An intelligent structural damage detection approach based on self-powered wireless sensor data

Automation in Construction

Volumn 62, Issue , 2016, Pages 24-44

  Alavi, Amir H ^a   Hasni, Hassene ^a   Lajnef, Nizar ^a   Chatti, Karim ^a   Faridazar, Fred ^b  

^a Michigan State University   (United States)

^b Federal Highway Administration Virginia Division   (United States)

Author keywords

Damage detection; Probabilistic neural networks; Self powered wireless sensor; Structural health monitoring; Uncertainty analysis

Indexed keywords

DATA ACQUISITION; DECISION THEORY; FINITE ELEMENT METHOD; GAUSSIAN BEAMS; GAUSSIAN NOISE (ELECTRONIC); HIGHWAY ADMINISTRATION; NEURAL NETWORKS; PLATES (STRUCTURAL COMPONENTS); STRUCTURAL ANALYSIS; STRUCTURAL HEALTH MONITORING; UNCERTAINTY ANALYSIS; WIRELESS SENSOR NETWORKS;

BAYESIAN DECISION THEORY; FEDERAL HIGHWAY ADMINISTRATION; PROBABILISTIC NEURAL NETWORKS; SIMPLY SUPPORTED BEAMS; STRUCTURAL DAMAGE DETECTION; THREE POINT BENDING; WIRELESS SENSOR; WIRELESS SENSOR DATA;

DAMAGE DETECTION;

EID: 84947261506     PISSN: 09265805     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.autcon.2015.10.001     Document Type: Article

References (117)

