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Deep BBN Learning for Health Assessment toward Decision-Making on Structures under Uncertainties

  • Probabilistic Learning for Decision-making on Civil Infrastructures
  • Published:
KSCE Journal of Civil Engineering Aims and scope

Abstract

Structural systems are often exposed to harsh environment, while these environmental factors in turn could degrade the system over time. Their health state and structural conditions are key for structural safety control and decision-making management. Although great efforts have been paid on this field, the high level of variability due to noise and other interferences, and the uncertainties associated with data collection, structural performance and in-service operational environments post great challenges in finding information to assist decision making. The machine learning techniques in recent years have been gaining increasing attentions due to their merits capturing information from statistical representation of events and thus enabling making decision. In this study, the deep Bayesian Belief Network Learning (DBBN) was used to extract structural information and probabilistically determine structural conditions. Different to conventional shallow learning that highly relies on the quality of the hand-crafted features, the deep learning is an end-to-end method to encode the information and interpret vast amount of data with minimizing or no features. A case study was conducted to address the methods for structure under viabilities and uncertainties due to operation, damage and noise interferences. Numerical results revealed that the deep learning exhibits considerably enhanced accuracy for structural diagnostics, as compared to the supervised shallow learning. With predetermined training set, the DBBN could accurately determine the structural health state in terms of damage level, which could dramatically help decision making for further structural retrofit or not. Note that the noise interference could contaminate the data representation and in turn increase the difficulty of the data mining, though the deep learning could reduce the impacts, as compared to conventional shallow learning techniques.

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References

  • Bennett, K. and Demiriz, A. (1999). “Semi-supervised support vector machines.” Advances in Neural Information Processing Systems, Vol. 11, pp. 368–374, DOI: https://doi.org/10.1016/j.neucom.2011.06.026.

    Google Scholar 

  • Bisgin, H., Kilinc, O. U., Ugur, A., Xu, X., and Tuzcu, V. (2011). “Diagnosis of long QT syndrome via support vector machines classification.” Journal of Biomedical Science and Engineering, Vol. 4, No. 4, p. 4, DOI: https://doi.org/10.4236/jbise.2011.44036.

    Article  Google Scholar 

  • Boldt, F. d. A., Rauber, T. W., and ao, F. M. V. (2013). “Feature extraction and selection for automatic fault diagnosis of rotating machinery.” http://www.lbd.dcc.ufmg.br/colecoes/eniac/2013/0011.pdf.

    Google Scholar 

  • Bornn, L., Farrar, C. R., Park, G., and Farinholt, K. (2009). “Structural health monitoring with autoregressive support vector machines.” Journal of Vibration and Acoustics, Vol. 131, No. 2, pp. 021004–021004. DOI: https://doi.org/10.1115/1.3025827.

    Article  Google Scholar 

  • Cha, Y.-J. and Buyukozturk, O. (2015). “Structural damage detection using model strain energy and hybrid multiobjective optimization.” Computer-Aided Civil and Infrastructure Engineering, Vol. 30, No. 5, pp. 347–358, DOI: https://doi.org/10.1111/mice.12122.

    Article  Google Scholar 

  • Chaturvedi, I., Cambria, E., Poria, S. and Bajpai, R. (2016). “Bayesian deep convolutional belief networks for subjectivity detection.” 2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW), DOI: https://doi.org/10.1109/ICDMW.2016.0134.

    Google Scholar 

  • Cho, K., Raiko, T., and Ilin, A. (2013). “Gaussian-Bernoulli deep Boltzmann machine.” The 2013 International Joint Conference on Neural Networks (IJCNN), DOI: https://doi.org/10.1109/ijcnn.2013.6706831.

    Google Scholar 

  • Chong, J. W., Kim, Y., and Chon, K. H. (2014). “Nonlinear multiclass support vector machine-based health monitoring system for buildings employing magnetorheological dampers.” Journal of Intelligent Material Systems and Structures, Vol. 25, No. 12, pp. 1456–1468, DOI: https://doi.org/10.1177/1045389x13507343.

    Article  Google Scholar 

  • Christian, I. (2009). “Contrastive divergence learning may diverge when training restricted boltzmann machines.” Frontiers in Computational Neuroscience, Vol. 3, DOI: https://doi.org/10.3389/conf.neuro.10.2009.14.121.

  • Dahl, G., Dong, Yu Li Deng, and Acero, A. (2012). “Context-dependent pre-trained deep neural networks for large-vocabulary speech recognition.” IEEE Transactions on Audio, Speech, and Language Processing, Vol. 20, No. 1, pp. 30–42, DOI: https://doi.org/10.1109/tasl.2011.2134090.

