메뉴 건너뛰기




Volumn 7, Issue 9, 2014, Pages 1750-1764

Frequency response function-based structural damage identification using artificial neural networks-A review

Author keywords

Artificial Neural Networks (ANNs); Back Propagation Neural Network (BPNN); Damage identification; Frequency Response Functions (FRFs); Structural Health Monitoring (SHM)

Indexed keywords


EID: 84897387414     PISSN: 20407459     EISSN: 20407467     Source Type: Journal    
DOI: 10.19026/rjaset.7.459     Document Type: Article
Times cited : (25)

References (79)
  • 1
    • 0035452198 scopus 로고    scopus 로고
    • The Effect of corrosion on the natural frequency and modal damping of reinforced concrete beams
    • Abdul-Razak, H. and F.C. Choi, 2001. The Effect of corrosion on the natural frequency and modal damping of reinforced concrete beams. Eng.Struct., 23: 1126-1133.
    • (2001) Eng.Struct. , vol.23 , pp. 1126-1133
    • Abdul-Razak, H.1    Choi, F.C.2
  • 4
    • 84897440027 scopus 로고    scopus 로고
    • Vibration-based damage detection of slab structure using artificial neural network.Technol
    • Bakhary, N., 2006. Vibration-based damage detection of slab structure using artificial neural network.Technol. J. Univ., Technol. Malaysia., 44(B): 17-30.
    • (2006) J. Univ., Technol. Malaysia. , vol.44 , Issue.B , pp. 17-30
    • Bakhary, N.1
  • 5
    • 35448951634 scopus 로고    scopus 로고
    • Damage detection using artificial neural network with consideration of uncertainties
    • Bakhary, N., H. Hao and A.J. Deeks, 2007. Damage detection using artificial neural network with consideration of uncertainties. Eng. Struct., 29: 2806-2815.
    • (2007) Eng. Struct. , vol.29 , pp. 2806-2815
    • Bakhary, N.1    Hao, H.2    Deeks, A.J.3
  • 6
    • 0035053250 scopus 로고    scopus 로고
    • A Comparison of modal properties derived from forced and output-only measurement for a reinforced concrete highway bridge
    • Bolton, R., N. Stubbs, C. Sikorsky and S. Choi, 2001. A Comparison of modal properties derived from forced and output-only measurement for a reinforced concrete highway bridge. Proceedings of the IMAC-XIX: A Conference on Structural Dynamic, 1: 857-863.
    • (2001) Proceedings of the IMAC-XIX: A Conference on Structural Dynamic , vol.1 , pp. 857-863
    • Bolton, R.1    Stubbs, N.2    Sikorsky, C.3    Choi, S.4
  • 7
    • 29144513019 scopus 로고    scopus 로고
    • Damage Detection in Steel Bridge Using Artificial Neural Network and Signal Anomaly Index
    • Balageas, D.L. (Ed.), DEStech Publications, Lancaster, PA
    • Chang, S.P., J. Lee and S. Kim, 2002. Damage Detection in Steel Bridge Using Artificial Neural Network and Signal Anomaly Index. In: Balageas, D.L. (Ed.), Structural Health Monitoring. DEStech Publications, Lancaster, PA, pp: 718-725.
    • (2002) Structural Health Monitoring , pp. 718-725
    • Chang, S.P.1    Lee, J.2    Kim, S.3
  • 8
    • 0028466322 scopus 로고
    • Damage detection using neural networks: An initial experimental study on de bonded beams
    • Chaudhry, Z. and A.J. Ganino, 1994. Damage detection using neural networks: An initial experimental study on de bonded beams. Intell. Mater. Syst.Struct., 5: 585.
    • (1994) Intell. Mater. Syst.Struct. , vol.5 , pp. 585
    • Chaudhry, Z.1    Ganino, A.J.2
  • 10
    • 84968470212 scopus 로고
    • An algorithm for the machine calculation of complex fourier series.Math
    • Cooley, J.W. and J.W. Tukey, 1965. An algorithm for the machine calculation of complex fourier series.Math. Comput., 19: 297-301.