1. M. Malekzadeh, G. Atia, and F.N. Catbas Performance-based structural health monitoring through an innovative hybrid data interpretation framework J. Civ. Struct. Heal. Monit. 5 3 2015 287 305
2. M. Malekzadeh, M. Gul, I.B. Kwon, and F.N. Catbas An integrated approach for structural health monitoring using an in-house built fiber optic system and non-parametric data analysis Smart Struct. Syst. 14 2014 917 942
3. J.J. Lee, J.W. Lee, J.H. Yi, C.B. Yun, and H.Y. Jung Neural networks-based damage detection for bridges considering errors in baseline finite element models J. Sound Vib. 280 2005 555 578
4. S. Shang, G.J. Yun, B.R. Kim, and H.K. Lee Parameter estimation of a rate-dependent damage constitutive model for damage-tolerant brittle composites by Self-OPTIM analyses Int. J. Damage Mech. 2012 10.1177/1056789512464634
5. S.W. Doebling, C.R. Farrar, M.B. Prime, and D.W. Shevitz Damage identification and health monitoring of structural and mechanical systems from changes in their vibration characteristics: a literature review Technical report LA-13070-MS 1996 Los Alamos National Laboratory NM
6. Y. Zou, L. Tong, and G.P. Steven Vibration-based model-dependent damage (delamination) identification and health monitoring for composite structures - a review J. Sound Vib. 230 2 2000 357 378
7. N.E. Huang, Z. Shen, and S.R. Long A new view of nonlinear water waves: the Hilbert spectrum Annu. Rev. Fluid Mech. 31 1999 417 475
8. Z. Hou, M. Noori, and R.S. Amand Wavelet-based approach for structural damage detection, American Society ofCivil Engineers J. Eng. Mech. 126 7 2000 677 683
9. S.T. Quek, Q. Wang, L. Zhang, and K.H. Ong Practical issues in the detection of damage inbeams using wavelets Smart Mater. Struct. 10 2001 1009 1017
10. Z.Y. Shi, S.S. Law, and L.M. Zhang Structural damage localization from modal strain energy change J. Sound Vib. 218 5 1998 825 844
11. K.A. Stetson, and I.R. Harrison Redesign of structural vibration modes by finite element inverse perturbation, American Society of Mechanical Engineers J. Eng. Power 103 1981 319 325
12. C.B. Yun, and K.S. Hong Damage assessment of structures by inverse modal perturbation method Proceedings of the4th East Asia-Pacific Conference on Structural Engineering and Construction, Seoul, Korea 1992
13. J.M.W. Brownjohn, P.Q. Xia, H. Hao, and Y. Xia Civil structure condition assessment by FE model updating: methodology and case studies Finite Elem. Anal. Des. 37 2001 761 775
14. C.R. Farrar, G. Park, D.W. Allen, and M. Todd Sensor network paradigms for structural health monitoring Struct. Control Health Monit. 13 2006 210 255
15. H. Sohn, C.R. Farrar, F.M. Hemez, J.J. Czarnecki, D.D. Shunk, D.W. Stinemates, and B.R. Nadler A Review of Structural Health Monitoring Literature: 1996-2001, Report Number LA-13976-MS 2004 Los Alamos National Laboratory Los Alamos, NM
16. C.R. Farrar Historical Overview of Structural Health Monitoring Lecture Notes on Structural Health Monitoring Using Statistical Pattern Recognition 2001 Los Alamos Dynamics Los Alamos, NM
17. W. Fan, and P. Qiao Vibration-based damage identification methods: a review and comparative study Struct. Health Monit. 10 1 2011 83 111
18. Z.H. Zong, T.L. Wang, and D.Z. Huang State-of-the-art report of bridge health monitoring J. Fuzhou Univ. (Nat. Sci.) 30 2002 127 152
19. M. Sanayei, J.E. Phelps, J.D. Sipple, E.S. Bell, and B.R. Brenner Instrumentation, nondestructive testing, and FEM updating for bridge evaluation using strain measurements J. Bridg. Eng. 17 1 January/February 2012 130 138
20. E. Santini-Bell, M. Sanayei, B. Brenner, J. Sipple, and A. Blanchard Nondestructive testing for design verification of Boston's central artery underpinning frames and connections Bridg. Struct. 4 2 Jun. 2008 87 98
21. B.A. Sundaram, K. Ravisankar, R. Senthil, and S. Parivalla Wireless sensors for structural health monitoring and damage detection techniques Curr. Sci. 104 11 2013 1496 1505
22. J.P. Lynch Overview of wireless sensors for real-time health monitoring of civil structures Proceedings of the 4th International Workshop on Structural Control, New York, NY, June 10-11 2004 189 194
23. J.P. Lynch Design of a wireless active sensing unit for localized structural health monitoring J. Struct. Control Health Monit. 12 3-4 2005 405 423
