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The main purpose of this work attempts to assemble a framework of a health monitoring system for smart structures based on ANN models. By investigating from analytical study to experimental study, the proposed framework for an ANN-based integrated system for structural monitoring and damage diagnosis is revealed adaptive and feasible. According to the study results shown in this research, they are summarized and discussed in the succeeding sections.

(1) The ANNSI model successfully identified the structural modal parameters of the specimen under various damage states from the measurements of the accelerometers, FBG sensors, and RSGs. The identified results show consistency between each of them.

(2) The induced damage can be reflected by the changes in structural modal parameters of the specimen. However, the modal parameters changes of the lower mode are not significant in the structure with slight damage.

(3) The FBG sensors do show their potentials in system identification and monitoring. The noise effect of the FBG sensors measurements is much smaller than that of the RSGs and accelerometers. This will make the identification easier when using the FBG sensors data. Furthermore, the distinguishing advantages of much less mass and great capacity of multiplexing a large number of sensors along a single fiber link make FBG sensors promising for health monitoring of practical structures.

(4) Compare with the CMS that based on the displacement mode shapes, the CSMS that based on the strain mode shapes is more sensitive to the structural damage. Moreover, the location of damage can be reflected by the sensing stations with larger value of CSMS. By using this approach, the damage location for the most simulated damage

cases can be identified.

(5) The damage detection strategy that based on the prediction errors from the monitoring networks is easy to implement without limitation on the number of sensors. The increasing prediction error from the global monitoring network in the simulation of degradation development signifies deterioration of the structural integrity. Moreover, the larger prediction errors from the decentralized neural networks indicate the locality of the structural damage.

(6) Although the damage detection method that based on the DLF and the UFN model failed to be applied to the experimental measurements due to the problem of without a suitable analytical model, the damage diagnosis of the structure can still be carried out by other proposed strategies. If a suitable analytical model is available, the damage diagnosis of the structure will be improved and enhanced.

(7) Since the methods and approaches involved in the system are mainly based on ANNs, the system is adaptive because ANNs are expected to improve their performance as they experience more episodes form the reality.

(8) The damage detection mechanism of the system was designed to integrate different diagnosis strategies to implement the similar tasks. In this way, even one of the diagnosis strategies fails to perform its duty, the system can still work properly.

(9) The system is independent of the methods used in each mechanism and is expandable.

Any effective or improved method can be added to the corresponding mechanism to enhance the performance of the whole system.

R EFERENCE

[1] Salawu, O. S., "Detection of structural damage through changes in frequency: a review,"

Engineering Structures, 19(9), pp. 718-723, 1997.

[2] Alampalli, S. and Fu, G. K., "Full scale dynamic monitoring of highway bridges,"

Structural Engineering in Natural Hazards Mitigation, 1(pp. 1602-1607, 1993.

[3] Salawu, O. S. and Williams, C., "Bridge assessment using forced-vibration testing,"

Journal of Structural Engineering, ASCE, 121(2), pp. 161-173, 1995.

[4] West, W. M., "Illustration of the use of modal assurance criterion to detect structural changes in an orbiter test specimen," Proc., 4th International Modal Analysis Conference, 1986.

[5] Lieven, N. A. J. and Ewins, D. J., "Spatial correlation of mode shapes, the coordinate modal assurance criterion (COMAC)," Proc., 5th International Modal Analysis Conference, 1988.

[6] Biswas, M., et al., "Diagnosis experiment spectral/modal analysis of highway bridges,"

The International Journal of Analytical and Experimental Modal Analysis, 5(1), pp. 33-42, 1990.

[7] Cawley, P. and Adams, R. D., "The location of defects in structures from measurements of natural frequencies," Journal of Strain Analysis, 14(2), pp. 49-57, 1979.

[8] Penny, J. E. T., et al., "Damage location in structures using vibration data," Proc., 11th Internatiional Modal Analysis Conference, Kissimee, FL., 1993.

[9] Contursi, T., et al., "A multiple-damage location assurance criterion based on natural frequency changes," Journal of Vibration and Control, 4(5), pp. 619-663, 1998.

[10] Hearn, G. and Testa, R. B., "Modal analysis for damage detection in structures," Journal of Structural Engineering, ASCE, 117(10), pp. 3042-3063, 1991.

[11] Shi, Z. Y., et al., "Structural damage localization from modal strain energy change,"

Journal of Sound and Vibration, 218(5), pp. 825-844, 1998.

[12] Shi, Z. Y., et al., "Structural damage detection from modal strain energy change," Journal of Engineering Mechanics, ASCE, 126(12), pp. 1216-1223, 2000.

[13] Zimmermann, D. C. and Kaouk, M., "Structural damage detection using a subspace rotation algorithm," Proc. AIAA 33rd Structures, Structural Dynamics, and Materials Conference, Dallas, TX, 1992.

[14] Lim, T. W. and Kashangaki, T. A. L., "Structural damage detection of space truss structures using best achievable eigenvectors," AIAA Journal, 32(5), pp. 1049-1057, 1994.

[15] Yao, G. C., et al., "Damage diagnosis of steel frames using vibrational signature analysis,"

Journal of Engineering Mechanics, ASCE, 118(9), pp. 1949-1961, 1992.

[16] Stubbs, N. and Kim, J. T., "Damage localization in structures without baseline modal parameters," AIAA Journal, 34(8), pp. 1644-1649, 1996.

[17] Topole, K. G. and Stubbs, N., "Non-destructive damage evaluation of a structure from limited modal parameters," Earthquake Engineering and Structural Dynamics, 24(12), pp.

1427-1436, 1995.

[18] Messina, A., et al., "Damage detection and localisation using natural frequency changes,"

Proc., Identification in Engineering Systems, Swansea, Wales, 1996.