    • (1965) Comput. , vol.19 , pp. 297-301
    • Cooley, J.W.1    Tukey, J.W.2
  • 16
    • 84856641370 scopus 로고    scopus 로고
    • Implementation of BPnetwork using frequency response function as networks input data
    • Fang, J.Q. and G.Q. Jiao, 2007. Implementation of BPnetwork using frequency response function as networks input data. Comput. Simulat., 3(21-30).
    • (2007) Comput. Simulat. , vol.3 , Issue.21-30
    • Fang, J.Q.1    Jiao, G.Q.2
  • 17
    • 23144446338 scopus 로고    scopus 로고
    • Structural damage detection using neural network with learning rate improvement
    • Fang, X., H. Luo and J. Tang, 2005. Structural damage detection using neural network with learning rate improvement. Comput. Struct., 83: 2150-2161.
    • (2005) Comput. Struct. , vol.83 , pp. 2150-2161
    • Fang, X.1    Luo, H.2    Tang, J.3
  • 18
  • 19
    • 0036487714 scopus 로고    scopus 로고
    • A path load parametric analysis using neural networks.Construct
    • Fonseca, E.T. and P.G.S. Vellasco, 2003. A path load parametric analysis using neural networks.Construct. Steel Res., 59: 251-267.
    • (2003) Steel Res. , vol.59 , pp. 251-267
    • Fonseca, E.T.1    Vellasco, P.G.S.2
  • 21
    • 84455181467 scopus 로고    scopus 로고
    • Application of combined artificial neural networks and modal analysis for structural damage identification in bridge girder
    • Hakim, S.J.S. and H. Abdul Razak, 2011. Application of combined artificial neural networks and modal analysis for structural damage identification in bridge girder. Int. J. Phys. Sci., 6(35): 7991-8001.
    • (2011) Int. J. Phys. Sci. , vol.6 , Issue.35 , pp. 7991-8001
    • Hakim, S.J.S.1    Abdul Razak, H.2
  • 22
    • 84875424199 scopus 로고    scopus 로고
    • Adaptive Neuro Fuzzy Inference System (ANFIS) and Artificial Neural Networks (ANNs) for structural damage identification
    • Hakim, S.J.S. and H. Abdul Razak, 2013a. Adaptive Neuro Fuzzy Inference System (ANFIS) and Artificial Neural Networks (ANNs) for structural damage identification. Struct. Eng. Mech., 45(6): 779-802.
    • (2013) Struct. Eng. Mech. , vol.45 , Issue.6 , pp. 779-802
    • Hakim, S.J.S.1    Abdul Razak, H.2
  • 23
    • 84876835623 scopus 로고    scopus 로고
    • Structural damage detection of steel bridge girder using artificial neural networks and finite element models
    • (In Print)
    • Hakim, S.J.S. and H. Abdul Razak, 2013b. Structural damage detection of steel bridge girder using artificial neural networks and finite element models. Steel Compos. Struct., 4(14), (In Print).
    • (2013) Steel Compos. Struct. , vol.4 , Issue.14
    • Hakim, S.J.S.1    Abdul Razak, H.2
  • 24
    • 79956213944 scopus 로고    scopus 로고
    • Application of artificial neural networks to predict compressive strength of high strength concrete
    • Hakim, S.J.S., J. Noorzaei, M.S. Jaafar, M. Jameel and M.H. Mohammad, 2011. Application of artificial neural networks to predict compressive strength of high strength concrete. Int. J. Phys. Sci., 6(5): 975-981.
    • (2011) Int. J. Phys. Sci. , vol.6 , Issue.5 , pp. 975-981
    • Hakim, S.J.S.1    Noorzaei, J.2    Jaafar, M.S.3    Jameel, M.4    Mohammad, M.H.5
  • 26
    • 84897453302 scopus 로고    scopus 로고
    • Application of artificial neural network for modeling benefit to cost ratio of broiler farms in tropical regions of Iran
    • Heidari, M.D., M. Omid and A. Akram, 2011. Application of artificial neural network for modeling benefit to cost ratio of broiler farms in tropical regions of Iran. Res. J. Appl. Sci. Eng.Technol., 3(6): 546-552.