24. J.P. Lynch, A. Sundararajan, K.H. Law, A.S. Kiremidjian, T. Kenny, and E. Carryer Embedment of structural monitoring algorithms in a wireless sensing unit J. Struct. Eng. Mech. 15 3 2003 285 297
25. J.P. Lynch, A. Sundararajan, K.H. Law, E. Carryer, C.R. Farrar, H. Sohn, D.W. Allen, B. Nadler, and J.R. Wait Design and performance validation of a wireless sensing unit for structural health monitoring applications Struct. Eng. Mech. 17 3 2004 393 408
26. M. Kurata, J. Kim, J.P. Lynch, G.W. van der Linden, H. Seddat, E. Thometz, P. Hipley, and L.H. Sheng Internet-enabled wireless structural monitoring systems: development and permanent deployment at the New Carquinez Suspension Bridge J. Struct. Eng. ASCE 139 10 2013 1688 1702
27. B.F. Spencer Jr., M.E. Ruiz-Sandoval, and N. Kurata Smart sensing technology: opportunities and challenges J. Struct. Control Health Monit. 11 4 2004 349 368
28. B.F. Spencer Jr. Opportunities and challenges for smart sensing technology Proceedings of the International Conference on Structural Health Monitoring and Intelligent Infrastructure, Tokyo, Japan, November 13-15 vol. 1 2003 65 71
29. A. Yonghui, B.F. Spencer Jr., and O. Jinping A test method for damage diagnosis of suspension bridge suspender cables Comput.-Aided Civ. Infrastruct. Eng. 2015 10.1111/mice.12144
30. Z. Zou, Y. Bao, H. Li, and B.F. Spencer Jr. Embedding compressive sensing-based data loss recovery algorithm into wireless smart sensors for structural health monitoring IEEE Sensors J. 15 2 2014 797 808
31. D.G. Watters, P. Jayaweera, A.J. Bahr, D.L. Huestis, N. Priyantha, R. Meline, R. Reis, and D. Parks Smart pebble: wireless sensors for structural health monitoring of bridge decks Proc. SPIE 5057 2003 20 28
32. J.P. Lynch, and K.J. Loh A summary review of wireless sensors and sensor networks for structural health monitoring Shock Vib. Dig. 38 2006 91 128
33. S. Cho, C. Yun, J.P. Lynch, A.T. Zimmerman, B.F. Spencer Jr., and T. Nagayama Smart wireless sensor technology for structural health monitoring of civil structures Int. J. Steel Struct. 8 2008 267 275
34. M.J. Chae, H.S. Yoo, J.Y. Kim, and M.Y. Cho Development of a wireless sensor network system for suspension bridge health monitoring Autom. Constr. 21 2012 237 252
35. S.C. Bae, W.S. Jang, S. Woo, and D.H. Shin Prediction of WSN placement for bridge health monitoring based on material characteristics Autom. Constr. 35 2013 18 27
36. C.B. Yun, and J. Min Smart sensing, monitoring, and damage detection for civil infrastructures KSCE J. Civ. Eng. 15 1 2011 1 14
37. J. Sirohi, and I. Chopra Fundamental understanding of piezoelectric strain sensors J. Intell. Mater. Syst. Struct. 11 2001 246 257
38. S. Roundy, P.K. Wright, and J. Rabaey A study of low level vibrations as a power source for wireless sensor nodes Comput. Commun. 26 2002 1131 1144
39. M. Rahimi, H. Shah, G.S. Sukhatme, J. Heideman, and D. Estrin Studying the feasibility of energy harvesting in a mobile sensor network Proceedings of the 2003 IEEE International Conference on Robotics & Automation, Taipei. Taiwan, September 14-19 2003 19 24
40. N. Lajnef, W. Borchani, R. Burgueño, and S. Chakrabartty Self-powered piezo-floating-gate smart-gauges based on quasi-static mechanical energy concentrators and triggers IEEE Sensors J. 15 2014 676 683 10.1109/JSEN.2014.2351398
41. N. Lajnef, R. Burgueño, W. Borchani, and Y. Sun A concept for energy harvesting from quasi-static structural deformations through axially loaded bilaterally constrained columns with multiple bifurcation points Smart Mater. Struct. 23 2014 055005 10.1088/0964-1726/23/5/055005
42. W. Borchani, N. Lajnef, and R. Burgueño Model development for Dynamic Energy Conversion in post-buckled multi-stable slender columns ASME Conference on Smart Materials, Adaptive Structures and Intelligent Systems, Newport, Rhode Island, September 2014 2014
43. N. Lajnef, W. Borchani, R. Burgueno, and S. Chakrabartty Energy concentrators for piezo-powered quasi-static data logging Proceedings of the 6th World Conference on Structural Control and Monitoring (6WCSCM), Barcelona, Spain July 2014
