Structural Engineering and Mechanics

  • 제37권6호
  • /
  • Pages.671-684
  • /
  • 2011
  • /
  • 1225-4568(pISSN)
  • /
  • 1598-6217(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

A new method for optimal selection of sensor location on a high-rise building using simplified finite element model

  • Yi, Ting-Hua (Faculty of Infrastructure Engineering, State Key Laboratory of Structural Analysis for Industrial Equipment, Dalian University of Technology) ;
  • Li, Hong-Nan (Faculty of Infrastructure Engineering, State Key Laboratory of Structural Analysis for Industrial Equipment, Dalian University of Technology) ;
  • Gu, Ming (State Key Laboratory for Disaster Reduction in Civil Engineering, Tongji University)
  • 투고 : 2010.01.07
  • 심사 : 2010.11.23
  • 발행 : 2011.03.25
⟨ 이전 논문 다음 논문 ⟩

초록

Deciding on an optimal sensor placement (OSP) is a common problem encountered in many engineering applications and is also a critical issue in the construction and implementation of an effective structural health monitoring (SHM) system. The present study focuses with techniques for selecting optimal sensor locations in a sensor network designed to monitor the health condition of Dalian World Trade Building which is the tallest in the northeast of China. Since the number of degree-of-freedom (DOF) of the building structure is too large, multi-modes should be selected to describe the dynamic behavior of a structural system with sufficient accuracy to allow its health state to be determined effectively. However, it's difficult to accurately distinguish the translational and rotational modes for the flexible structures with closely spaced modes by the modal participation mass ratios. In this paper, a new method of the OSP that computing the mode shape matrix in the weak axis of structure by the simplified multi-DOF system was presented based on the equivalent rigidity parameter identification method. The initial sensor assignment was obtained by the QR-factorization of the structural mode shape matrix. Taking the maximum off-diagonal element of the modal assurance criterion (MAC) matrix as a target function, one more sensor was added each time until the maximum off-diagonal element of the MAC reaches the threshold. Considering the economic factors, the final plan of sensor placement was determined. The numerical example demonstrated the feasibility and effectiveness of the proposed scheme.