    • (2011) Res. J. Appl. Sci. Eng.Technol. , vol.3 , Issue.6 , pp. 546-552
    • Heidari, M.D.1    Omid, M.2    Akram, A.3
  • 27
    • 0005231771 scopus 로고
    • Finite element model updating using frequency response function data-I: Theory and initial investigation.Mech
    • Imregun, M., W.J. Visser and D.J. Ewins, 1995. Finite element model updating using frequency response function data-I: Theory and initial investigation.Mech. Syst. Signal Process., 9: 187-202.
    • (1995) Syst. Signal Process. , vol.9 , pp. 187-202
    • Imregun, M.1    Visser, W.J.2    Ewins, D.J.3
  • 28
    • 33745946230 scopus 로고    scopus 로고
    • Determination of damage location in RC beams using mode shape derivatives
    • Ismail, Z., H. Abdul Razak and A.G. Abdul Rahman, 2006. Determination of damage location in RC beams using mode shape derivatives. Eng. Struct., 28: 1566-1573.
    • (2006) Eng. Struct. , vol.28 , pp. 1566-1573
    • Ismail, Z.1    Abdul Razak, H.2    Abdul Rahman, A.G.3
  • 29
    • 34848844075 scopus 로고    scopus 로고
    • Damage detection for framed RCC buildings using ANN modeling
    • Kanwar, V., N. Kwatra and P. Aggarwal, 2007.Damage detection for framed RCC buildings using ANN modeling. Int. J. Damage Mech., 16: 457-472.
    • (2007) Int. J. Damage Mech. , vol.16 , pp. 457-472
    • Kanwar, V.1    Kwatra, N.2    Aggarwal, P.3
  • 30
    • 77957315843 scopus 로고    scopus 로고
    • System identification of concrete gravity dams using artificial neural networks based on a hybrid finite element-boundary element approach.Eng
    • Karimi, I., N. Khaji, M.T. Ahmadi and M. Mirzayee, 2010. System identification of concrete gravity dams using artificial neural networks based on a hybrid finite element-boundary element approach.Eng. Struct., 32: 3583-3591.
    • (2010) Struct. , vol.32 , pp. 3583-3591
    • Karimi, I.1    Khaji, N.2    Ahmadi, M.T.3    Mirzayee, M.4
  • 31
    • 84863563314 scopus 로고    scopus 로고
    • Experimental investigation of local damage detection on a 1/15 scale model of a suspension bridge deck
    • Kim, S., 2003. Experimental investigation of local damage detection on a 1/15 scale model of a suspension bridge deck. Eng. Struct., 7(4): 461-468.
    • (2003) Eng. Struct. , vol.7 , Issue.4 , pp. 461-468
    • Kim, S.1
  • 32
    • 34547924528 scopus 로고    scopus 로고
    • Use of modal testing to identify damage on steel members
    • Kim, S. and J. Lee, 2000. Use of modal testing to identify damage on steel members. KSCE J. Civil Eng., 4(2): 75-82.
    • (2000) KSCE J. Civil Eng. , vol.4 , Issue.2 , pp. 75-82
    • Kim, S.1    Lee, J.2
  • 34
    • 85039604799 scopus 로고    scopus 로고
    • Seismic damage evaluation of a 38-storey building model using measured FRF data reduced via principal component analysis
    • Ko, J.M., X.T. Zhou and Y.Q. Ni, 2002. Seismic damage evaluation of a 38-storey building model using measured FRF data reduced via principal component analysis. Adv. Build. Technol., 2: 953-960.
    • (2002) Adv. Build. Technol. , vol.2 , pp. 953-960
    • Ko, J.M.1    Zhou, X.T.2    Ni, Y.Q.3
  • 35
    • 0037266430 scopus 로고    scopus 로고
    • Damage detection of the Z24 bridge using control charts
    • Kullaa, J., 2003. Damage detection of the Z24 bridge using control charts. Mech. Syst. Signal Process., 17(1): 163-170.
    • (2003) Mech. Syst. Signal Process. , vol.17 , Issue.1 , pp. 163-170
    • Kullaa, J.1
  • 36
    • 52749085340 scopus 로고    scopus 로고
    • The selection of pattern features for structural damage detection using an extended Bayesian ANN algorithm
    • Lam, H.F. and C.T. Ng, 2008. The selection of pattern features for structural damage detection using an extended Bayesian ANN algorithm. Eng. Struct., 30: 2762-2770.
    • (2008) Eng. Struct. , vol.30 , pp. 2762-2770
    • Lam, H.F.1    Ng, C.T.2
  • 37
    • 0037653583 scopus 로고    scopus 로고
    • Prediction of concrete strength using artificial neural networks
    • Lee, S.C., 2003. Prediction of concrete strength using artificial neural networks. Eng. Struct., 25: 849-857.
    • (2003) Eng. Struct. , vol.25 , pp. 849-857
    • Lee, S.C.1
  • 38
    • 34547882736 scopus 로고    scopus 로고
    • Structural damage detection in the frequency domain using neural networks.Intell
    • Lee, J. and S. Kim, 2007. Structural damage detection in the frequency domain using neural networks.Intell. Mater. Syst. Struct., 18: 785.
    • (2007) Mater. Syst. Struct. , vol.18 , pp. 785
    • Lee, J.1    Kim, S.2
  • 39
    • 0036143988 scopus 로고    scopus 로고
    • Frequency response function-based structural damage identification method
    • Lee, U. and J.A. Shin, 2002. Frequency response function-based structural damage identification method. Comput. Struct., 80(2): 117-132.
    • (2002) Comput. Struct. , vol.80 , Issue.2 , pp. 117-132
    • Lee, U.1    Shin, J.A.2
  • 40
    • 84862732502 scopus 로고    scopus 로고
    • T-S neural network model identification of ultra-supercritical units for superheater based on improved fcm
    • Li, Y. and Y. Fang, 2012. T-S neural network model identification of ultra-supercritical units for superheater based on improved fcm. Res. J. Appl.Sci. Eng. Technol., 4(14): 2147-2152.
    • (2012) Res. J. Appl.Sci. Eng. Technol. , vol.4 , Issue.14 , pp. 2147-2152
    • Li, Y.1    Fang, Y.2
  • 43
    • 3042608373 scopus 로고    scopus 로고
    • A two-level neural network approach for dynamic FE model updating including damping
    • Lu, Y. and Z. Tu, 2004. A two-level neural network approach for dynamic FE model updating including damping. Sound Vib., 275: 931-952.
    • (2004) Sound Vib. , vol.275 , pp. 931-952
    • Lu, Y.1    Tu, Z.2
  • 44
    • 0000391067 scopus 로고    scopus 로고
    • Dynamic learning rate neural networks training and composite structural damage detection
    • Luo, H. and S. Hanagud, 1997. Dynamic learning rate neural networks training and composite structural damage detection. AIAA J., 35(9): 1522-1527.
    • (1997) AIAA J. , vol.35 , Issue.9 , pp. 1522-1527
    • Luo, H.1    Hanagud, S.2
  • 45
    • 0029184345 scopus 로고
    • Structural diagnostics using vibration transfer functions
    • Lyon, R., 1995. Structural diagnostics using vibration transfer functions. Sound Vib., 29: 28-31.
    • (1995) Sound Vib. , vol.29 , pp. 28-31
    • Lyon, R.1
  • 47
    • 0033717908 scopus 로고    scopus 로고
    • Damage identification using committee of neural network
    • Marwala, T., 2000. Damage identification using committee of neural network. Eng. Mech., 126(1): 43-50.
    • (2000) Eng. Mech. , vol.126 , Issue.1 , pp. 43-50
    • Marwala, T.1
  • 48
    • 0033131657 scopus 로고    scopus 로고
    • Fault identification using finite element models and neural networks
    • Marwala, T. and H.E.M. Hunt, 1999. Fault identification using finite element models and neural networks. Mech. Syst. Signal Process., 13(3): 475-490.
    • (1999) Mech. Syst. Signal Process. , vol.13 , Issue.3 , pp. 475-490
    • Marwala, T.1    Hunt, H.E.M.2
  • 49
    • 77952547031 scopus 로고    scopus 로고
    • Neural networks model and adaptive neuro-fuzzy inference system for predicting the moment capacity of ferrocement members
    • Mashrei, M.A., N. Abdulrazzaq, T.Y. Abdalla and M.S. Rahman, 2010. Neural networks model and adaptive neuro-fuzzy inference system for predicting the moment capacity of ferrocement members. Eng. Struct., 32: 1723-1734.
    • (2010) Eng. Struct. , vol.32 , pp. 1723-1734
    • Mashrei, M.A.1    Abdulrazzaq, N.2    Abdalla, T.Y.3    Rahman, M.S.4
  • 50
    • 79251649947 scopus 로고    scopus 로고
    • Interpretation of concrete dam behaviour with artificial neural network and multiple linear regression models
    • Mata, J., 2011. Interpretation of concrete dam behaviour with artificial neural network and multiple linear regression models. Eng. Struct., 33: 903-910.
    • (2011) Eng. Struct. , vol.33 , pp. 903-910
    • Mata, J.1
  • 51
    • 29144497217 scopus 로고    scopus 로고
    • Experimental investigation of seismic damage identification using PCA-compressed frequency response functions and neural networks
    • Ni, Y.Q., X.T. Zhou and J.M. Ko, 2006. Experimental investigation of seismic damage identification using PCA-compressed frequency response functions and neural networks. Sound Vib., 290(1-2): 242-263.
    • (2006) Sound Vib. , vol.290 , Issue.1-2 , pp. 242-263
    • Ni, Y.Q.1    Zhou, X.T.2    Ko, J.M.3
  • 52
    • 35648977118 scopus 로고    scopus 로고
    • An optimal architecture of artificial neural network for predicting of compressive strength of concrete
    • Noorzaei, J., S.J.S. Hakim, M.S. Jaafar, A.A.A. Abang and A.M.T. Waleed, 2007. An optimal architecture of artificial neural network for predicting of compressive strength of concrete. Indian Concrete J., 81(8): 17-24.
    • (2007) Indian Concrete J. , vol.81 , Issue.8 , pp. 17-24
    • Noorzaei, J.1    Hakim, S.J.S.2    Jaafar, M.S.3    Abang, A.A.A.4    Waleed, A.M.T.5
  • 53
    • 79953036114 scopus 로고    scopus 로고
    • Evaluation of artificial neural network performance in predicting diesel engine nox emissions
    • Obodeh, O. and C.I. Ajuwa, 2009. Evaluation of artificial neural network performance in predicting diesel engine nox emissions. Res. J. Appl. Sci.Eng. Technol., 1(3): 125-131.
    • (2009) Res. J. Appl. Sci.Eng. Technol. , vol.1 , Issue.3 , pp. 125-131
    • Obodeh, O.1    Ajuwa, C.I.2
  • 54
    • 0037526548 scopus 로고    scopus 로고
    • Damage detection in large-scale laboratory bridge models
    • Owen, J.S. and N. Haritos, 2003. Damage detection in large-scale laboratory bridge models. Key Eng.Mater. J., 245-246: 35-42.
    • (2003) Key Eng.Mater. J. , vol.245-246 , pp. 35-42
    • Owen, J.S.1    Haritos, N.2
  • 55
    • 33947209066 scopus 로고    scopus 로고
    • Damage detection in beams using spatial fourier analysis and neural networks
    • Pawar, P.M., K.V. Reddy and R. Ganguli, 2007.Damage detection in beams using spatial fourier analysis and neural networks. Intell. Mater. Syst.Struct., 18: 347-359.
    • (2007) Intell. Mater. Syst.Struct. , vol.18 , pp. 347-359
    • Pawar, P.M.1    Reddy, K.V.2    Ganguli, R.3
  • 56
    • 0001857570 scopus 로고
    • An artificial neural network approach to structural damage detection using frequency response function
    • USA.
    • Povich, C. and T.W. Lim, 1994. An artificial neural network approach to structural damage detection using frequency response function. Proceeding of the AIAA Adaptive Structures Forum, USA.
    • (1994) Proceeding of the AIAA Adaptive Structures Forum
    • Povich, C.1    Lim, T.W.2
  • 57
    • 35048884121 scopus 로고    scopus 로고
    • Substructural damage detection using neural networks and ICA.Lect
    • Qu, F., D. Zou and X. Wang, 2004. Substructural damage detection using neural networks and ICA.Lect. Notes Comput. Sc., 3173: 750-754.
    • (2004) Notes Comput. Sc. , vol.3173 , pp. 750-754
    • Qu, F.1    Zou, D.2    Wang, X.3
  • 58
    • 0029405055 scopus 로고
    • A neural network approach for damage detection and identification of structures
    • Rhim, J. and S.W. Lee, 1995. A neural network approach for damage detection and identification of structures. Comput. Mech., 16: 437-443.
