  Zhuang X,  Zi G, Rabczuk  T. Detection of material interfaces using a regularized level set method in piezoelectric structures. Inverse Problems in Science and Engineering, 2016, 24(1): 153–176
20 Nanthakumar S S,  Lahmer T,  Rabczuk T. Detection of multiple flaws in piezoelectric structures using XFEM and level sets. Computer Methods in Applied Mechanics and Engineering, 2014, 275(12): 98–112
21 Nanthakumar S S,  Lahmer T,  Rabczuk T. Detection of flaws in piezoelectric structures using extended FEM. International Journal for Numerical Methods in Engineering, 2013, 96(6): 373–389
22 Vo-Duy T, Ho-Huu  V, Dang-Trung H,  Nguyen-Thoi T. A two-step approach for damage detection in laminated composite structures using modal strain energy method and an improved differential evolution algorithm. Composite Structures, 2016, 147: 42–53
23 Lie S T, Zhang  Y, Wang L Q. Damage Detection in Compressed Natural Gas (CNG) Cylinders Based on Auxiliary Mass Induced Frequency Shift. Experimental Mechanics, 2015, 55(3): 487–498
24 Quek S T, Tran  V A, Hou  X Y, Duan  W H. Structural damage detection using enhanced damage locating vector method with limited wireless sensors. Journal of Sound and Vibration, 2009, 328(4–5): 411–427
25 Cawley P, Adams  R D. The location of defects in structures from measurements of natural frequencies. Journal of Strain Analysis for Engineering Design, 1979, 14(2): 49–57
26 Messina A, Williams  E J, Contursi  T. Structural damage detection by a sensitivity and statistical-based method. Journal of Sound and Vibration, 1998, 216(5): 791–808
27 Mares C, Surace  C. An application of genetic algorithms to identify damage in elastic structures. Journal of Sound and Vibration, 1996, 195(2): 195–215
28 Koh B H, Dyke  S J. Structural health monitoring for flexible bridge structures using correlation and sensitivity of modal data. Computers & Structures, 2007, 85(3–4): 117–130
29 Mohan S C, Maiti  D K, Maity  D. Structural damage assessment using FRF employing particle swarm optimization. Applied Mathematics and Computation, 2013, 219(20): 10387–10400
30 Kaveh A, Javadi  S M, Maniat  M. Damage Assessment via Modal Data with a Mixed Particle Swarm Strategy, Ray Optimizer, and Harmony Search. Asian Journal of Civil Engineering, 2014, 15(1): 95–106
31 Kaveh A, Maniat  M. Damage detection based on MCSS and PSO using modal data. Smart Struct Syst. Techno, 2015, 15(5): 1253–1270
32 Kaveh A, Zolghadr  A. An improved CSS for damage detection of truss structures using changes in natural frequencies and mode shapes. Advances in Engineering Software, 2015, 80: 93–100
33 Kaveh A, Talatahari  S. A novel heuristic optimization method: charged system search. Acta Mechanica, 2010, 213(3): 267–289
34 Moslem K, Nafaspour  R. Structural damage detection by genetic algorithms. AIAA Journal, 2002, 40(7): 1395–1401
35 Au F T K,  Cheng Y S,  Tham L G,  Bai Z Z. Structural damage detection based on a micro-genetic algorithm using incomplete and noisy modal test data. Journal of Sound and Vibration, 2003, 259(5): 1081–1094
36 Guo H Y, Li  Z L. A two-stage method to identify structural damage sites and extents by using evidence theory and micro-search genetic algorithm. Mechanical Systems and Signal Processing, 2009, 23(3): 769–782
37 Seyedpoor S M. A two stage method for structural damage detection using a modal strain energy based index and particle swarm optimization. International Journal of Non-linear Mechanics, 2012, 47(1): 1–8
38 Fan W, Qiao  P. Vibration-based Damage Identification Methods: A Review and Comparative Study. Structural Health Monitoring, 2011, 10(1): 83–111
39 Hao H, Xia  Y. Vibration-based Damage Detection of Structures by Genetic Algorithm. Journal of Computing in Civil Engineering, 2002, 16(3): 222–229
40 Storn R, Price  K. Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization, 1997, 11(4): 341–359
41 Price K, Storn  R M, Lampinen  J A. Differential evolution: a practical approach to global optimization. Springer, 2006
42 Civicioglu P, Besdok  E. A conceptual comparison of the Cuckoo-search, particle swarm optimization, differential evolution and artificial bee colony algorithms. Artificial Intelligence Review, 2013, 39(4): 315–346
43 Hegerty B, Hung  C, Kasprak K. A Comparative Study on Differential Evolution and Genetic Algorithms for Some Combinatorial Problems. Mexican International Conference on Artificial Intelligence, 2009
44 Gao Y, Spencer  B F, Ruiz-Sandoval  M. Distributed computing strategy for structural health monitoring. Structural Control and Health Monitoring, 2006, 13(1): 488–507
45 Bernal D, Gunes  B. Flexibility Based Approach for Damage Characterization: Benchmark Application. Journal of Engineering Mechanics, 2003, 130(1): 61–70
46 Gao Y, Spencer  B Jr, Bernal D. Experimental Verification of the Flexibility-Based Damage Locating Vector Method. Journal of Engineering Mechanics, 2007, 133(10): 1043–1049
47 Qian J, Ji  X, Xu Y. Two-stage damage diagnosis approach for steel braced space frame structures. Engineering Structures, 2007, 29(12): 3277–3292
48 Vo-Duy T, Nguyen-Minh  N, Dang-Trung H,  Tran-Viet A,  Nguyen-Thoi T. Damage assessment of laminated composite beam structures using damage locating vector (DLV) method. Frontiers of Structural and Civil Engineering, 2015, 9(4): 457–465
49 Storn R. On the Usage of Differential Evolution for Function Optimization. in NAFIPS’96, IEEE, 1996, 519–523
50 Kaveh A, Zolghadr  A. Truss optimization with natural frequency constraints using a hybridized CSS-BBBC algorithm with trap recognition capability. Computers & Structures, 2012, 102–10314–27
51 Xiang J, Liang  M, He Y. Experimental investigation of frequency-based multi-damage detection for beams using support vector regression. Engineering Fracture Mechanics, 2014, 131: 257–268
52 Shi Z, Law  S, Zhang L. Structural damage detection from modal strain energy change. Journal of Engineering Mechanics, 2000, 126(12): 1216–1223
53 Fu Y, Liu  J, Wei Z,  Lu Z. A two-step approach for damage Identification in plates. Journal of Vibration and Control, 2014
54 Wei Z T, Liu  J K, Lu  Z R. Damage identification in plates based on the ratio of modal strain energy change and sensitivity analysis. Inverse Problems in Science and Engineering, 2016, 24(2): 265– 283
55 Vu-Bac N, Lahmer  T, Zhuang X,  Nguyen-Thoi T,  Rabczuk T. A software framework for probabilistic sensitivity analysis for computationally expensive models. Advances in Engineering Software, 2016, 100: 19–31
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed