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Civil Structural Testing

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Handbook of Experimental Structural Dynamics

Abstract

Civil engineering structures are especially complex due to their size, geometric and physical uniqueness, intrinsic nonlinearity, and also due to the variations of their properties as a function of environmental or operational loadings and support conditions. While overloading can produce severe but recoverable changes in modal properties, in the order of 30%, environmental conditions like temperature and humidity can produce state changes, particularly in supporting soils and boundary conditions, that have been observed to impose changes of 50% on modal properties without the presence of damage. Some of these variations are abrupt while others are slow, yet regardless of their speed they impose challenges on the experimental setups and identifications techniques. The basic civil structural instrumentation is mainly based on accelerometers and displacements sensors, being their characteristics described in the present chapter. The environmental excitations are in general nonstationary and input excitations are usually not measured. So, response is generally analyzed using Operational Modal Analysis (OMA) techniques, even if Experimental Modal Analysis (EMA) have also been extensively used. The most commonly applied technique for identification is the Stochastic Identification algorithm in their covariance and data-driven versions. In order to observe the complexities of civil infrastructure, examples are given for buildings under environmental and earthquake loads and bridges under environmental and traffic loads. Identification and automatic tracking algorithm are presented theoretically and with examples.

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Abbreviations

[A]:

System state matrix

a gi :

Base acceleration

a P :

Acceleration at position p

\( {a}_i^{(k)} \) :

Average distance between the i-th object of a cluster and the remaining object of the same cluster

ADC:

Analog to digital

ARX:

Auto-regressive model with exogenous input

\( {b}_i^{(k)} \) :

Average distance between the i-th object of a cluster and the objects assigned to other clusters

B(PK):

Overall between-cluster distance measured from a partition P of k clusters

[C]:

Observation matrix

COV:

Reference to covariance-driven in SSI

C k :

k-th cluster of partition PK

d(xi,xj):

Distance between observations i and j measured for quantity x

DAS:

Data acquisition systems

EFDD:

Enhanced frequency domain decomposition

EMD:

Empirical mode decomposition

ERA:

Eigensystem realization algorithm

FDD:

Frequency domain decomposition

FFT:

Fast Fourier transform

[H]:

Hankel matrix

ITD:

Ibrahim time domain decomposition

K :

Number of clusters in cluster partition

[L]:

Matrix obtained from LQ decomposition

LVDT:

Linear variable differential transformer

MAC:

Modal assurance criterion

MIMO:

Muitiple input multiple output

MOESP:

Multivariate output-error state space

MP:

Mean phase

MPD:

Mean phase deviation

MPC:

Mean phase collinearity

Next-ERA:

Natural excitation technique eigensystem realization algorithm

[O]:

Observability matrix

P k :

Cluster partition containing k clusters

PP:

Peak picking

[Q]:

Matrix obtained from LQ decomposition

R XX, RXY:

Cross-correlation

SIL(Pt):

Silhouette width of partition P containing t clusters

S/N:

Signal-to-noise ratio

SHM:

Structural Health Monitoring

\( {s}_i^{(k)} \) :

Silhouette width of the i-th object assigned to a cluster

s k :

Silhouette width of cluster k

SSI:

Stochastic subspace identification

STFT:

Short-time Fourier transform

SVD:

Singular value decomposition

S XX, SXY:

Periodogram

T :

Overall distance measured from a cluster partition

[T]:

Toeplitz matrix

[U]:

Hankel matrix of observations

UPS:

Uninterruptable power supply

v(t):

Decay response

W(PK):

Overall within-cluster distance measured from a partition P of k clusters

{y}:

Response vector

\( \dot{y},\ddot{y} \) :

First and second time derivatives of y

[Y]:

Hankel matrix of response

{z}:

State space vector

β t :

Damping of mode t

γ 2 :

Coherence

λ :

Eigenvalue

ω, f:

Natural frequency of mode t

[Σ]:

Singular values matrix

[ϕ]:

Modal shape matrix

[ψ]:

Eigenvector matrix

References

  1. Allemang R (2003) The modal assurance criterion – twenty years of use and abuse. Sound and Vibration 37(8):14–21. https://doi.org/10.1016/j.chemgeo.2006.02.014

    Article  Google Scholar 

  2. Astroza R, Gutiérrez G, Repenning C, Hernández F (2018) Time-variant modal parameters and response behavior of a base-isolated building tested on a shake table. Earthquake Spectra 34(1):121–143. https://doi.org/10.1193/032817EQS054M

    Article  Google Scholar 

  3. Beck JL (1978) Determining models of structures from earthquake records. EERL report 78-01

    Google Scholar 

  4. Bernal D, Döhler M, Kojidi SM, Kwan K, Liu Y (2015) First mode damping ratios for buildings. Earthquake Spectra 31(1):367–381. https://doi.org/10.1193/101812EQS311M

    Article  Google Scholar 

  5. Bishop CM (2006) Pattern recognition and machine learning. pattern recognition. Springer, Berkeley

    MATH  Google Scholar 

  6. Bixio AR, Dolce M, Nigro D, Ponzo FC, Braga F, Nicoletti M (2001) Repeatable dynamic release tests on a base-isolated building. Journal of Earthquake Engineering 5(3):369–393. https://doi.org/10.1080/13632460109350398

    Article  Google Scholar 

  7. Boore DM, Sisi AA, Akkar S (2012) Using pad-stripped acausally filtered strong-motion data. Bulletin of the Seismological Society of America 102(2):751–760. https://doi.org/10.1785/0120110222

    Article  Google Scholar 

  8. Boroschek RL, Bilbao JA (2019) Interpretation of stabilization diagrams using density-based clustering algorithm. Engineering Structures 178:245–257. https://doi.org/10.1016/J.ENGSTRUCT.2018.09.091

    Article  Google Scholar 

  9. Boroschek RL, Legrand D (2006) Tilt motion effects on the double-time integration of linear accelerometers: an experimental approach. Bulletin of the Seismological Society of America 96(6). https://doi.org/10.1785/0120050167

  10. Boroschek RL, Baesler H, Vega C (2011) Experimental evaluation of the dynamic properties of a wharf structure. Engineering Structures 33(2). https://doi.org/10.1016/j.engstruct.2010.10.014

  11. Brincker R, Ventura CE (2015) Introduction to operational modal analysis. Wiley, Chichester

    Book  Google Scholar 

  12. Brincker R, Ventura C, Andersen P (2001a) Damping estimation by frequency domain decomposition. In: Proceedings of IMAC 19: a conference on structural dynamics, pp 1–6

    Google Scholar 

  13. Brincker R, Zhang L, Andersen P (2001b) Modal identification of output-only systems using frequency domain decomposition. Smart Materials and Structures 10(3):441–445. https://doi.org/10.1088/0964-1726/10/3/303

    Article  Google Scholar 

  14. Brownjohn JMW (2003) Ambient vibration studies for system identification of tall buildings. Earthquake Engineering and Structural Dynamics 32(1):71–95. https://doi.org/10.1002/eqe.215

    Article  Google Scholar 

  15. Brownjohn JMW, Magalhaes F, Caetano E, Cunha A (2010) Ambient vibration re-testing and operational modal analysis of the Humber Bridge. Engineering Structures 32(8):2003–2018. https://doi.org/10.1016/j.engstruct.2010.02.034

    Article  Google Scholar 

  16. Cabboi A, Magalhães F, Gentile C, Cunha A (2011) Automated modal identification and tracking: application to an iron arch bridge. Structural Control and Health Monitoring 19(1):88–106. https://doi.org/10.1002/stc

    Article  Google Scholar 

  17. Cabboi A, Magalhães F, Gentile C, Cunha Á (2017) Automated modal identification and tracking: application to an iron arch bridge. Structural Control and Health Monitoring 24(1):e1854. https://doi.org/10.1002/stc.1854

    Article  Google Scholar 

  18. Caicedo JM, Marulanda J (2011) Fast mode identification technique for online monitoring. Structural Control and Health Monitoring 18(4):416–429. https://doi.org/10.1002/stc.381

    Article  Google Scholar 

  19. Carreño RP, Boroschek RL (2011) Modal parameter variations due to earthquakes of different intensities. In: Conference proceedings of the Society for Experimental Mechanics Series, vol 4

    Google Scholar 

  20. Clinton JF, Bradford SC, Heaton TH, Favela J (2006) The observed wander of the natural frequencies in a structure. Bulletin of the Seismological Society of America 96(1):237–257. https://doi.org/10.1785/0120050052

    Article  Google Scholar 

  21. Cohen L (1995) Time-frequency analysis. Prentice Hall PTR

    Google Scholar 

  22. Crawford R, Ward HS (1964) Determination of the natural periods of buildings. Bulletin of the Seismological Society of America 54(6A):1743–1756

    Google Scholar 

  23. Cremona C, Santos JP (2018) Structural health monitoring as a big data problem. Structural Engineering Internacio:1–18. https://doi.org/10.1080/10168664.2018.1461536

  24. Cruz C, Miranda E (2017a) Evaluation of damping ratios for the seismic analysis of tall buildings. Journal of Structural Engineering 143(1):04016144. https://doi.org/10.1061/(ASCE)ST.1943-541X.0001628

    Article  Google Scholar 

  25. Cruz C, Miranda E (2017b) Evaluation of soil-structure interaction effects on the damping ratios of buildings subjected to earthquakes. Soil Dynamics and Earthquake Engineering 100(May):183–195. https://doi.org/10.1016/j.soildyn.2017.05.034

    Article  Google Scholar 

  26. Cunha A, Caetano E (2006) Experimental modal analysis of civil engineering structures. Sound and Vibration June:12–20. https://doi.org/10.1007/978-1-4020-6239-1_299

    Article  Google Scholar 

  27. Cunha A, Caetano E, Delgado R (2001) Dynamic tests on large cable-stayed bridge. Journal of Bridge Engineering 6(1):54–62. https://doi.org/10.1061/(ASCE)1084-0702(2001)6:1(54)

    Article  Google Scholar 

  28. Cury A, Crémona C, Diday E (2010) Application of symbolic data analysis for structural modification assessment. Engineering Structures 32(3):762–775

    Article  Google Scholar 

  29. Döhler M, Mevel L (2012) Fast multi-order computation of system matrices in subspace-based system identification. Control Engineering Practice 20(9):882–894. https://doi.org/10.1016/j.conengprac.2012.05.005

    Article  MATH  Google Scholar 

  30. Eyre R, Tilly GP (1977) Damping measurements on steel and composite bridges – abstract. In: Symposium on dynamic behaviour of bridges (abstract), pp 22–39

    Google Scholar 

  31. Furukawa T, Ito M, Izawa K, Noori MN (2005) System identification of base-isolated building using seismic response data. Journal of Engineering Mechanics 131(3):268–275. https://doi.org/10.1061/(ASCE)0733-9399(2005)131:3(268)

    Article  Google Scholar 

  32. Gao W, Zhou Y (2003) Eigenvalue-based algorithms for testing positive realness of SISO systems. IEEE Transactions on Automatic Control 48(11):2051–2055. https://doi.org/10.1109/TAC.2003.819302

    Article  MathSciNet  MATH  Google Scholar 

  33. Giraldo DF, Song W, Dyke SJ, Caicedo JM (2009) Modal identification through ambient vibration: comparative study. Journal Of Engineering Mechanics-Asce 135(8):759–770. https://doi.org/10.1061/(asce)0733-9399(2009)135:8(759)

    Article  Google Scholar 

  34. Gonzalez I (2013) Stochastic model updating of ballast stiffness in cold conditions. In: The 6th International conference on structural health monitoring of intelligent infrastructure