    • (1995) Comput. Mech. , vol.16 , pp. 437-443
    • Rhim, J.1    Lee, S.W.2
  • 59
    • 0001614845 scopus 로고
    • A probabilistic resource allocating network for novelty detection
    • Roberts, S. and L. Tarassenko, 1994. A probabilistic resource allocating network for novelty detection.Neural Comput., 6: 270-84.
    • (1994) Neural Comput. , vol.6 , pp. 270-284
    • Roberts, S.1    Tarassenko, L.2
  • 60
    • 70349127326 scopus 로고    scopus 로고
    • Crack detection in beam-like structures
    • Rosales, M.B., C.P. Filipich and F.S. Buezas, 2009.Crack detection in beam-like structures. Eng.Struct., 31: 2257-2264.
    • (2009) Eng.Struct. , vol.31 , pp. 2257-2264
    • Rosales, M.B.1    Filipich, C.P.2    Buezas, F.S.3
  • 61
    • 0022471098 scopus 로고
    • Learning representations by back propagation errors
    • Rumelhart, D.E., R.J. Williams and G.E. Hinton, 1986.Learning representations by back propagation errors. Nature, 323: 533-536.
    • (1986) Nature , vol.323 , pp. 533-536
    • Rumelhart, D.E.1    Williams, R.J.2    Hinton, G.E.3
  • 62
    • 0003698254 scopus 로고
    • Ph.D. Thesis, Aalborg University, Department of Building Technology and Structural Engineering, Denmark
    • Rytter, A., 1993.Vibration based inspection of civil engineering structures. Ph.D. Thesis, Aalborg University, Department of Building Technology and Structural Engineering, Denmark.
    • (1993) Vibration based inspection of civil engineering structures
    • Rytter, A.1
  • 63
    • 0242658761 scopus 로고    scopus 로고
    • Quantification and localization of damage in beam-like structures by using artificial neural networks with experimental validation
    • Sahin, M. and R.A. Shenoi, 2003. Quantification and localization of damage in beam-like structures by using artificial neural networks with experimental validation. Eng. Struct., 25: 1785-1802.
    • (2003) Eng. Struct. , vol.25 , pp. 1785-1802
    • Sahin, M.1    Shenoi, R.A.2
  • 64
    • 0031237412 scopus 로고    scopus 로고
    • Detection of structural damage through changes in frequency: A review
    • Salawu, O.S., 1997. Detection of structural damage through changes in frequency: A review. Eng.Struct., 19(9): 718-723.
    • (1997) Eng.Struct. , vol.19 , Issue.9 , pp. 718-723
    • Salawu, O.S.1
  • 68
    • 0029221045 scopus 로고
    • DIGNET: An unsupervised learning algorithm for clustering and data fusion
    • Thomopoulos, S.C.A., D.K. Bougoulias and C.D. Wann, 1995. DIGNET: An unsupervised learning algorithm for clustering and data fusion.IEEE T. Aero. Elec. Sys., AES-31(1): 21-38.
    • (1995) IEEE T. Aero. Elec. Sys. , vol.AES 31 , Issue.1 , pp. 21-38
    • Thomopoulos, S.C.A.1    Bougoulias, D.K.2    Wann, C.D.3
  • 69
    • 0026822025 scopus 로고
    • Use of neural networks in detection of structural damage.Comput
    • Wu, X., J. Ghaboussi and J.H. Garret, 1992. Use of neural networks in detection of structural damage.Comput. Struct., 41(4): 649-659.
    • (1992) Struct. , vol.41 , Issue.4 , pp. 649-659
    • Wu, X.1    Ghaboussi, J.2    Garret, J.H.3
  • 70
    • 0036569746 scopus 로고    scopus 로고
    • Decentralized parametric damage based on neural networks
    • Wu, Z.S., B. Xu and K. Yokoyama, 2002.Decentralized parametric damage based on neural networks. Comput-Aided Civ. Inf., 17: 175-184.