키워드

참고문헌

  1. Carne, T.G. and Dohmann, C.R. (1995), "A modal test design strategy for modal correlation", Proceedings of the 13th International Modal Analysis Conference, New York, February.
  2. Chung, Y.T. and Moore, J.D. (1993), "On-orbit sensor placement and system identification of space station with limited instrumentations", Proceedings of the 11th International Modal Analysis Conference, Florida, January.
  3. Efstathiades, C.H., Baniotopoulos, C.C., Nazarko, P., Ziemianskib, L. and Stavroulakis, G.E. (2007), "Application of neural Networks for the structural health monitoring in curtain-wall systems", Eng. Struct., 29(12), 3475- 3484. https://doi.org/10.1016/j.engstruct.2007.08.017
  4. Housner, G.W., Bergman, L.A., Caughey, T.K., Chassiakos, A.G., Claus, R.O., Masri, S.F., Skelton, R.E., Soong, T.T., Spencer, B.F. and Yao, J.T.P. (1997), "Structural control: past, present, and future", J. Eng. Mech., 123(9), 897-971. https://doi.org/10.1061/(ASCE)0733-9399(1997)123:9(897)
  5. Heredia-Zavoni, E., Montes-Iturrizaga, R. and Esteva, L. (1999), "Optimal instrumentation of structures on flexible base for system identification", Earthq. Eng. Struct. D., 28(12), 1471-1482. https://doi.org/10.1002/(SICI)1096-9845(199912)28:12<1471::AID-EQE872>3.0.CO;2-M
  6. Hiroyuki, A. (2002), Design of Modern Highrise Reinforced Concrete Structures, Imperial College Press, England.
  7. Kammer, D.C. (1991), "Sensor placement for on-orbit modal identification and correlation of large space structures", J. Guid. Contr. Dynam., 14(2), 251-259. https://doi.org/10.2514/3.20635
  8. Kammer, D.C. (2005), "Sensor set expansion for modal vibration testing", Mech. Syst. Signal Pr., 19(4), 700- 713 https://doi.org/10.1016/j.ymssp.2004.06.003
  9. Kim, H., Kim, W., Kim, B.Y. and Hwang, J.S. (2008), "System identification of a building structure using wireless MEMS and PZT sensors", Struct. Eng. Mech., 30(2), 191-209. https://doi.org/10.12989/sem.2008.30.2.191
  10. Kistera, G., Badcocka, R.A., Gebremichaelb, Y.M., Boyleb, W.J.O., Grattanb, K.T.V., Fernandoc, G.F. and Canningd, L. (2007), "Monitoring of an all-composite bridge using Bragg grating sensors", Constr. Buil. Mater., 21(7), 1599-1604. https://doi.org/10.1016/j.conbuildmat.2006.07.007
  11. Liu, C. and Tasker, F.A. (1996), "Sensor placement for time-domain modal parameter estimation", J. Guid. Contr. D., 19(6), 1349-1356. https://doi.org/10.2514/3.21793
  12. Li, H.N., Yi, T.H., Yi, X.D. and Wang, G.X. (2007), "Measurement and analysis of wind-induced response of tall building based on GPS technology", Adv. Struct. Eng., 10(1), 83-93. https://doi.org/10.1260/136943307780150869
  13. Mossberg, M. (2004), "Optimal experimental design for identification of viscoelastic materials", IEEE T. Contr. Syst. T., 12(2), 578-582. https://doi.org/10.1109/TCST.2004.825132
  14. Majumder, M., Gangopadhyay, T.K., Chakraborty, A.K., Dasgupta, K. and Bhattacharya, D.K. (2008), "Fibre Bragg gratings in structural health monitoring-present status and applications", Sensor Actuat. A-Phys., 147(1), 150-164. https://doi.org/10.1016/j.sna.2008.04.008
  15. Naimimohasses, D.M., Barnett, D.M., Green, D.A. and Smith, P.R. (1995), "Sensor optimization using neural network sensitivity measures", Meas. Sci. Technol., 6(9), 1291-1930. https://doi.org/10.1088/0957-0233/6/9/008
  16. Oh, D.Y. and No, H.C. (1994), "Determination of the minimal number and optimal sensor location in a nuclear system with fixed incore detectors", Nucl. Eng. Des., 152(1-3), 197-212. https://doi.org/10.1016/0029-5493(94)90085-X
  17. Papadimitriou, C. (2004), "Optimal sensor placement methodology for parametric identification of structural systems", J. Sound. Vib., 278(4-5), 923-947. https://doi.org/10.1016/j.jsv.2003.10.063
  18. Qin, X.R. and Zhang, L.M. (2001), "Successive sensor placement for modal paring based on QR-factorization", J. Vib., Meas. Diag., 21(3), 168-173. (In Chinese)
  19. Sun, H.C., Qu, N.S. and Lin, J.H. (1992), Computational Structural Dynamics for High Education, Dalian University of Technology Press, China. (In Chinese)
  20. Takeda, S., Aoki, Y., Ishikawa, T., Takedac, N. and Kikukawad, H. (2007), "Structural health monitoring of composite wing structure during durability test", Compos. Struct., 79(1), 133-139. https://doi.org/10.1016/j.compstruct.2005.11.057
  21. Worden, K. and Burrows, A.P. (2001), "Optimal sensor placement for fault detection", Eng. Struct., 23(8), 885- 901. https://doi.org/10.1016/S0141-0296(00)00118-8