    Google Scholar 

  35. Gonzalez W, Boroschek RL (2019) Modal tracking on a building with a reduced number of sensors system. In: International modal analysis conference

    Google Scholar 

  36. González L, Boroschek R, Verdugo G (2009) Experimental study to correlate change in the structural frequency and foundation soil water content. In: Proceedings of the 17th international conference on soil mechanics and geotechnical engineering: the academia and practice of geotechnical engineering, vol 3. https://doi.org/10.3233/978-1-60750-031-5-2040

  37. Guéguen P, Langlais M, Garambois S, Voisin C, Douste-Bacqué I (2017) How sensitive are site effects and building response to extreme cold temperature? The case of the Grenoble’s (France) City Hall building. Bulletin of Earthquake Engineering 15(3):889–906. https://doi.org/10.1007/s10518-016-9995-3

    Article  Google Scholar 

  38. Guillier B, Chatelain JL, Perfettini H, Oubaiche EH, Voisin C, Bensalem R et al (2016) Building frequency fluctuations from continuous monitoring of ambient vibrations and their relationship to temperature variations. Bulletin of Earthquake Engineering 14(8):2213–2227. https://doi.org/10.1007/s10518-016-9901-z

    Article  Google Scholar 

  39. Hastie T (2011) The elements of statistical learning, data mining, inference and prediction, 2nd edn. Springer, New York

    Google Scholar 

  40. Haviland RW (1976) A study of the uncertainties in the fundamental translational periods and damping values for real buildings. Res. Rep. No. 5, Pub. No. R76-12, Department of Civil Engineering, MIT, Cambridge, p 115

    Google Scholar 

  41. Hisada T, Nakagawa K (1956) Vibration tests of various types of building structures up to failure. In: 1st World conference of earthquake engineering (WCEE)

    Google Scholar 

  42. Huang NE, Shen Z, Long SR, Wu MC, Shih HH, Zheng Q et al (1998) The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 454(1971):903–995. https://doi.org/10.1098/rspa.1998.0193

    Article  MathSciNet  MATH  Google Scholar 

  43. Hudson DE (1964) Resonance testing of full-scale structures. Journal of the Engineering Mechanics Division 90:em3

    Google Scholar 

  44. Imregun M, Ewins DJ (1995) Complex modes – origins and limits. In: 13th International modal analysis conference, pp 496–506

    Google Scholar 

  45. Iwan WD, Cifuentes A (1986) A model for system identification of degrading structures. Eathquake Engineering and Structural Dynamics 14(December 1985):877–890

    Article  Google Scholar 

  46. James III, G. H., Carne, T. G., Lauffer, P., J., Lauffer, J. P. (1995). The Natural Excitation Technique (NExT) for modal parameter extraction from operating wind turbines. The International Journal of Analytical and Experimental Modal Analysis, 10(4), 260–277. SAND92-1666

    Google Scholar 

  47. Jeary AP (1986) Damping in tall buildings – a mechanism and a predictor. Earthquake Engineering & Structural Dynamics 14(5):733–750. https://doi.org/10.1002/eqe.4290140505

    Article  Google Scholar 

  48. Juang J-N, Pappa RS (1985) An eigensystem realization algorithm for modal parameter identification and model reduction. Journal of Guidance, Control, and Dynamics 8(5):620–627. https://doi.org/10.2514/3.20031

    Article  MATH  Google Scholar 

  49. Kaya Y, Safak E (2015) Real-time analysis and interpretation of continuous data from structural health monitoring (SHM) systems. Bulletin of Earthquake Engineering 13(3):917–934. https://doi.org/10.1007/s10518-014-9642-9

    Article  Google Scholar 

  50. Kaya Y, Kocakaplan S, Şafak E (2015) System identification and model calibration of multi-story buildings through estimation of vibration time histories at non-instrumented floors. Bulletin of Earthquake Engineering 13(11):3301–3323. https://doi.org/10.1007/s10518-015-9774-6