[19] Messina, A., et al., "Structural damage detection by a sensitivity and statistical-based method," Journal of Sound and Vibration, 216(5), pp. 791-808, 1998.

[20] Shi, Z. Y., et al., "Damage localization by directly using incomplete mode shapes,"

Journal of Engineering Mechanics, ASCE, 126(6), pp. 656-660, 2000.

[21] Lin, C. S., "Location of modeling errors using modal test data," AIAA Journal, 33(9), pp.

1650-1654, 1995.

[22] Pandey, A. K. and Biswas, M., "Damage diagnosis of truss structures by estimation of flexibity change," International Journal of Analytical and Experimental Modal Anal, 10(2), pp. 104-117, 1995.

[23] Pandey, A. K., et al., "Damage detection from chamges in curvature mode shapes,"

Journal of Sound and Vibration, 145(2), pp. 321-332, 1991.

[24] Cornwell, P., et al., "Application of the strain energy damage detection method to plate-like structures," Journal of Sound and Vibration, 224(2), pp. 359-374, 1999.

[25] Ratcliffe, C. P., "Damage detection using a modified Laplacian operator on mode shape data," Journal of Sound and Vibration, 204(3), pp. 505-517, 1997.

[26] Wahab, M. M. A. and Roeck, D. D., "Damage detection in bridges using modal curvatures:

application to a real damage scenario," Journal of Sound and Vibration, 226(2), pp.

217-235, 1999.

[27] Ricles, J. M. and Kosmatka, J. B., "Damage detection in elastic structures using vibratory residual forces and weighted sensitivity," AIAA Journal, 30(9), pp. 2310-2316, 1992.

[28] Kosmatka, J. B. and Ricles, J. M., "Damage detection in structures by modal vibration characterization," Journal of Structural Engineering, ASCE, 125(12), pp. 1384-1392,

1999.

[29] Zimmermann, D. C. and Kaouk, M., "Structural damage detection using a minimum rank update theory," Journal of Vibration and Acoustics, ASME, 116(pp. 222-231, 1994.

[30] Kaouk, M. and Zimmermann, D. C., "Structural damage assessment using a generalized minimum rank perturbation theory," AIAA Journal, 32(4), pp. 836-842, 1994.

[31] Shi, Z. Y., et al., "Optimum sensor placement for structural damage detection," Journal of Engineering Mechanics, ASCE, 126(11), pp. 1173-1179, 2000.

[32] Koh, C. G., et al., "Damage detection of buildings: numerical and experimental studies,"

Journal of Structural Engineering, ASCE, 121(8), pp. 1155-1160, 1995.

[33] Shi, Z. Y., et al., "Improved damage quantification from elemental modal strain energy change," Journal of Engineering Mechanics, ASCE, 128(5), pp. 521-529, 2002.

[34] Golden, R. M., Mathematical methods for neural network analysis and design. MIT Press, Cambridge, MA., 1996.

[35] Adeli, H. and Hung, S. L., "A fuzzy neural network learning model for image recognition," Integrated Computer-Aided Engineering, 1(1), pp. 43-55, 1993.

[36] Adeli, H. and Hung, S. L., Machine Learning- Neural Networks, Genetic Algorithms, and Fuzzy Systems. John Wiley & Sons, New York, NY, 1995.

[37] Adeli, H. and Park, H. S., Neurocomputing in design automation. CRC Press, Boca Raton, FL, 1998.

[38] Haykin, S., Neural networks: a comprehensive foundation. Prentice-Hall, Englewood Cliffs, NJ., 1999.

[39] Ghaboussi, J., et al., "Knowledge-based modeling of material behavior with neural networks," Journal of Engineering Mechanics, ASCE, 117(1), pp. 132-153, 1991.

[40] Wu, X., et al., "Use of neural networks in detection of structural damage," Computers and Structures, 42(4), pp. 649-659, 1992.

[41] Elkordy, M. F., et al., "Neural networks trained by analytically simulated damage states,"

Journal of Computing In Civil Engineering., ASCE, 7(2), pp. 130-145, 1993.

[42] Szewezyk, P. and Hajela, P., "Damage detection in structures based on feature-sensitive neural network," Journal of Computing In Civil Engineering., ASCE, 8(2), pp. 163-178, 1994.

[43] Pandey, P. C. and Barai, S. V., "Multilayer perceptron in damage detection of bridge structures," Computers and Structures, 54(4), pp. 597-608, 1995.

[44] Pandey, P. C. and Barai, S. V., "Time-delay neural networks in damage detection of

railway bridges," Advance in Engineering Software, 28(1), pp. 1-10, 1997.

[45] Zhao, J., et al., "Structural damage detection using artificial neural networks," Journal of Infrastructure Systems, ASCE, 4(2), pp. 93-101, 1998.

[46] Masri, S. F., et al., "Application of neural networks for detection of changes in nonlinear systems," Journal of Engineering Mechanics, ASCE, 126(7), pp. 666-676, 2000.

[47] Zapico, J. L., et al., "Vibration-based damage assessment in steel frames using neural networks," Smart Materials and Structures, 10(3), pp. 553-559, 2001.

[48] Sahin, M. and Shenoi, R. A., "Quantification and localisation of damage in beam-like structures by using artificial neural networks wth experimental validation," Engineering Structures, 25(14), pp. 1785-1802, 2003.

[49] Kim, S. H., et al., "Structural monitoring system based on sensitivity analysis and a neural network," Computer-Aided Civil and Infrastructure Engineering, 15(4), pp. 309-318, 2000.

[50] Marwala, T., "Damage identification using committee of neural networks," Journal of Engineering Mechanics, ASCE, 126(1), pp. 43-50, 2000.

[51] Yun, C. B., et al., "Joint damage assessment of framed structures using a neural networks technique," Engineering Structures, 23(5), pp. 425-435, 2001.

[52] Ni, Y. Q., et al., "Constructing input vectors to nueral networks for structiral damage identification," Smart Material and Structures, 11(pp. 825-833, 2002.

[53] Tsai, C. H. and Hsu, D. S., "Diagnosis of reinforced concrete structural damage based on displacement time history using the back-propagation neural network technique," Journal of Computing In Civil Engineering., ASCE, 16(1), pp. 49-58, 2002.

[54] Wu, Z., et al., "Decentralized parameteric damage detection based on neural networks,"

Computer-Aided Civil and Infrastructure Engineering, 17(pp. 175-184, 2002.

[55] McCulloch, W. and Pitts, W., "A logical calculus of ideas imminent in nervous activity,"

Bulletin of Mathematical Biophysics, 5(pp. 115-133, 1943.

[56] Rosenblatt, F., "The perceptron: a probabilitistic model for information storage and organization in the brain," Psychological Review, 65(pp. 386-408, 1958.

[57] Rumelhart, D. E., et al., "Learning international representation by error propagation," in Parallel Distributed Processing, R. J. Williams, Ed. Cambridge, MA.: The MIT Press, 1986, pp. 318-362.

[58] Hecht-Nielsen, R., "Theory of the back propagation neural network," Proceedins of International Joint Conference on Neural Network, IEEE, 1989.

[59] Hung, S. L. and Lin, Y. L., "Application of an L-BFGS Neural Network Learning

Algorithm in Engineering Analysis and Design," Proc., The 2nd National Conf. on Struct.

Engrg., Taiwan, R.O.C., 1994.

[60] Adeli, H., "Neural network in civil engineering: 1989-2000," Compter-Aided Civil and Infrastructure Engineering, 16(2), pp. 126-142, 2001.

[61] Hung, S. L. and Jan, J. C., "Machine learning in engineering design: an unsupervised fuzzy neural network learning model," Proc. of Intelligent Information Systems, IEEE Computer Society, California, 1997.

[62] Hung, S. L. and Jan, J. C., "Machine learning in engineering analysis and design: an integrated fuzzy neural network learning model," Computer-Aided Civil and Infrastructure Engineering, 14(pp. 207-219, 1999.

[63] Hung, S. L. and Jan, J. C., "Augmented IFN Learning Model," Journal of Computing in Civil Engineering, ASCE, 14(1), pp. 15-22, 2000.

[64] Hung, S. L. and Lai, C. M., "Unsupervised fuzzy neural network structural active pulse controller," Earthquake Engineering and Structural Dynamics, 30(4), pp. 465-484, 2001.

[65] Wen, C. M. and Hung, S. L., "Unsupervised fuzzy neural network for the damage detection of structures," (Submitted to Earthquake Engineering and Structural Dynamics), 2004.

[66] Maia, N. M. M., et al., Theoretical and experimental modal analysis. Research Studies Press, Ltd., Baldock, Hertfordshire, England, 1997.

[67] He, J. and Fu, Z. F., Modal analysis. Butterworth-Heinemann, Woburn, MA, 2001.

[68] Yun, C. B. and Bahng, E. Y., "Substructural identification using neural networks,"

Computers and Structures, 77(1), pp. 41-52, 2000.

[69] Huang, C. S., "Structural identification from ambient vibration measurement using the multivariate AR model," Journal of Sound and Vibration, 241(3), pp. 337-359, 2001.

[70] Huang, C. S., "A study on techniques for analyzing ambient vibration measurement (II) - time series methods," National Center for Research on Earthquake Engineering, Taiwan, R.O.C NCREE Report No. NCREE-99-018, 1999.

[71] Law, S. S., et al., "Efficient numerical model for the damage detection of large scale engineering structures," Engineering Structures, 23(5), pp. 436-451, 2001.

Table 4.1 Specifications of the shaking table in NCTU

Item Value

Table size (m ) 2 3×3

Weight of table (kg) 5000 Max. specimen weight (kg) 10,000 Max. displacement (cm) ± 12.5

Max. velocity (cm/sec) ± 60 Max. acceleration (g) ± 1

Table 4.2 The characterizations of the experimental specimen

Item Value

Plane size (m2) 2× 2

Story height (m) 1.6

Weight (kg) ≈ 5000

Cross section of the column (mm) 125×60×6×8 Cross section of the beam (mm) 125×60×6×8 Cross section of the girder (mm) 100×50×5×7 Size of the mass block (mm) 1360×1360×32 Mass at 4th floor (kgs2/m) 117.06 Mass at 3rd floor (kgs2/m) 121.21 Mass at 2nd floor (kgs2/m) 121.21 Mass at 1st floor (kgs2/m) 121.54

Table 4.3 Analytical modal parameters of the test model in the transverse direction

Mode 1 2 3 4

Frequency (Hz) 1.18 3.48 5.45 6.80

Damping ratio (%) 5 5 5 5

4F 1.000 1.000 0.664 0.380

3F 0.879 0.011 -0.846 -0.903

2F 0.648 -0.998 -0.348 1.000

Mode shape

1F 0.332 -0.976 1.000 -0.659

Table 4.4 Specifications of the accelerometers

TYPE Axes Span (g)

A4 CrossBow CXL02LF1 X ± 2 A3 CrossBow CXL02LF1 X ± 2 A2 CrossBow CXL01LF1 X ± 1 A1 CrossBow CXL01LF1 X ± 1 Abase CrossBow CXL01LF1 X ± 1

Table 4.5 Specifications of the FBG-SLI

Optical

Number of Optical Channels 4

Maximum Number of FBG Sensors/Channel 64 (256 total across 4 channels)

Wavelength Range 1525 - 1565 nm

Absolute Accuracy +/- 5 pm (~4.2 µ) typ, +/- 10 pm max Repeatability +/- 2 pm (~1.7 µ) typ, +/- 5 pm max

Optical Power/Channel -10 dBm approx.

Dynamic Range (4 software-controlled gain settings) 30 dB

Resolution <1 pm (~0.8µ)

Scan Frequency 108 Hz max

Minimum FBG Spacing 0.5 nm

Optical Connector FC/APC

Hardware and Software

Computer Interface Card PCI or PC CARD (PCMCIA)

Interface Cable Included

FBG-IS Software for Windows

95, 98, 2000, NT and XP Included Electrical

Power Supply 95-135 VAC or 190-265 VAC, 15W Uncalibrated Analog Output - BNC Connectors Test, sync and scan

Mechanical

Operating Temperature 10o – 40oC

Dimensions 69 x 277 x 267 mm

Weight 4.1 kg

Options

Test Processor Laptop computer/data management system Custom Optical Connectors FC/SPC

Custom Computer Interface Cards ISA

Table 4.6 Center wavelength of the FBG sensors along Channel 1

FBG1 FBG2 FBG3 FBG4 FBG5 FBG6 FBG7 FBG8 Wavelength

(nm) 1542 1545 1548 1551 1554 1557 1560 1563

Table 4.7 Center wavelength of the FBG sensors along Channel 2 FBG9 FBG10 FBG11 FBG12 Wavelength

(nm) 1530 1533 1539 1536

Table 4.8 Dimension of the SC Cross section

(mm)

Area (cm2)

Mass (kg/m)

Ix (cm4)

SC-A 100×50×20×2.3 5.14 4.06 80.7 SC-B 75×45×15×2.3 4.14 3.25 37.1

Table 4.9 Characterizations of the simulated damage cases

No. Notation of the damage case

SC arrangement (1F-2F-3F)

Notation of the damage class

1 AAA A-A-A Intact

2 Dcase_BAA B-A-A Dclass_k1

3 Dcase_NAA N-A-A Dclass_k1

4 Dcase_ABA A-B-A Dclass_k2

5 Dcase_ANA A-N-A Dclass_k2

6 Dcase_AAB A-A-B Dclass_k3

7 Dcase_AAN A-A-N Dclass_k3

8 Dcase_BBA B-B-A Dclass_k1&k2

9 Dcase_BNA B-N-A Dclass_k1&k2

10 Dcase_NBA N-B-A Dclass_k1&k2

11 Dcase_NNA N-N-A Dclass_k1&k2 12 Dcase_BAB B-A-B Dclass_k1&k3

13 Dcase_BAN B-A-N Dclass_k1&k3

14 Dcase_NAB N-A-B Dclass_k1&k3

15 Dcase_NAN N-A-N Dclass_k1&k3 16 Dcase_ABB A-B-B Dclass_k2&k3 17 Dcase_ABN A-B-N Dclass_k2&k3

18 Dcase_ANB A-N-B Dclass_k2&k3

19 Dcase_ANN A-N-N Dclass_k2&k3 20 Dcase_BBB B-B-B Dclass_k1&k2&k3 21 Dcase_BBN B-B-N Dclass_k1&k2&k3

22 Dcase_NBB N-B-B Dclass_k1&k2&k3

23 Dcase_BNN B-N-N Dclass_k1&k2&k3

24 Dcase_NNB N-N-B Dclass_k1&k2&k3 25 Dcase_NNN N-N-N Dclass_k1&k2&k3

Table 4.10 Operation sequence of the shaking table tests

Case Save acc. data as Save RSG data as Save FBG data as

AAA AAA_acc AAA_RSG AAA_FBG

Dcase_BAA BAA_acc BAA_RSG BAA_FBG

Dcase_NAA NAA_acc NAA_RSG NAA_FBG

Dcase_ABA ABA_acc ABA_RSG ABA_FBG

Dcase_ANA ANA_acc ANA_RSG ANA_FBG

Dcase_AAB AAB_acc AAB_RSG AAB_FBG

Dcase_AAN AAN_acc AAN_RSG AAN_FBG

Dcase_BBA BBA_acc BBA_RSG BBA_FBG

Dcase_BNA BNA_acc BNA_RSG BNA_FBG

Dcase_NBA NBA_acc NBA_RSG NBA_FBG

Dcase_NNA NNA_acc NNA_RSG NNA_FBG

Dcase_BAB BAB_acc BAB_RSG BAB_FBG

Dcase_BAN BAN_acc BAN_RSG BAN_FBG

Dcase_NAB NAB_acc NAB_RSG NAB_FBG

Dcase_NAN NAN_acc NAN_RSG NAN_FBG

Dcase_ABB ABB_acc ABB_RSG ABB_FBG

Dcase_ABN ABN_acc ABN_RSG ABN_FBG

Dcase_ANB ANB_acc ANB_RSG ANB_FBG

Dcase_ANN ANN_acc ANN_RSG ANN_FBG

Dcase_BBB BBB_acc BBB_RSG BBB_FBG

Dcase_BBN BBN_acc BBN_RSG BBN_FBG

Dcase_NBB NBB_acc NBB_RSG NBB_FBG

Dcase_BNN BNN_acc BNN_RSG BNN_FBG

Dcase_NNB NNB_acc NNB_RSG NNB_FBG

Dcase_NNN NNN_acc NNN_RSG NNN_FBG

Table 4.11 Statistical summaries of the acceleration records I. Max. response (g)

.) max(acc

II. Standard deviation (g) .)

( std acc Case

1F 2F 3F 4F 1F 2F 3F 4F AAA 0.095 0.119 0.131 0.177 0.016 0.023 0.028 0.035 Dcase_BAA 0.096 0.129 0.140 0.200 0.018 0.027 0.034 0.041 Dcase_NAA 0.107 0.144 0.170 0.191 0.028 0.038 0.046 0.057 Dcase_ABA 0.093 0.124 0.142 0.181 0.020 0.030 0.036 0.045 Dcase_ANA 0.095 0.145 0.160 0.204 0.021 0.038 0.047 0.056 Dcase_AAB 0.091 0.126 0.137 0.178 0.019 0.028 0.034 0.042 Dcase_AAN 0.117 0.149 0.157 0.180 0.021 0.030 0.037 0.045 Dcase_BBA 0.094 0.123 0.144 0.175 0.019 0.030 0.038 0.045 Dcase_BNA 0.093 0.143 0.167 0.202 0.021 0.037 0.048 0.056 Dcase_NBA 0.106 0.143 0.166 0.207 0.028 0.042 0.052 0.062 Dcase_NNA 0.102 0.156 0.184 0.244 0.033 0.058 0.074 0.085 Dcase_BAB 0.102 0.138 0.160 0.184 0.022 0.032 0.039 0.048 Dcase_BAN 0.137 0.172 0.159 0.218 0.028 0.036 0.04 0.052 Dcase_NAB 0.104 0.148 0.192 0.216 0.027 0.043 0.054 0.063 Dcase_NAN 0.143 0.189 0.188 0.235 0.036 0.048 0.056 0.070 Dcase_ABB 0.097 0.131 0.141 0.184 0.022 0.033 0.039 0.048 Dcase_ABN 0.115 0.150 0.152 0.181 0.023 0.034 0.043 0.052 Dcase_ANB 0.095 0.161 0.171 0.225 0.023 0.040 0.051 0.060 Dcase_ANN 0.100 0.158 0.162 0.230 0.026 0.043 0.055 0.066 Dcase_BBB 0.089 0.129 0.152 0.184 0.024 0.035 0.042 0.051 Dcase_BBN 0.130 0.170 0.160 0.221 0.030 0.041 0.046 0.058 Dcase_NBB 0.103 0.159 0.201 0.227 0.036 0.059 0.074 0.087 Dcase_BNN 0.135 0.171 0.179 0.265 0.034 0.053 0.065 0.078 Dcase_NNB 0.098 0.148 0.193 0.248 0.043 0.079 0.099 0.115 Dcase_NNN 0.133 0.184 0.210 0.298 0.050 0.084 0.109 0.127

Table 4.11 (Continue) III. max(acc.) (g)

(=damage case – baseline) IV. ∆max(acc /.) baseline (%) Case

1F 2F 3F 4F 1F 2F 3F 4F

AAA / / / / / / / /

Dcase_BAA 0.001 0.010 0.009 0.023 0.6 8.3 6.9 13.0 Dcase_NAA 0.012 0.025 0.039 0.014 12.3 21.0 29.5 7.9 Dcase_ABA -0.002 0.005 0.011 0.004 -1.8 4.3 8.0 2.0 Dcase_ANA 0.000 0.026 0.029 0.027 0.2 21.7 22.1 15.0 Dcase_AAB -0.004 0.007 0.006 0.001 -3.9 6.0 4.9 0.8 Dcase_AAN 0.022 0.030 0.026 0.003 23.6 24.8 19.8 1.5 Dcase_BBA -0.001 0.004 0.013 -0.002 -1.6 3.0 10.2 -1.4 Dcase_BNA -0.002 0.024 0.036 0.025 -1.9 20.0 27.5 13.9 Dcase_NBA 0.011 0.024 0.035 0.030 11.2 20.1 27.0 16.9 Dcase_NNA 0.007 0.037 0.053 0.067 7.4 31.1 40.5 37.9 Dcase_BAB 0.007 0.019 0.029 0.007 7.5 15.9 22.1 4.1 Dcase_BAN 0.042 0.053 0.028 0.041 44.7 44.8 21.4 23.4 Dcase_NAB 0.009 0.029 0.061 0.039 9.2 24.5 46.9 22.2 Dcase_NAN 0.048 0.070 0.057 0.058 50.5 58.7 43.3 32.6 Dcase_ABB 0.002 0.012 0.010 0.007 2.6 10.3 7.3 3.7 Dcase_ABN 0.020 0.031 0.021 0.004 21.4 25.7 15.8 2.1 Dcase_ANB 0.000 0.042 0.040 0.048 0.4 35.4 30.5 26.9 Dcase_ANN 0.005 0.039 0.031 0.053 5.3 32.8 23.7 29.9 Dcase_BBB -0.006 0.010 0.021 0.007 -6.3 8.4 16.0 4.0 Dcase_BBN 0.035 0.051 0.029 0.044 36.7 43.2 22.1 25.0 Dcase_NBB 0.008 0.040 0.070 0.050 8.0 33.6 53.5 28.1 Dcase_BNN 0.040 0.052 0.048 0.088 42.1 43.7 36.6 49.7 Dcase_NNB 0.003 0.029 0.062 0.071 2.7 24.6 47.5 40.0 Dcase_NNN 0.038 0.065 0.079 0.121 40.0 54.5 60.2 68.5

Table 4.12 Statistical summaries of the strain records from the FBG sensors at BE I. Max. response (µ strain)

) max(ε

II. Standard deviation (µ strain) )

( std ε Case

FBG1 FBG3 FBG5 FBG7 FBG1 FBG3 FBG5 FBG7 AAA 238.1 184.2 143.1 109.6 44.5 36.8 28.2 20.5 Dcase_NNB 419.7 385.1 230.2 171.5 220.5 196.9 107.1 75.4 Dcase_NNN 427.8 385.8 318.0 187.0 195.7 176.9 129.8 66.9

Table 4.12 (Continue)

FBG1 FBG3 FBG5 FBG7 FBG1 FBG3 FBG5 FBG7

AAA 18.7 20.0 19.7 18.7 / / / /

Table 4.13 Statistical summaries of the strain records from FBG the sensors at TE I. Max. response (µ strain)

) max(ε

II. Standard deviation (µ strain) )

( std ε Case

FBG2 FBG4 FBG6 FBG8 FBG2 FBG4 FBG6 FBG8 AAA 241.5 188.2 151.5 111.6 46.1 37.8 30.1 20.9 Dcase_NNB 429.7 387.2 251.8 174.6 226.1 198.1 118.1 76.8 Dcase_NNN 434.72 384.2 338.5 196.8 201.2 176.7 138.7 70.8

Table 4.13 (Continue)

FBG2 FBG4 FBG6 FBG8 FBG2 FBG4 FBG6 FBG8

AAA 19.1 20.1 19.8 18.7 / / / /

Table 4.14 Statistical summaries of the strain records from the FBG sensors at BW I. Max. response (µ strain)

) max(ε

II. Standard deviation (µ strain) )

( std ε Case

FBG9 FBG10 FBG11 FBG12 FBG9 FBG10 FBG11 FBG12 AAA 250.0 190.1 142.8 98.1 47.0 38.2 28.5 19.2 Dcase_NNB 449.3 389.6 233.2 167.7 235.9 199.2 108.1 70.6 Dcase_NNN 455.1 390.6 316.5 185.3 209.9 179.1 129.5 62.7

Table 4.14 (Continue)

FBG9 FBG10 FBG11 FBG12 FBG9 FBG10 FBG11 FBG12

AAA 18.8 20.1 20.0 19.5 / / / /

Dcase_BAA 22.2 23.7 23.2 21.7 10.2 8.0 9.1 10.2 Dcase_NAA 27.5 25.7 28.0 28.3 45.0 34.9 25.7 13.3 Dcase_ABA 23.1 24.8 24.8 24.1 14.2 16.6 -4.4 11.8 Dcase_ANA 28.3 30.1 28.9 26.6 25.7 61.1 8.1 29.4 Dcase_AAB 20.1 21.3 22.1 21.0 11.3 15.6 1.3 8.9 Dcase_AAN 23.2 26.7 26.9 23.3 19.7 14.2 37.1 14.6 Dcase_BBA 23.8 25.1 25.3 23.0 24.3 19.6 11.6 10.4 Dcase_BNA 27.6 29.1 28.3 24.3 26.0 61.6 25.1 31.4 Dcase_NBA 30.5 29.1 29.0 27.7 48.7 46.0 39.0 28.4 Dcase_NNA 35.1 33.7 31.2 26.8 60.9 86.7 58.0 59.0 Dcase_BAB 24.9 25.6 26.6 24.8 29.0 26.0 8.1 16.4 Dcase_BAN 23.0 24.7 23.8 20.7 40.9 32.7 65.5 41.0 Dcase_NAB 30.4 29.5 30.8 28.3 59.3 51.2 27.2 33.3 Dcase_NAN 28.9 29.5 31.7 27.2 67.2 50.6 68.6 44.0 Dcase_ABB 24.1 26.2 26.5 24.6 15.0 15.8 4.8 10.8 Dcase_ABN 24.5 27.2 29.3 28.2 25.5 24.5 41.4 6.9 Dcase_ANB 28.1 30.4 29.1 26.2 32.2 66.0 25.6 31.8 Dcase_ANN 30.2 31.8 29.2 24.9 33.1 71.3 82.4 52.9 Dcase_BBB 25.2 25.3 26.2 24.0 32.9 33.6 18.5 26.5 Dcase_BBN 24.7 26.6 25.7 23.0 48.5 39.4 73.7 41.4 Dcase_NBB 36.3 35.9 36.2 34.4 79.7 64.7 48.7 46.1 Dcase_BNN 32.3 32.1 27.8 23.3 50.2 83.2 103.1 75.3 Dcase_NNB 52.5 51.1 46.3 42.1 79.7 104.9 63.3 70.9 Dcase_NNN 46.1 45.9 40.9 33.8 82.1 105.4 121.6 88.8

Table 4.15 Statistical summaries of the strain records from the RSGs

I. Max. response (µ strain) )

max(ε

II. Standard deviation (µ strain)

) ( std ε Case

RSG1 RSG2 RSG3 RSG4 RSG1 RSG2 RSG3 RSG4

AAA 166.4 144.9 121.3 96.2 36.6 33.5 27.5 21.1

Dcase_BAA 185.4 156.3 129.1 103.9 45.4 41.5 33.8 25.3 Dcase_NAA 236.7 196.3 152.0 112.6 73.8 57.1 47.8 34.0 Dcase_ABA 192.3 169.1 116.0 113.7 48.9 45.5 31.1 27.2 Dcase_ANA 214.7 235.9 139.4 123.9 63.5 73.7 39.9 33.6 Dcase_AAB 188.6 167.6 121.9 107.8 45.7 43.1 32.5 25.5 Dcase_AAN 203.1 167.2 169.0 112.8 49.4 46.3 46.5 26.7 Dcase_BBA 210.1 179.5 137.6 117.6 56.9 49.6 37.5 27.3 Dcase_BNA 210.9 232.8 164.2 119.6 64.9 75.2 46.4 33.9 Dcase_NBA 246.4 215.3 175.2 131.8 83.5 69.5 52.5 37.1 Dcase_NNA 266.8 273.6 188.8 136.9 119.3 114.7 73.4 50.6 Dcase_BAB 213.6 184.3 135.4 105.5 58.1 50.1 36.8 28.7 Dcase_BAN 240.7 188.6 194.1 123.8 60.1 52.0 51.9 30.3 Dcase_NAB 266.3 221.2 165.4 137.5 86.7 70.9 51.1 37.6 Dcase_NAN 278.4 221.1 201.9 134.5 89.4 72.6 72.0 40.5 Dcase_ABB 195.7 171.4 128.2 102.9 52.3 48.8 36.9 28.7 Dcase_ABN 210.7 186.2 171.5 105.8 55.7 52.2 53.1 30.6 Dcase_ANB 222.9 240.5 151.1 131.8 68.6 79.4 47.9 36.1 Dcase_ANN 225.4 243.4 212.1 129.0 73.9 85.1 69.5 38.4 Dcase_BBB 223.6 191.6 148.2 114.6 63.3 55.1 40.8 30.6 Dcase_BBN 253.7 205.6 208.3 129.7 68.7 59.3 58.2 33.9 Dcase_NBB 302.4 234.4 186.6 145.6 121.5 94.7 72.5 52.8 Dcase_BNN 255.0 260.0 244.2 147.9 96.7 99.8 81.0 45.0 Dcase_NNB 298.8 300.9 207.0 148.0 162.8 158.3 92.3 67.6 Dcase_NNN 307.1 302.7 273.2 171.8 173.4 170.7 134.2 72.6

Table 4.15 (Continue)

RSG1 RSG2 RSG3 RSG4 RSG1 RSG2 RSG3 RSG4

AAA 22.0 23.1 22.7 21.9 / / / /

Dcase_BAA 24.5 26.5 26.2 24.4 11.4 7.9 6.4 8.0 Dcase_NAA 31.2 29.1 31.4 30.2 42.3 35.5 25.4 17.0 Dcase_ABA 25.4 26.9 26.8 23.9 15.6 16.7 -4.3 18.3 Dcase_ANA 29.6 31.2 28.6 27.1 29.1 62.8 15.0 28.9 Dcase_AAB 24.2 25.7 26.6 23.6 13.4 15.7 0.6 12.1 Dcase_AAN 24.3 27.7 27.5 23.7 22.1 15.4 39.3 17.3 Dcase_BBA 27.1 27.6 27.3 23.2 26.3 23.9 13.5 22.3 Dcase_BNA 30.8 32.3 28.3 28.4 26.8 60.7 35.4 24.4 Dcase_NBA 33.9 32.3 30.0 28.1 48.1 48.6 44.5 37.1 Dcase_NNA 44.7 41.9 38.9 37.0 60.4 88.9 55.7 42.4 Dcase_BAB 27.2 27.2 27.2 27.2 28.4 27.2 11.7 9.7 Dcase_BAN 25.0 27.6 26.7 24.5 44.7 30.2 60.1 28.8 Dcase_NAB 32.5 32.0 30.9 27.3 60.0 52.7 36.4 43.0 Dcase_NAN 32.1 32.9 35.7 30.1 67.3 52.6 66.5 39.8 Dcase_ABB 26.7 28.5 28.8 27.9 17.7 18.3 5.7 7.0 Dcase_ABN 26.4 28.1 31.0 28.9 26.7 28.5 41.4 10.0 Dcase_ANB 30.8 33.0 31.7 27.4 34.0 66.0 24.6 37.0 Dcase_ANN 32.8 35.0 32.8 29.8 35.5 68.0 74.9 34.2 Dcase_BBB 28.3 28.7 27.5 26.7 34.4 32.3 22.2 19.2 Dcase_BBN 27.1 28.8 27.9 26.1 52.5 41.9 71.8 34.8 Dcase_NBB 40.2 40.4 38.8 36.3 81.8 61.8 53.9 51.4 Dcase_BNN 37.9 38.4 33.2 30.4 53.3 79.5 101.4 53.8 Dcase_NNB 54.5 52.6 44.6 45.7 79.6 107.7 70.7 53.8 Dcase_NNN 56.5 56.4 49.1 42.3 84.6 108.9 125.3 78.6

Table 5.1 Modal parameters of the test structure in healthy condition (AAA)

Mode 1 2 3 4

Frequency (Hz) 1.69 5.04 8.14 10.22 Damping ratio (%) 3.32 1.38 1.40 2.01

A4 1.000 1.000 0.427 0.288

A3 0.846 -0.130 -0.729 -0.729

A2 0.628 -0.914 -0.137 1.000

Mode shape

A1 0.336 -0.830 1.000 -0.705

Table 5.2 Modal parameters of Dcase_BAA

Mode 1 2 3 4

Frequency (Hz) 1.68 4.94 7.89 10.17

Damping ratio (%) 2.54 1.23 0.54 3.04

A4 1.000 1.000 0.557 0.254

A3 0.844 -0.100 -0.893 -0.775

A2 0.629 -0.935 -0.206 1.000

Mode shape

A1 0.340 -0.887 1.000 -0.672

MAC 1.000 0.999 0.989 0.998

Table 5.3 Modal parameters of Dcase_NAA

Mode 1 2 3 4

Frequency (Hz) 1.60 4.78 7.77 9.88

Damping ratio (%) 1.80 0.28 0.49 2.53

A4 1.000 1.000 0.582 0.317

A3 0.857 -0.035 -0.870 -0.809

A2 0.649 -0.879 -0.303 1.000

Mode shape

A1 0.370 -0.932 1.000 -0.618

MAC 1.000 0.992 0.979 0.993

Table 5.4 Modal parameters of Dcase_ABA

Mode 1 2 3 4

Frequency (Hz) 1.70 5.09 7.93 10.15

Damping ratio (%) 2.11 0.50 1.27 2.68

A4 1.000 1.000 0.449 0.249

A3 0.843 -0.149 -0.769 -0.871

A2 0.648 -0.835 -0.197 1.000

Mode shape

A1 0.344 -0.786 1.000 -0.757

MAC 1.000 0.998 0.998 0.993

Table 5.5 Modal parameters of Dcase_ANA

Mode 1 2 3 4

Frequency (Hz) 1.63 5.02 7.84 9.85 Damping ratio (%) 1.78 0.69 4.64 4.26 A4 1.000 1.000 0.415 0.388 A3 0.856 -0.121 -0.614 -0.923 A2 0.671 -0.823 -0.214 1.000 Mode shape