    Article  Google Scholar 

  • Deng, L. and Yu, D. (2014). “Deep learning: Methods and applications.” Foundations and Trends in Signal Processing, Vol. 7, Nos. 3–4, pp. 197–387.

    Article  MathSciNet  MATH  Google Scholar 

  • Dushyanth, N. D., Suma, M. N., and Latte, M. V. (2016). “Detection and localization of damage using empirical mode decomposition and multilevel support vector machine.” Applied Physics A, Vol. 122, No. 4, pp. 1–9, DOI: https://doi.org/10.1007/s00339-016-9753-z.

    Google Scholar 

  • Fahim, A. A., Gallego, R., Bochud, N., and Rus, G. (2013). “Model-based damage reconstruction in composites from ultrasound transmission.” Composites Part B: Engineering, Vol. 45, No. 1, pp. 50–62, DOI: https://doi.org/10.1016/j.compositesb.2012.09.003.

    Article  Google Scholar 

  • Farrar, C. R. and Worden, K. (2007). “An introduction to structural health monitoring.” Philosophical Transactions of the Royal Society of London A: Mathematical, Physical and Engineering Sciences, Vol. 365, No. 1851, pp. 303–315, DOI: https://doi.org/10.1098/rsta.2006.1928.

    Article  Google Scholar 

  • Farrar, C. R. and Worden, K. (2013). Structural Health Monitoring: A Machine Learning Perspective, John Wiley & Sons, Ltd., Chichester, UK.

    Google Scholar 

  • Figueiredo, E., Figueiras, J., Park, G., Farrar, C. R., and Worden, K. (2011). “Influence of the autoregressive model order on damage detection.” Computer-Aided Civil and Infrastructure Engineering, Vol. 26, No. 3, pp. 225–238, DOI:https://doi.org/10.1111/j.1467-8667.2010.00685.x.

    Article  Google Scholar 

  • Figueiredo, E., Park, G., Farrar, C. R., Worden, K., and Figueiras, J. (2011). “Machine learning algorithms for damage detection under operational and environmental variability.” Structural Health Monitoring, Vol. 10, No. 6, pp. 559–572, DOI: https://doi.org/10.1177/1475921710388971.

    Article  Google Scholar 

  • Figueiredo, E., Park, G., Figueiras, J., Farrar, C., and Worden, K. (2009). Structural health monitoring algorithm comparisons using standard data sets, Technical ReportLA-14393, U.S. Department of Energy. Tennessee.

    Book  Google Scholar 

  • Fischer, A. (2015). “Training restricted boltzmann machines.” KI - Künstliche Intelligenz, Vol. 29, No. 4, pp. 441–444, DOI: https://doi.org/10.1007/s13218-015-0371-2.

    Article  Google Scholar 

  • Furey, T. S., Cristianini, N., Duffy, N., Bednarski, D. W., Schummer, M., and Haussler, D. (2000). “Support vector machine classification and validation of cancer tissue samples using microarray expression data.” Bioinformatics, Vol. 16, No. 10, pp. 906–914, DOI: https://doi.org/10.1093/bioinformatics/16.10.906.

    Article  Google Scholar 

  • Ge, R., Pan, H., lin, Z., gong, N., and wang, J. (2016). “RF-Powered Battery-less Wireless Sensor Network.” The 5th International Symposium on Next-Generation Electronics, Hsinchu, Taiwan, pp. 32–33.

    Google Scholar 

  • Gersch, W. (1970). “Estimation of the autoregressive parameters of a mixed autoregressive moving-average time series.” IEEE Transactions on Automatic Control, Vol. 15, No. 5, pp. 583–588, DOI: https://doi.org/10.1109/TAC.1970.1099560.

    Article  MathSciNet  Google Scholar 

  • Ghiasi, R., Torkzadeh, P., and Noori, M. (2016). “A machine-learning approach for structural damage detection using least square support vector machine based on a new combinational kernel function.” Structural Health Monitoring, Vol. 13, No. 3, pp. 302–316, DOI: https://doi.org/10.1177/1475921716639587.

    Article  Google Scholar 

  • Gui, G., Pan, H., Zhibin Lin, Li, Y., and Yuan, Z. (2017). “Data-driven support vector machine with optimization techniques for structural health monitoring and damage detection.” KSCE Journal of Civil Engineering, Vol. 21, No. 2, pp. 523–534.5.

    Article  Google Scholar 

  • Herrasti, Z., Val, I., Gabilondo, I., Berganzo, J., Arriola, A., and Martínez, F. (2016). “Wireless sensor nodes for generic signal conditioning: Application to Structural Health Monitoring of wind turbines.” Sensors and Actuators A: Physical, Vol. 237, pp. 604–613. DOI: https://doi.org/10.1016/j.sna.2016.06.027.