피인용 문헌

  1. Experimental Investigation of a Self-Sensing Hybrid GFRP-Concrete Bridge Superstructure with Embedded FBG Sensors vol.8, pp.10, 2012, https://doi.org/10.1155/2012/902613
  2. Seismic Fortification Analysis of the Guoduo Gravity Dam in Tibet, China vol.2015, 2015, https://doi.org/10.1155/2015/396124
  3. The Reduction of Modal Sensor Channels through a Pareto Chart Methodology vol.2015, 2015, https://doi.org/10.1155/2015/869258
  4. Damage Identification and Optimal Sensor Placement for Structures under Unknown Traffic-Induced Vibrations vol.30, pp.2, 2017, https://doi.org/10.1061/(ASCE)AS.1943-5525.0000550
  5. Sensing Methodologies and Sensor Networks for Health Monitoring of Civil Infrastructures vol.8, pp.12, 2012, https://doi.org/10.1155/2012/358286
  6. Optimal sensor placement for health monitoring of high-rise structure using adaptive monkey algorithm vol.22, pp.4, 2015, https://doi.org/10.1002/stc.1708
  7. Intelligent Monitoring of Multistory Buildings under Unknown Earthquake Excitation by a Wireless Sensor Network vol.8, pp.12, 2012, https://doi.org/10.1155/2012/914638
  8. Sensor Placement for Structural Damage Detection considering Measurement Uncertainties vol.16, pp.5, 2013, https://doi.org/10.1260/1369-4332.16.5.899
  9. Modal Identification for High-Rise Building Structures Using Orthogonality of Filtered Response Vectors vol.32, pp.12, 2017, https://doi.org/10.1111/mice.12310
  10. Multi-dimensional sensor placement optimization for Canton Tower focusing on application demands vol.12, pp.3_4, 2013, https://doi.org/10.12989/sss.2013.12.3_4.235
  11. A Flexible Network Structure for Temperature Monitoring of a Super High Arch Dam vol.8, pp.11, 2012, https://doi.org/10.1155/2012/917849
  12. Feasibility of Output-Only Modal Identification Using Wireless Sensor Network: A Quantitative Field Experimental Study vol.8, pp.11, 2012, https://doi.org/10.1155/2012/560161
  13. Evolutionary Spectra Estimation of Field Measurement Typhoon Processes Using Wavelets vol.2015, 2015, https://doi.org/10.1155/2015/945203
  14. Geotechnical monitoring and analyses on the stability and health of a large cross-section railway tunnel constructed in a seismic area 2017, https://doi.org/10.1016/j.measurement.2017.10.039
  15. Real-Time Monitoring System for Workers' Behaviour Analysis on a Large-Dam Construction Site vol.9, pp.10, 2013, https://doi.org/10.1155/2013/509423
  16. The Node Arrangement Methodology of Wireless Sensor Networks for Long-Span Bridge Health Monitoring vol.9, pp.10, 2013, https://doi.org/10.1155/2013/865324
  17. Real-time simultaneous identification of structural systems and unknown inputs without collocated acceleration measurements based on MEKF-UI 2017, https://doi.org/10.1016/j.measurement.2017.07.001
  18. Reviews on innovations and applications in structural health monitoring for infrastructures vol.1, pp.1, 2014, https://doi.org/10.12989/smm.2014.1.1.001
  19. A modified monkey algorithm for optimal sensor placement in structural health monitoring vol.21, pp.10, 2012, https://doi.org/10.1088/0964-1726/21/10/105033
  20. Optimal sensor placement for health monitoring of high-rise structure based on collaborative-climb monkey algorithm vol.54, pp.2, 2015, https://doi.org/10.12989/sem.2015.54.2.305
  21. An Improved Genetic Algorithm for Optimal Sensor Placement in Space Structures Damage Detection vol.29, pp.3, 2014, https://doi.org/10.1260/0266-3511.29.3.121
  22. Study on the Stress Relaxation Behavior of Large Diameter B-GFRP Bars Using FBG Sensing Technology vol.9, pp.10, 2013, https://doi.org/10.1155/2013/201767
  23. Non-Line-of-Sight Beacon Identification for Sensor Localization vol.8, pp.8, 2012, https://doi.org/10.1155/2012/459590
  24. Diagnosis and recovering on spatially distributed acceleration using consensus data fusion vol.12, pp.3_4, 2013, https://doi.org/10.12989/sss.2013.12.3_4.271
  25. Wind-Induced Vibration Control of Dalian International Trade Mansion by Tuned Liquid Dampers vol.2012, 2012, https://doi.org/10.1155/2012/848031
  26. Review of Bolted Connection Monitoring vol.9, pp.12, 2013, https://doi.org/10.1155/2013/871213