    Article  Google Scholar 

  51. Keightley WO, Housner GW, Hudson DE (1961) Vibration tests of the Encino Dam intake tower. Technical report: Caltech EERL.1961.003. California Institute of Technology

    Google Scholar 

  52. Li QS, Yang K, Wong CK, Jeary AP (2003) The effect of amplitude-dependent damping on wind-induced vibrations of a super tall building. Journal of Wind Engineering and Industrial Aerodynamics 91(9):1175–1198. https://doi.org/10.1016/S0167-6105(03)00080-1

    Article  Google Scholar 

  53. Loh C-H, Weng J-H, Chen C-H, Lu K-C (2013) System identification of mid-story isolation building using both ambient and earthquake response data. Structural Control and Health Monitoring 20(2):139–155. https://doi.org/10.1002/stc.479

    Article  Google Scholar 

  54. Magalhães F (2010) Operational modal analysis for testing and monitoring of bridges and special structures. University of Porto, Faculty of Engineering

    Google Scholar 

  55. Magalhães F, Cunha Á, Caetano E (2009) Online automatic identification of the modal parameters of a long span arch bridge. Mechanical Systems and Signal Processing 23(2):316–329. https://doi.org/10.1016/j.ymssp.2008.05.003

    Article  Google Scholar 

  56. Magalhães F, Cunha Á, Caetano E, Brincker R (2010) Damping estimation using free decays and ambient vibration tests. Mechanical Systems and Signal Processing 24(5):1274–1290. https://doi.org/10.1016/j.ymssp.2009.02.011

    Article  Google Scholar 

  57. Mendes P (2010) Observação e análise do comportamento dinâmico de barragens de betão. Faculdade de Engenharia da Universidade do Porto

    Google Scholar 

  58. Milligan G, Cooper M (1985) An Examination of Procedures for Determining the Number of Clusters in a Data Set. Psychometrika 50(2):159–179

    Article  Google Scholar 

  59. Moroni MO, Sarrazin M, Boroschek R (1998) Experiments on a base-isolated building in Santiago, Chile. Engineering Structures 20(8). https://doi.org/10.1016/S0141-0296(97)00086-2

  60. Murray TM, Allen DE, Ungar EE (2003) Floor vibrations due to human activity. Steel Design Guide Series 11, D811(10M797)

    Google Scholar 

  61. Nagarajaiah S, Xiaohong S (2000) Response of base-isolated USC hospital building in Northridge earthquake. Journal of Structural Engineering 126(10):1177–1186. https://doi.org/10.1061/(ASCE)0733-9445(2000)126:10(1177)

    Article  Google Scholar 

  62. Peeters B, De Roeck G (1999) Reference-based stochastic subspace identification for output-only modal analysis. Mechanical Systems and Signal Processing 13(6):855–878. https://doi.org/10.1006/mssp.1999.1249

    Article  Google Scholar 

  63. Peeters B, De Roeck G (2001) Stochastic system identification for operational modal analysis: a review. Journal of Dynamic Systems, Measurement, and Control 123(4):659. https://doi.org/10.1115/1.1410370

    Article  Google Scholar 

  64. Peeters B, Ventura CE (2003) Comparative study of modal analysis techniques for bridge dynamic characteristics. Mechanical Systems and Signal Processing 17(5):965–988. https://doi.org/10.1006/mssp.2002.1568

    Article  Google Scholar 

  65. Peeters B, Maeck J, De Roeck G (2000) Dynamic monitoring of the Z24-Bridge: separating temperature effects from damage. In: Proceeding of the European COST F3 conference on system identification structural health monitoring, November 2015, pp 377–386

    Google Scholar 

  66. Peeters B, Van der Auweraer H, Guillaume P, Leuridan J (2004) The PolyMAX frequency-domain method: a new standard for modal parameter estimation? Shock and Vibration 11(3–4):395–409. https://doi.org/10.1155/2004/523692

    Article  Google Scholar 

  67. Pereira S, Magalhães F, Gomes JP, Cunha Á, Lemos JV (2018) Dynamic monitoring of a concrete arch dam during the first filling of the reservoir. Engineering Structures 174(July):548–560. https://doi.org/10.1016/j.engstruct.2018.07.076