44. W. Borchani, N. Lajnef, and R. Burgueño Dynamic transitions in multiple bifurcation points mechanical energy concentrators 17th U.S. National Congress on Theoretical and Applied Mechanics June 2014 Michigan State University
45. N. Lajnef, R. Burgueno, W. Borchani, and S. Chakrabartty Sub-Hz self-powered sensing based on mechanical-buckling driven hot-electron injection International Symposium on Circuits and Systems, Melbourne, Australia, June 2014 2014
46. N. Lajnef, S. Chakrabartty, R. Burgueño, and W. Borchani Quasi-static self-powered sensing and data logging Proc. SPIE Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems, San Diego, USA, March 2014 2014
47. W. Borchani, N. Lajnef, and R. Burgueño Control of snap-through transitions in the response of mechanically-equivalent frequency modulators ASME Conference on Smart Materials, Adaptive Structures and Intelligent Systems, Snowbird, UT, September 2013 2013
48. N. Lajnef, R. Burgueño, S. Chakrabartty, and W. Borchani Self-powered sensing system for the monitoring of quasi-static structural response ASME Conference on Smart Materials, Adaptive Structures and Intelligent Systems, Snowbird, UT, September 2013 2013
49. H. Salehi, T. Taghikhany, and A.Y. Fallah Seismic protection of vulnerable equipment with semi-active control by employing robust and clipped-optimal algorithms Int. J. Civ. Eng. 12 4 2014 413 428
50. N. Elvin, A. Elvin, and M. Spector A self-powered mechanical strain energy sensor Smart Mater. Struct. 10 2001 293 299
51. N. Elvin, A. Elvin, and D.H. Choi A self-powered damage detection sensor J. Strain Anal. 38 2 2003 115 124
52. N. Lajnef, M. Rhimi, K. Chatti, and L. Mhamdi Toward an integrated smart sensing system and data interpretation techniques for pavement fatigue monitoring J. Comput. Aided Civ. Infrastruct. Eng. 26 2011 513 523
53. N. Lajnef, S. Chakrabartty, and N. Elvin A piezo-powered floating-gate sensor array for long-term fatigue monitoring in biomechanical implants IEEE Trans. Biomed. Circuits Syst. 2 3 2008 164 172
54. N. Lajnef, K. Chatti, S. Chakrabartty, M. Rhimi, and P. Sarkar Smart pavement monitoring system Report: FHWA-HRT-12-072 2013 Federal Highway Administration (FHWA) Washington, DC
55. C. Huang, N. Lajnef, and S. Chakrabartty Self-calibration and characterization of self-powered floating-gate usage monitors with single electron per second operational limit IEEE Trans. Biomed. Circuits Syst. I 57 2010 556 567
56. A. Tessler, H.R. Riggs, C.E. Freese, and G.M. Cook An improved variational method for finite element stress recovery and a posteriori error estimation Comput. Methods Appl. Mech. Eng. 155 2 1998 15 30
57. A.P. Dempster, N.M. Laird, and D.B. Rubin Maximum likelihood from incomplete data via the EM algorithm J. R. Stat. Soc. B-39 1 1977 1 38
58. A.T.C. Goh Probabilistic neural network for evaluating seismic liquefaction potential Can. Geotech. J. 39 2002 219 232
59. N.G. Singh, and M. Joshi Optimization of location and number of sensors for structural health monitoring using genetic algorithm Mater. Forum 33 2009 359 367
60. C. Huang, and S. Chakrabartty An asynchronous analog self-powered sensor-data-logger with a 13.56 MHz RF programming interface IEEE J. Solid State Circuits 47 2012 10.1109/JSSC.2011.2172159
61. N. Elvin, and C.K.Y. Leung A fast iterative boundary element technique for solving closed crack problems Eng. Fract. Mech. 63 5 1999 631 650
62. P.L. Rosenstrauch, M. Sanayei, and B.R. Brenner Capacity analysis of gusset plate connections using the Whitmore, block shear, global section shear, and finite element methods Eng. Struct. 48 March 2013
63. Jesse D. Sipple, and Masoud Sanayei Finite element model updating using frequency response functions and numerical sensitivities, 2013 Struct. Control. Health Monit. 21 5 2013 784 802
64. NTSB Structural and local failure study of gusset plate in Minneapolis Bridge collapse Report - NTSBC070010 2008 National Transportation Safety Board (NTSB) Washington, DC
65. M. Liao, T. Okazaki, R. Ballarini, A.E. Schultz, and T.V. Galambos Nonlinear finite-element analysis of critical gusset plates in the I-35 W Bridge in Minnesota J. Struct. Eng. ASCE 2011 59 69
66. M. Liao, and T. Okazaki A computational study of the I-35 W bridge collapse Final report, CTS Project # 2008049 2009 Center for Transportation Studies, University of Minnesota Minneapolis, MN