    • (2002) Comput-Aided Civ. Inf. , vol.17 , pp. 175-184
    • Wu, Z.S.1    Xu, B.2    Yokoyama, K.3
  • 71
    • 33646745520 scopus 로고    scopus 로고
    • Damage detection in a girder bridge by artificial neural network technique
    • Xu, H. and J. Humar, 2006. Damage detection in a girder bridge by artificial neural network technique. Comput-Aided Civ. Inf., 21: 450-464.
    • (2006) Comput-Aided Civ. Inf. , vol.21 , pp. 450-464
    • Xu, H.1    Humar, J.2
  • 72
    • 15844393476 scopus 로고    scopus 로고
    • Damage detection in bridges using neural networks for pattern recognition of vibration signatures
    • Yeung, W.T. and J.W. Smith, 2005. Damage detection in bridges using neural networks for pattern recognition of vibration signatures. Eng. Struct., 27(5): 685-698.
    • (2005) Eng. Struct. , vol.27 , Issue.5 , pp. 685-698
    • Yeung, W.T.1    Smith, J.W.2
  • 73
    • 33644976627 scopus 로고    scopus 로고
    • On the complexity of artificial neural networks for smart structures monitoring
    • Yuen, K.V. and H.F. Lam, 2006. On the complexity of artificial neural networks for smart structures monitoring. Eng. Struct., 28: 977-984.
    • (2006) Eng. Struct. , vol.28 , pp. 977-984
    • Yuen, K.V.1    Lam, H.F.2
  • 74
    • 84873551179 scopus 로고    scopus 로고
    • Feed forward neural network for solid waste image classification
    • Zailah, W., M.A. Hannan and A. Al-Mamun, 2013. Feed forward neural network for solid waste image classification. Res. J. Appl. Sci. Eng. Technol., 5(4): 1466-1470.
    • (2013) Res. J. Appl. Sci. Eng. Technol. , vol.5 , Issue.4 , pp. 1466-1470
    • Zailah, W.1    Hannan, M.A.2    Al-Mamun, A.3
  • 75
    • 0035902057 scopus 로고    scopus 로고
    • Structural damage detection using artificial neural network and measured FRF data reduced via principal component projection
    • Zang, C. and M. Imregun, 2001a. Structural damage detection using artificial neural network and measured FRF data reduced via principal component projection. J. Sound Vib., 242(5): 813-827.
    • (2001) J. Sound Vib. , vol.242 , Issue.5 , pp. 813-827
    • Zang, C.1    Imregun, M.2
  • 76
    • 0035416998 scopus 로고    scopus 로고
    • Combined neural network and reduced FRF techniques for slight damage detection using measured response data.Archive Appl
    • Zang, C. and M. Imregun, 2001b. Combined neural network and reduced FRF techniques for slight damage detection using measured response data.Archive Appl. Mech., 71(8): 525-536.
    • (2001) Mech. , vol.71 , Issue.8 , pp. 525-536
    • Zang, C.1    Imregun, M.2
  • 77
    • 0037526467 scopus 로고    scopus 로고
    • Structural health monitoring and damage assessment using measured FRFs from multiple sensors: part I: the indicator of correlation criteria
    • Zang, C., M.I. Friswell and M. Imregun, 2003.Structural health monitoring and damage assessment using measured FRFs from multiple sensors: part I: the indicator of correlation criteria.Key Eng. Mater., (245-246): 131-140.
    • (2003) Key Eng. Mater. , vol.245-246 , pp. 131-140
    • Zang, C.1    Friswell, M.I.2    Imregun, M.3
  • 78
    • 80052623458 scopus 로고    scopus 로고
    • Fault diagnosis on steel structure using artificial neural networks
    • Zapico, A. and L. Molisani, 2009. Fault diagnosis on steel structure using artificial neural networks.Mecanica Comput., 28: 181-188.
    • (2009) Mecanica Comput. , vol.28 , pp. 181-188
    • Zapico, A.1    Molisani, L.2
  • 79
    • 0032171120 scopus 로고    scopus 로고
    • Structural damage detection using artificial neural networks
    • Zhao, J., J.N. Ivan and J.T. Dewolf, 1998. Structural damage detection using artificial neural networks.Infrastruct. Syst., 4: 93-101
    • (1998) Infrastruct. Syst. , vol.4 , pp. 93-101
    • Zhao, J.1    Ivan, J.N.2    Dewolf, J.T.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.