  27. Efficient Sensor Placement Optimization Using Gradient Descent and Probabilistic Coverage vol.14, pp.12, 2014, https://doi.org/10.3390/s140815525
  28. Identification of flexible buildings with bending deformation and the unmeasured earthquake ground motion vol.58, pp.3, 2015, https://doi.org/10.1007/s11431-014-5755-2
  29. 3D sensor placement strategy using the full-range pheromone ant colony system vol.25, pp.7, 2016, https://doi.org/10.1088/0964-1726/25/7/075003
  30. Distributed Strain Sensor Networks for In-Construction Monitoring and Safety Evaluation of a High-Rise Building vol.8, pp.9, 2012, https://doi.org/10.1155/2012/685054
  31. Improvement of decentralized random decrement technique for data processing in wireless sensor network vol.15, pp.3, 2016, https://doi.org/10.1007/s11803-016-0349-6
  32. System identification of super high-rise buildings using limited vibration data during the 2011 Tohoku (Japan) earthquake 2012, https://doi.org/10.1002/stc.1537
  33. Data fusion based EKF-UI for real-time simultaneous identification of structural systems and unknown external inputs vol.88, 2016, https://doi.org/10.1016/j.measurement.2016.02.002
  34. Temperature-Compensated Damage Monitoring by Using Wireless Acceleration-Impedance Sensor Nodes in Steel Girder Connection vol.8, pp.9, 2012, https://doi.org/10.1155/2012/167120
  35. Isogeometric-Meshfree Coupled Analysis of Kirchhoff Plates vol.17, pp.8, 2014, https://doi.org/10.1260/1369-4332.17.8.1159
  36. Research on Optimal Sensor Placement Based on Reverberation Matrix for Structural Health Monitoring vol.8, pp.11, 2012, https://doi.org/10.1155/2012/454530
  37. A triaxial accelerometer monkey algorithm for optimal sensor placement in structural health monitoring vol.26, pp.6, 2015, https://doi.org/10.1088/0957-0233/26/6/065104
  38. Bridge Assessment and Health Monitoring with Distributed Long-Gauge FBG Sensors vol.9, pp.12, 2013, https://doi.org/10.1155/2013/494260
  39. Dynamic Programming Approach to Load Estimation Using Optimal Sensor Placement and Model Reduction 2018, https://doi.org/10.1142/S0219876218500718
  40. Calibration of the Performance of Bidirectional Shaker Table in Centrifuge Model Tests Using Sensor Networks vol.9, pp.7, 2013, https://doi.org/10.1155/2013/648916
  41. A novel transmissibility concept based on wavelet transform for structural damage detection vol.12, pp.3_4, 2013, https://doi.org/10.12989/sss.2013.12.3_4.291
  42. Anchor Dragging Analysis of Rock-Berm Using Smoothed Particle Hydrodynamics Method vol.2015, 2015, https://doi.org/10.1155/2015/687623
  43. A labor consumption measurement system based on real-time tracking technology for dam construction site vol.52, 2015, https://doi.org/10.1016/j.autcon.2015.02.004
  44. Simulation Study on Train-Induced Vibration Control of a Long-Span Steel Truss Girder Bridge by Tuned Mass Dampers vol.2014, 2014, https://doi.org/10.1155/2014/506578
  45. Fatigue Damage Evolution and Monitoring of Carbon Fiber Reinforced Polymer Bridge Cable by Acoustic Emission Technique vol.8, pp.10, 2012, https://doi.org/10.1155/2012/282139
  46. Field Implementation of Wireless Vibration Sensing System for Monitoring of Harbor Caisson Breakwaters vol.8, pp.12, 2012, https://doi.org/10.1155/2012/597546
  47. Experimental study on failure behaviour of deep tunnels under high in-situ stresses vol.46, 2015, https://doi.org/10.1016/j.tust.2014.10.009
  48. Methodology Developments in Sensor Placement for Health Monitoring of Civil Infrastructures vol.8, pp.8, 2012, https://doi.org/10.1155/2012/612726
  49. Safety assessment of submarine power cable protectors by anchor dragging field tests vol.65, 2013, https://doi.org/10.1016/j.oceaneng.2013.03.004
  50. Sensor placement on Canton Tower for health monitoring using asynchronous-climb monkey algorithm vol.21, pp.12, 2012, https://doi.org/10.1088/0964-1726/21/12/125023
  51. A Two-stage Parametric Identification of Strong Nonlinear Structural Systems with Incomplete Response Measurements vol.16, pp.04, 2016, https://doi.org/10.1142/S0219455416400228
  52. Robust control of civil structures with parametric uncertainties through D-K iteration vol.25, pp.3, 2016, https://doi.org/10.1002/tal.1233