67. R. Holt, and J. Hartmann Adequacy of the U10 & L11 Gusset Plate Designs for the Minnesota Bridge No. 9340 (I-35 over the Mississippi River) Interim Report 2008 Federal Highway Administration
68. S. Hao I-35 W Bridge Collapse J. Bridg. Eng. ASCE 2010 608 614
69. N. Moes, J. Dolbow, and T. Belytschko A finite element method for crack growth without remeshing Int. J. Numer. Methods Eng. 46 1 1999 131 150
70. I. Flood, and P. Christophilos Modeling construction processes using artificial neural networks Autom. Constr. 4 4 1996 307 320
71. N. Kartam, and I. Flood Construction simulation using parallel computing environments Autom. Constr. 10 1 2000 69 78
72. M.A. Shahin, and M.B. Jaksa Neural network prediction of pullout capacity of marquee ground anchors Comput. Geotech. 32 3 2005 153 163
73. S.K. Das, and P.K. Basudhar Prediction of residual friction angle of clays using artifical neural network Eng. Geol. 100 3-4 2008 142 145
74. P. Samui Support vector machine applied to settlement of shallow foundations on cohesionless soils Comput. Geotech. 35 3 2008 419 427
75. M.A. Shahin Intelligent computing for modelling axial capacity of pile foundations Can. Geotech. J. 47 2 2010 230 243
76. A.H. Gandomi, A.H. Alavi, and M.G. Sahab New formulation for compressive strength of CFRP confined concrete cylinders using linear genetic programming Mater. Struct. 43 7 2010 963 983
77. S.K. Das, P. Samui, A.K. Sabat, and T.G. Sitharam Prediction of swelling pressure of soil using artificial intelligence techniques Environ. Earth Sci. 61 2 2010 393 403
78. A.H. Alavi, and A.H. Gandomi Prediction of principal ground-motion parameters using a hybrid method coupling artificial neural networks and simulated annealing Comput. Struct. 89 23-24 2011 2176 2194
79. A.H. Alavi, and A.H. Gandomi A robust data mining approach for formulation of geotechnical engineering systems Eng. Comput. 28 3 2011 242 274
80. S.K. Das, P. Samui, and A.K. Sabat Application of artificial intelligence to maximum dry density and unconfined compressive strength of cement stabilized soil Geotech. Geol. Eng. 29 3 2011 329 342
81. S.K. Das, P. Samui, S.Z. Khan, and N. Sivakugan Machine learning techniques applied to prediction of residual strength of clay Cent. Eur. J. Geosci. 3 4 2011 449 461
82. H.M. Azamathulla, A. Guven, and Y.K. Demir Linear genetic programming to scour below submerged pipeline Ocean Eng. 38 8-9 2011 995 1000
83. H.M. Azamathulla, and F.C. Wu Support vector machine approach for longitudinal dispersion coefficients in natural streams Appl. Soft Comput. 11 2 2011 2902 2905
84. P. Samui Application of statistical learning algorithms to ultimate bearing capacity of shallow foundation on cohesionless soil Int. J. Numer. Anal. Methods Geomech. 36 1 2012 100 110
85. H.M. Azamathulla Gene expression programming for prediction of scour depth downstream of sills J. Hydrol. 460-461 2012 156 159
86. H. Salehi, S. Das, S. Chakrabartty, S. Biswas, and R. Burgueno Structural assessment and damage identification algorithms using binary data ASME 2015 conference on smart materials, adaptive structures and intelligent systems (SMASIS2015), Colorado Springs, Colorado 2015
87. S.K. Babanajad, A.H. Gandomi, D. Mohammadzadeh, and A.H. Alavi Numerical modeling of concrete strength under multiaxial confinement pressures using linear genetic programming Autom. Constr. 36 2013 136 144
88. M.Y. Cheng, M.T. Cao, and D.H. Tran A hybrid fuzzy inference model based on RBFNN and artificial bee colony for predicting the uplift capacity of suction caissons Autom. Constr. 41 2014 60 69
89. M.S. El-Abbasy, A. Senouci, T. Zayed, F. Mirahadi, and L. Parvizsedghy Artificial neural network models for predicting condition of offshore oil and gas pipelines Autom. Constr. 45 2014 50 65
90. J.S. Chou, and A.D. Pham Hybrid computational model for predicting bridge scour depth near piers and abutments Autom. Constr. 48 2014 88 96
91. R.S. Adhikari, O. Moselhi, and A. Bagchi Image-based retrieval of concrete crack properties for bridge inspection Autom. Constr. 39 2014 180 194
92. M.A. Shahin Load-settlement modeling of axially loaded steel driven piles using CPT-based recurrent neural networks Soils Found. 54 3 2014 515 522