    Article  Google Scholar 

  68. Rainieri C, Fabbrocino G (2014) Operational modal analysis of civil engineering structures. Springer, New York. https://doi.org/10.1007/978-1-4939-0767-0

    Book  Google Scholar 

  69. Rendón E, Abundez I, Arizmendi A, Quiroz EM (2011) Internal versus external cluster validation indexes. International Journal of Computers and Communications 5(1):158–163

    Google Scholar 

  70. Reynders E (2012) System identification methods for (operational) modal analysis: review and comparison. Archives of Computational Methods in Engineering 19(1):51–124. https://doi.org/10.1007/s11831-012-9069-x

    Article  MathSciNet  MATH  Google Scholar 

  71. Reynders E, Houbrechts J, De Roeck G (2012) Fully automated (operational) modal analysis. Mechanical Systems and Signal Processing 29:228–250

    Article  Google Scholar 

  72. Rodrigues J (2004) Identificação Modal Estocástica Métodos de análise e aplicações em estruturas de engenharia civil. University of Porto, Faculty of Engineering

    Google Scholar 

  73. Rodrigues J, Brincker R, Andersen P (2004) Improvement of frequency domain output-only modal identification from the application of the random decrement technique. In: Proceedings of the 23rd international modal analysis conference

    Google Scholar 

  74. Rousseeuw P (1987) Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. Journal of Computational and Applied Mathematics 20(1):53–65

    Article  Google Scholar 

  75. Ryan KL, Polanco J (2008) Problems with Rayleigh damping in base-isolated buildings. Journal of Structural Engineering 134(11):1780–1784. https://doi.org/10.1061/(ASCE)0733-9445(2008)134:11(1780)

    Article  Google Scholar 

  76. Safak E (1989) Adaptive modeling, identification, and control of dynamic structural systems. II: applications. Journal of Engineering Mechanics 115(11):2406–2426. https://doi.org/10.1061/(ASCE)0733-9399(1989)115:11(2406)

    Article  Google Scholar 

  77. Şafak E (1989) Adaptive modeling, identification, and control of dynamic structural systems. I: theory. Journal of Engineering Mechanics 115(11):2386–2405. https://doi.org/10.1061/(ASCE)0733-9399(1989)115:11(2386)

    Article  Google Scholar 

  78. Şafak E (1991) Identification of linear structures using discrete-time filters. Journal of Structural Engineering 117(10):3064–3085. https://doi.org/10.1061/(ASCE)0733-9445(1991)117:10(3064)

    Article  Google Scholar 

  79. Santos JP (2014) Smart structural health monitoring techniques for novelty identification in civil engineering structures. PhD thesis. Instituto Superior Técnico – University of Lisbon

    Google Scholar 

  80. Santos J, Crémona C, Orcesi AD, Silveira P (2013a) Multivariate statistical analysis for early damage detection. Engineering Structures 56:273–285

    Article  Google Scholar 

  81. Santos J, Crémona C, Orcesi A, Silveira P (2013b) Baseline-free real-time novelty detection using vibration-based symbolic features. In: EVACES’13, Ouro Preto, pp 1–8

    Google Scholar 

  82. Santos J, Orcesi AD, Crémona C, Silveira P (2015) Baseline-free real-time assessment of structural changes. Structure and Infrastructure Engineering: Maintenance, Management, Life-Cycle Design and Performance 11(2):145–161. https://doi.org/10.1080/15732479.2013.858169

    Article  Google Scholar 

  83. Santos JP, Cremona C, Silveira P, Calado L (2016a) Real-time damage detection based on pattern recognition. Structural Concrete. https://doi.org/10.1002/suco.201500092

  84. Santos J, Mata J, Cremona C, Silveira P (2016b) Detection of structural changes using symbolic representations of stochastic subspace identification estimates. In: 8th Workshop on structural health monitoring, pp 1–10