  53. Sensor placement for structural health monitoring of Canton Tower vol.10, pp.4_5, 2012, https://doi.org/10.12989/sss.2012.10.4_5.313
  54. Health monitoring sensor placement optimization for Canton Tower using immune monkey algorithm vol.22, pp.1, 2015, https://doi.org/10.1002/stc.1664
  55. Identifiability-Enhanced Bayesian Frequency-Domain Substructure Identification vol.33, pp.9, 2018, https://doi.org/10.1111/mice.12377
  56. Optimal sensor placement methods and metrics – comparison and implementation on a timber frame structure vol.14, pp.7, 2018, https://doi.org/10.1080/15732479.2018.1438483
  57. Computational modeling of a unique tower in Kuwait for structural health monitoring: Numerical investigations vol.26, pp.3, 2019, https://doi.org/10.1002/stc.2317
  58. Sensor Placement Optimization in Structural Health Monitoring Using Niching Monkey Algorithm vol.14, pp.5, 2011, https://doi.org/10.1142/s0219455414400124
  59. Spurious mode distinguish by eigensystem realization algorithm with improved stabilization diagram vol.63, pp.6, 2011, https://doi.org/10.12989/sem.2017.63.6.743
  60. Improved block-wise MET for estimating vibration fields from the sensor vol.64, pp.3, 2011, https://doi.org/10.12989/sem.2017.64.3.279
  61. Frequency analysis of GPS data for structural health monitoring observations vol.66, pp.2, 2011, https://doi.org/10.12989/sem.2018.66.2.185
  62. A Deformation Prediction Approach for Supertall Building Using Sensor Monitoring System vol.2019, pp.None, 2019, https://doi.org/10.1155/2019/9283584
  63. Research on the Application of an Information System in Monitoring the Dynamic Deformation of a High-Rise Building vol.2020, pp.None, 2011, https://doi.org/10.1155/2020/3714973
  64. Artificial Neural Network for Vertical Displacement Prediction of a Bridge from Strains (Part 2): Optimization of Strain-Measurement Points by a Genetic Algorithm under Dynamic Loading vol.10, pp.3, 2011, https://doi.org/10.3390/app10030777
  65. Design and development of instrumentation for the measurement of sensor array responses vol.91, pp.2, 2020, https://doi.org/10.1063/1.5128967
  66. Bridge condition monitoring under moving loads using two sensor measurements vol.19, pp.3, 2020, https://doi.org/10.1177/1475921719868930
  67. Computational methodologies for optimal sensor placement in structural health monitoring: A review vol.19, pp.4, 2011, https://doi.org/10.1177/1475921719877579
  68. An innovative approach for conducting experimental modal analysis (EMA) in running harmonic for structural modal identification vol.159, pp.None, 2011, https://doi.org/10.1016/j.measurement.2020.107795
  69. Experimental validation of the proposed extended Kalman filter with unknown inputs algorithm based on data fusion vol.39, pp.4, 2011, https://doi.org/10.1177/1461348419868860
  70. PrimaVera: Synergising Predictive Maintenance vol.10, pp.23, 2011, https://doi.org/10.3390/app10238348
  71. A distance coefficient-multi objective information fusion algorithm for optimal sensor placement in structural health monitoring vol.24, pp.4, 2021, https://doi.org/10.1177/1369433220964375
  72. An adaptive sensor placement algorithm for structural health monitoring based on multi-objective iterative optimization using weight factor updating vol.151, pp.None, 2011, https://doi.org/10.1016/j.ymssp.2020.107363
  73. State-Input System Identification of Tall Buildings under Unknown Seismic Excitations Based on Modal Kalman Filter with Unknown Input vol.34, pp.4, 2011, https://doi.org/10.1061/(asce)as.1943-5525.0001277
  74. A probabilistic optimal sensor design approach for structural health monitoring using risk-weighted f -divergence vol.161, pp.None, 2011, https://doi.org/10.1016/j.ymssp.2021.107920
  75. A novel load-dependent sensor placement method for model updating based on time-dependent reliability optimization considering multi-source uncertainties vol.165, pp.None, 2011, https://doi.org/10.1016/j.ymssp.2021.108386
  76. An optimal sensor placement design framework for structural health monitoring using Bayes risk vol.168, pp.None, 2022, https://doi.org/10.1016/j.ymssp.2021.108618