    Article  Google Scholar 

  • Hinton, G. E. (2010). “A practical guide to training restricted boltzmann machines.” (Accessed 12 October 12, 2015) Available at: http://www.cs.toronto.edu/~hinton/absps/guideTR.pdf.

    Google Scholar 

  • Hinton, G., Deng, L., Yu, D., Dahl, G., Mohamed, A., Jaitly, N., Senior, A., Vanhoucke, V., Nguyen, P., Sainath, T., and Kingsbury, B. (2012). “Deep Neural networks for acoustic modeling in speech recognition: The shared views of four research groups.” IEEE Signal Processing Magazine, Vol. 29, No. 6, pp. 82–97, DOI: https://doi.org/10.1109/msp.2012.2205597.

    Article  Google Scholar 

  • Hinton, G., Osindero, S., and Teh, Y. (2006). “A fast learning algorithm for deep belief nets.” Neural Computation, Vol. 18, No. 7, pp. 1527–1554, DOI: https://doi.org/10.1162/neco.2006.18.7.1527.

    Article  MathSciNet  MATH  Google Scholar 

  • Hofmann, M. (2006). “Support vector machines-Kernels and the kernel trick.” from http://www.cogsys.wiai.uni-bamberg.de/teaching/ss06/hs_svm/slides/SVM_Seminarbericht_Hofmann.pdf.

    Google Scholar 

  • Holland, J. and ARbor, A. (1975). Adaptation in natural and artificial systems, MIT press, Cambrige.

    Google Scholar 

  • Hou, Z., Noori, M., and Amand, R. S. (2000). “Wavelet-based approach for structural damage detection.” Journal of Engineering Mechanics, Vol. 126, No. 7, pp. 677–683, DOI: https://doi.org/10.1061/(ASCE)0733-9399(2000)126:7(677).

    Article  Google Scholar 

  • Hsu, C.-W., Chang, C.-C., and Lin, C.-J. (2010). “A practical guide to support vector classification.” http://www.csie.ntu.edu.tw/~cjlin/papers/guide/guide.pdf.

    Google Scholar 

  • Huang, C.-L. and Dun, J.-F. (2008). “A distributed PSO-SVM hybrid system with feature selection and parameter optimization.” Applied Soft Computing, Vol. 8, No. 4, pp. 1381–1391, DOI: https://doi.org/10.1016/j.asoc.2007.10.007.

    Article  Google Scholar 

  • Huang, Q., Tang, B., and Deng, L. (2015). “Development of high synchronous acquisition accuracy wireless sensor network for machine vibration monitoring.” Measurement, Vol. 66, pp. 35–44, DOI: https://doi.org/10.1016/j.measurement.2015.01.021.

    Article  Google Scholar 

  • Jiang, M., Liang, Y., Feng, X., Fan, X., Pei, Z., Xue, Y., and Guan, R. (2016). “Text classification based on deep belief network and softmax regression.” Neural Computing and Applications, Vol. 29, No. 1, pp. 61–70, DOI: https://doi.org/10.1007/s00521-016-2401-x.

    Article  Google Scholar 

  • Kaveh, A., Bakhshpoori, T., and Azimi, M. (2015). “Seismic optimal design of 3D steel frames using cuckoo search algorithm.” The Structural Design of Tall and Special Buildings, Vol. 24, No. 3, pp. 210–227, DOI: https://doi.org/10.1002/tal.1162.

    Article  Google Scholar 

  • Kennedy, J. and Eberhart, R. C. (1995). “Particle swarm optimization.” In Proceedings of the IEEE international conference on neural networks IV, IEEE, Piscataway, pp. 1942–1948.

    Chapter  Google Scholar 

  • Kim, L. (2017). “DeepX: Deep learning accelerator for restricted boltzmann machine artificial Neural Networks.” IEEE Transactions on Neural Networks and Learning Systems, pp. 1–13, DOI: https://doi.org/10.1109/tnnls.2017.2665555.

    Google Scholar 

  • Kiwaki, T. and Aihara, K. (2015). “An effect of initial distribution covariance for annealing Gaussian restricted Boltzmann machines.” Artificial Intelligence Research, Vol. 4, No. 1, DOI: https://doi.org/10.5430/air.v4n1p53.

    Google Scholar 

  • Larochelle, H. and Bengio, Y. (2008). “Classification using discriminative restricted Boltzmann machines.” In Proceedings of the 25th International Conference on Machine Learning, Helsinki, Finland, pp. 536–543.