A1 0.326 -0.829 1.000 -0.640

MAC 0.999 0.998 0.989 0.982

Table 5.6 Modal parameters of Dcase_AAB

Mode 1 2 3 4

Frequency (Hz) 1.68 5.03 7.97 10.10 Damping ratio (%) 2.80 0.33 2.23 1.32

A4 1.000 1.000 0.609 0.277 A3 0.852 -0.119 -0.951 -0.836 A2 0.638 -0.885 -0.176 1.000 Mode shape

A1 0.337 -0.827 1.000 -0.709

MAC 1.000 1.000 0.981 0.996

Table 5.7 Modal parameters of Dcase_AAN

Mode 1 2 3 4

Frequency (Hz) 1.63 4.84 7.70 9.65 Damping ratio (%) 2.92 0.28 1.22 1.64 A4 1.000 0.955 0.692 0.284 A3 0.852 -0.042 -1.000 -0.723 A2 0.602 -1.000 -0.134 1.000 Mode shape

A1 0.317 -0.909 0.928 -0.866

MAC 1.000 0.993 0.955 0.991

Table 5.8 Modal parameters of Dcase_BBA

Mode 1 2 3 4

Frequency (Hz) 1.62 4.89 7.72 10.08 Damping ratio (%) 2.50 1.64 0.53 2.52

A4 1.000 1.000 0.558 0.351 A3 0.855 -0.075 -0.829 -0.807 A2 0.649 -0.855 -0.252 1.000 Mode shape

A1 0.354 -0.832 1.000 -0.565

MAC 1.000 0.998 0.988 0.986

Table 5.9 Modal parameters of Dcase_BNA

Mode 1 2 3 4

Frequency (Hz) 1.60 4.91 7.49 9.68 Damping ratio (%) 1.75 1.03 0.56 1.32 A4 1.000 1.000 0.532 0.361 A3 0.859 -0.086 -0.755 -0.954 A2 0.654 -0.934 -0.294 1.000 Mode shape

A1 0.319 -0.855 1.000 -0.547

MAC 1.000 0.999 0.985 0.967

Table 5.10 Modal parameters of Dcase_NBA

Mode 1 2 3 4

Frequency (Hz) 1.58 4.76 7.59 9.49 Damping ratio (%) 1.47 0.50 0.65 3.27 A4 1.000 1.000 0.564 0.312 A3 0.863 -0.025 -0.807 -0.826 A2 0.657 -0.883 -0.336 1.000 Mode shape

A1 0.363 -0.957 1.000 -0.685

MAC 1.000 0.990 0.977 0.996

Table 5.11 Modal parameters of Dcase_NNA

Mode 1 2 3 4

Frequency (Hz) 1.55 4.72 7.38 9.62 Damping ratio (%) 0.72 0.66 0.34 0.62 A4 1.000 1.000 0.520 0.352 A3 0.868 -0.014 -0.682 -0.982 A2 0.668 -0.918 -0.382 1.000 Mode shape

A1 0.349 -0.934 1.000 -0.546

MAC 1.000 0.992 0.964 0.962

Table 5.12 Modal parameters of Dcase_BAB

Mode 1 2 3 4

Frequency (Hz) 1.63 4.90 7.81 10.04 Damping ratio (%) 2.31 0.25 0.88 1.08

A4 1.000 1.000 0.610 0.294 A3 0.855 -0.063 -0.920 -0.808 A2 0.653 -0.844 -0.205 1.000 Mode shape

A1 0.355 -0.824 1.000 -0.671

MAC 1.000 0.997 0.983 0.997

Table 5.13 Modal parameters of Dcase_BAN

Mode 1 2 3 4

Frequency (Hz) 1.59 4.72 7.74 9.70 Damping ratio (%) 2.58 0.11 0.31 2.17 A4 1.000 0.985 0.675 0.246 A3 0.859 -0.014 -1.000 -0.948 A2 0.619 -1.000 -0.094 1.000 Mode shape

A1 0.335 -0.946 0.863 -0.847

MAC 1.000 0.991 0.941 0.985

Table 5.14 Modal parameters of Dcase_NAB

Mode 1 2 3 4

Frequency (Hz) 1.60 4.84 7.71 9.99 Damping ratio (%) 1.49 0.21 0.79 0.62 A4 1.000 1.000 0.632 0.289 A3 0.860 -0.044 -0.942 -0.808 A2 0.667 -0.835 -0.273 1.000 Mode shape

A1 0.371 -0.889 1.000 -0.603

MAC 0.999 0.993 0.975 0.992

Table 5.15 Modal parameters of Dcase_NAN

Mode 1 2 3 4

Frequency (Hz) 1.56 4.63 7.67 9.65 Damping ratio (%) 1.42 0.04 0.20 2.33 A4 1.000 0.978 0.698 0.282 A3 0.862 0.026 -1.000 -0.703 A2 0.627 -0.959 -0.163 1.000 Mode shape

A1 0.348 -1.000 0.803 -0.771

MAC 1.000 0.983 0.923 0.998

Table 5.16 Modal parameters of Dcase_ABB

Mode 1 2 3 4

Frequency (Hz) 1.68 5.02 7.86 10.07 Damping ratio (%) 2.31 0.31 1.67 1.12

A4 1.000 1.000 0.559 0.262 A3 0.848 -0.118 -0.871 -0.832 A2 0.637 -0.888 -0.171 1.000 Mode shape

A1 0.337 -0.815 1.000 -0.680

MAC 1.000 1.000 0.991 0.995

Table 5.17 Modal parameters of Dcase_ABN

Mode 1 2 3 4

Frequency (Hz) 1.64 4.84 7.77 9.58

Frequency (Hz) 1.64 4.84 7.77 9.58

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