93. M.A. Shahin Use of evolutionary computing for modelling some complex problems in geotechnical engineering Geotech. Geol. Eng. 2014 1 17
94. P. Yuvaraj, A.R. Murthy, N.R. Iyer, P. Samui, and S.K. Sekar Prediction of fracture characteristics of high strength and ultra high strength concrete beams based on relevance vector machine Int. J. Damage Mech. 2014 (1056789514520796)
95. P. Szewezyk, and P. Hajela Damage detection in structures based on feature - sensitive neural networks J. Comp. Civ. Eng. ASCE 8 2 1994 163 178
96. X. Wu, J. Gahboussi, and J.H. Garrett Use of neural networks in detection of structural damage[J] Comput. Struct. 42 4 1992 649 659
97. S.F. Masri, A.G. Chassiakos, and T.K. Caughey Identification of nonlinear dynamic systems using neural networks J. Appl. Mech. Trans. ASME 60 1993 123 133
98. M.F. Elkordy, K.C. Chang, and G.C. Lee Neural networks trained by analytically simulated damage states J. Comp. Civ. Eng. ASCE 7 2 1993 130 145
99. R.I. Levin, and N.A.J. Lieven Dynamic finite element model updating using neural network[J] J. Sound Vib. 210 5 1998 593 607
100. J. Zhao, J.N. Ivan, and J.T. DeWolf Structural damage detection using artificial neural networks J. Infrastruct. Syst. ASCE 4 3 1998 0093 0101
101. T. Marwala Damage identification using committee of neural networks[J] J. Eng. Mech. ASCE 126 1 2000 43 50
102. C.C. Chang, T.Y.P. Chang, Y.G. Xu, and M.L. Wang Structural damage detection using an iterative neural network J. Intell. Mater. Syst. Struct. 11 2000 32 42
103. C. Zang, and M. Imregun Structural damage detection using artificial neural networks and measured FRF data reduced via principal comoponent projection J. Sound Vib. 242 5 2001 813 827
104. H. Adeli, and X. Jiang Dynamic fussy wavelet neural network model for structural system identification J. Struct. Eng. 132 2006 102 111
105. S.J.S. Hakim, and H. Abdul Razak Structural damage detection of steel bridge girder using artificial neural networks and finite element models Steel Compos. Struct. 14 2013 367 377
106. B. Yan, and A. Miyamoto Application of probabilistic neural network and static test data to the classification of bridge damage patterns, Smart Structures and Materials 2003: Smart Systems and Nondestructive Evaluation for Civil Infrastructures, Liu, Shih-Chi Proc. SPIE 5057 2003 606 617
107. D. Specht Probabilistic neural networks Neural Netw. 3 1990 109 118
108. B.A. Mogan Damage Detection and Localization using Probabilistic Neural Networks Research Conducted at Keio University Tokyo, Japan 2005
109. J.J. Lee, and C.B. Yun Damage localization for bridges using probabilistic neural networks KSCE J. Civ. Eng. 11 2007 111 120
110. M.P. Paulraj, Y. Sazali, M.S. Abdul, and K. Pranesh Steel plate damage diagnosis using probabilistic neural network 7th International Conference on Intelligent Systems and Control (ISCO) 2013 545 549
111. V. Giurgiutiu, and C. Kropas-Hughes Comparative study of neural-network damage detection from a statistical set of electro-mechanical impedance spectra SPIE's 10th annual international symposium on smart structures and materials and 8th annual international symposium for on NDE for health monitoring and diagnostics, San Diego 2002 1 12
112. S.E. Klenke, and ad T.L. Paez Damage identification with probabilistic neural networks Proceedings of the 14th International Modal Analysis Conference 1996 99 104
113. Y.Q. Ni, S.F. Jiang, and J.M. Ko Application of adaptive probabilistic neural network to damage detection of Tsing Ma suspension bridge Proc. SPIE 4337 Health Monitoring and Management of Civil Infrastructure Systems 347 2001 10.1117/12.435610
114. H. Adeli, and A. Panakkat A probabilistic neural network for earthquake magnitude prediction Neural Netw. 22 2009 1018 1024
115. A.H. Alavi, M. Ameri, A.H. Gandomi, and M.R. Mirzahosseini Formulation of flow number of asphalt mixes using a hybrid computational method Constr. Build. Mater. 25 3 2011 1338 1355
116. R. Kohavi, and P. Foster Glossary of terms Mach. Learn. 30 1998 271 274
117. S.F. Jiang, C. Fu, and C. Zhang A hybrid data-fusion system using modal data and probabilistic neural network for damage detection Adv. Eng. Softw. 42 2011 368 374