    Google Scholar 

  85. Santos JP, Crémona C, Silveira P (2018) Automatic modal operational analysis of a long-span suspended bridge using pattern recognition. In: 40th IABSE symposium, 19–21 September 2018, Nantes, France. Tomorrow’s megastructures. IABSE, p 8

    Google Scholar 

  86. Shi W, Shan J, Lu X (2012) Modal identification of Shanghai World Financial Center both from free and ambient vibration response. Engineering Structures 36:14–26. https://doi.org/10.1016/j.engstruct.2011.11.025

    Article  Google Scholar 

  87. Silva R, Rodrigues J (2011) Influência da altura de água nas frequências de vibração de pontes com pilares submersos. Lisbon

    Google Scholar 

  88. Siringoringo DM, Fujino Y (2018) Seismic response of a suspension bridge: insights from long-term full-scale seismic monitoring system. Structural Control and Health Monitoring June:e2252. https://doi.org/10.1002/stc.2252

    Article  Google Scholar 

  89. Spence SMJ, Kareem A (2014) Tall buildings and damping: a concept-based data-driven model. Journal of Structural Engineering 140(5):04014005. https://doi.org/10.1061/(ASCE)ST.1943-541X.0000890

    Article  Google Scholar 

  90. Stewart JP, Conte JP, Aiken ID (1999) Observed behavior of seismically isolated buildings. Journal of Structural Engineering 125(9):955–964. https://doi.org/10.1061/(ASCE)0733-9445(1999)125:9(955)

    Article  Google Scholar 

  91. Stockwell RG, Mansinha L, Lowe RP (1996) Localization of the complex spectrum: the S transform. IEEE Transactions on Signal Processing 44(4):998–1001. https://doi.org/10.1109/78.492555

    Article  Google Scholar 

  92. Su J, Xia Y, Ni Y, Zhou L, Su C (2017) Field monitoring and numerical simulation of the thermal actions of a supertall structure. Structural Control and Health Monitoring 24(4):e1900. https://doi.org/10.1002/stc.1900

    Article  Google Scholar 

  93. Takeuchi M (1960) Vibrational characteristics of buildings, part I – vibrational characteristics of actual buildings determined by actual tests. In: Proceedings of the 2nd world conference on earthquake engineering, Tokyo

    Google Scholar 

  94. Tamura Y, Yoshida A (2008) Amplitude dependency of damping in buildings. In: Structures congress 2008. American Society of Civil Engineers, Reston, pp 1–10. https://doi.org/10.1061/41000(315)39

    Chapter  Google Scholar 

  95. Tamura Y, Suda K, Sasaki A (2000) Damping in buildings for wind resistant design. In: Proceedings of the international symposium on wind and structures for the 21st century, pp 115–130

    Google Scholar 

  96. Theodoridis S, Koutroumbas K (2009) Pattern Recognition, 4th edn. Elsevier, London

    MATH  Google Scholar 

  97. Uebayashi H, Nagano M, Hida T, Tanuma T, Yasui M, Sakai S (2016) Evaluation of the structural damage of high-rise reinforced concrete buildings using ambient vibrations recorded before and after damage. Earthquake Engineering & Structural Dynamics 45(2):213–228. https://doi.org/10.1002/eqe.2624

    Article  Google Scholar 

  98. Van Overschee P, De Moor B, Sista E (1994) N4SID: subspace algorithms for the identification of combined deterministic-stochastic systems. Automatica 30:75–93. https://doi.org/10.1016/0005-1098(94)90230-5

    Article  MATH  Google Scholar 

  99. Van Overschee P, De Moor B, Van Overschee P, De Moor B (1996) Subspace identification for linear system: theory – implementation – applications. In: Conference proceedings of the international conference of IEEE Engineering in Medicine and Biology Society, 2008, pp 4427–4430. https://doi.org/10.1109/IEMBS.2008.4650193

  100. Verhaegen M (1994) Identification of the deterministic part of MIMO state space models given in innovations form from input-output data. Automatica 30(1):61–74. https://doi.org/10.1016/0005-1098(94)90229-1

    Article  MathSciNet  MATH  Google Scholar 

  101. Ward HS, Crawford R (1966) Wind-induced vibrations and building modes. Bulletin of the Seismological Society of America 56(4):793–813

    Google Scholar 

  102. Yan A, Kerschen G, De Boe P, Golinval J-C (2005a) Structural damage diagnosis under varying environmental conditions – part II: local PCA for non-linear cases. Mechanical Systems and Signal Processing 19(4):865–880

    Article  Google Scholar 

  103. Yan A, Kerschen G, De Boe P, Golinval J-C (2005b) Structural damage diagnosis under varying environmental conditions – part I: a linear analysis. Mechanical Systems and Signal Processing 19(4):865–880

    Article  Google Scholar 

  104. Yoshimoto R, Mita A, Okada K (2005) Damage detection of base-isolated buildings using multi-input multi-output subspace identification. Earthquake Engineering and Structural Dynamics 34(3):307–324. https://doi.org/10.1002/eqe.435

    Article  Google Scholar 

  105. Yousefianmoghadam S, Behmanesh I, Stavridis A, Moaveni B, Nozari A, Sacco A (2018) System identification and modeling of a dynamically tested and gradually damaged 10-story reinforced concrete building. Earthquake Engineering and Structural Dynamics 47(1):25–47. https://doi.org/10.1002/eqe.2935

    Article  Google Scholar 

  106. Zembaty Z, Kowalski M, Pospisil S (2006) Dynamic identification of a reinforced concrete frame in progressive states of damage. Engineering Structures 28(5):668–681. https://doi.org/10.1016/j.engstruct.2005.09.025

    Article  Google Scholar 

  107. Zhou HF, Ni YQ, Ko JM (2011) Eliminating temperature effect in vibration-based structural damage detection. Journal of Engineering Mechanics 137(12):785–796. https://doi.org/10.1061/(ASCE)EM.1943-7889.0000273

    Article  Google Scholar 

  108. Zonno G, Aguilar R, Castañ B, Lourenç PB (2018) Preliminary validation of an automatic modal identification methodology for structural health monitoring of historical buildings. International Journal of Structural and Civil Engineering Research 7(2):144–150. https://doi.org/10.18178/ijscer.7.2.144-150

    Article  Google Scholar 

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Acknowledgments

The authors would like to acknowledge all the support given by the Department of Civil Engineering, of the University of Chile, and by the Structures Department of the Portuguese National Laboratory for Civil Engineering (LNEC).

In addition, the authors would also like to acknowledge the support given by Conicyt and Fondecyt Chile, projects 1950629, 1000912, 1070319, and the Portuguese Foundation for Science and Technology (FCT) through the SAFESUSPENSE project – POCI-01-0145-FEDER-031054 (funded by COMPETE2020, POR Lisboa and FCT).

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Authors and Affiliations

  1. University of Chile, Santiago, Chile

    Ruben Boroschek

  2. Structural Monitoring Unit, National Laboratory for Civil Engineering, Lisboa, Portugal

    Joao Pedro Santos

Authors
  1. Ruben Boroschek
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  2. Joao Pedro Santos
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Correspondence to Ruben Boroschek .

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Editors and Affiliations

  1. University of Cincinnati, Cincinnati, OH, USA

    R. Allemang

  2. Mechanical Engineering, University of Massachusetts Lowell, Lowell, MA, USA

    Peter Avitabile

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Boroschek, R., Santos, J.P. (2020). Civil Structural Testing. In: Allemang, R., Avitabile, P. (eds) Handbook of Experimental Structural Dynamics. Springer, New York, NY. https://doi.org/10.1007/978-1-4939-6503-8_29-1

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  • DOI: https://doi.org/10.1007/978-1-4939-6503-8_29-1

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  • Print ISBN: 978-1-4939-6503-8

  • Online ISBN: 978-1-4939-6503-8

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