    Google Scholar 

  • Li, H. and Zhang, Y. (2009). “An algorithm of soft fault diagnosis for analog circuit based on the optimized SVM by GA.” Proc., Electronic Measurement & Instruments, 2009. ICEMI, pp. 4-1023–1024-1027, DOI: https://doi.org/10.1109/ICEMI.2009.5274151.

    Google Scholar 

  • Lin, P., Fu, S., Wang, S., Lai, Y., and Tsao, Y. (2016). “Maximum entropy learning with deep belief networks.” Entropy, MDPI, Vol. 18, No. 7, pp. 251, DOI: https://doi.org/10.3390/e18070251.

    Article  MathSciNet  Google Scholar 

  • Lin, Z., Fakhairfar, M., Wu, C., and Chen, G. (2013). Design, Construction and Load Testing of the Pat Daly Road Bridge in Washington County, MO, with Internal Glass Fiber Reinforced Polymers Reinforcement, Washington County, Missouri, Final Report NUTC R275, Washington County Commission and US Department of Transportation.

    Google Scholar 

  • Lin, Z., Fakharifar, M., Huang, Y., Chen, G., and Wang, Z. (2014). “Damage detection of a full-size concrete box girder bridge with the moving-window least-square fitting method.” NDE/NDT for Structural Materials Technology for Highway & Bridges.

    Google Scholar 

  • Lin, Z., Zhao, J., and Habib, T. (2012). Impact of overweight vehicles (with Heavy Axle Loads) on bridge deck deterioration, Final Report CFIRE 04-06, USDOT’s RITA by CFIRE and UW-Milwaukee, Wisconsin, http://www.wistrans.org/cfire/documents/FR_0406.pdf.

    Google Scholar 

  • Liu, C., Harley, J. B., Bergés, M., Greve, D. W., and Oppenheim, I. J. (2015). “Robust ultrasonic damage detection under complex environmental conditions using singular value decomposition.” Ultrasonics, Vol. 58, pp. 75–86, DOI: https://doi.org/10.1016/j.ultras.2014.12.005.

    Article  Google Scholar 

  • Magalhães, F., Cunha, A., and Caetano, E. (2012). “Vibration based structural health monitoring of an arch bridge: From automated OMA to damage detection.” Mechanical Systems and Signal Processing, Vol. 28, pp. 212–228, DOI: https://doi.org/10.1016/j.ymssp.2011.06.011.

    Article  Google Scholar 

  • Masri, S. F., Smyth, A. W., Chassiakos, A. G., Caughey, T. K., and Hunter, N. F. (2000). “Application of neural networks for detection of changes in nonlinear systems.” Journal of Engineering Mechanics, Vol. 126, No. 7, pp. 666–676, DOI: https://doi.org/10.1061/(ASCE)0733-9399(2000)126:7(666).

    Article  Google Scholar 

  • Melgani, F. and Bazi, Y. (2008). “Classification of electrocardiogram signals with support vector machines and particle swarm optimization.” IEEE Transactions on Information Technology in Biomedicine, Vol. 12, No. 5, pp. 667–677, DOI: https://doi.org/10.1109/TITB.2008.923147.

    Article  Google Scholar 

  • Mohamed, A., Dahl, G., and Hinton, G. (2012). “Acoustic modeling using deep belief networks.” IEEE Transactions on Audio, Speech, and Language Processing, Vol. 20, No. 1, pp. 14–22, DOI: https://doi.org/10.1109/tasl.2011.2109382.

    Article  Google Scholar 

  • Neerukatti, R. K., Hensberry, K., Kovvali, N., and Chattopadhyay, A. (2016). “A novel probabilistic approach for damage localization and prognosis including temperature compensation.” Journal of Intelligent Material Systems and Structures, Vol. 27, No. 5, pp. 592–607, DOI: https://doi.org/10.1177/1045389x15575084.

    Article  Google Scholar 

  • Oh, C. K. and Sohn, H. (2009). “Damage diagnosis under environmental and operational variations using unsupervised support vector machine.” Journal of Sound and Vibration, Vol. 325, No. 1, pp. 224–239, DOI: https://doi.org/10.1016/j.jsv.2009.03.014.

    Article  Google Scholar 

  • Pan, H., Azimi, M., Gui, G., Yan, F., and Lin, Z. (2017). “Vibration-based support vector machine for structural health monitoring.” 7th International Conference on Experimental Vibration Analysis for Civil Engineering Structures, San Diego, CA, USA.

    Google Scholar 

  • Pan, H., Azimi, M., Yan, F., and Lin, Z. (2017). “Time-frequency based data-driven structural diagnosis and damage detection for cable-stayed bridges.” ASCE Journal of Bridge Engineering, accepted.

    Google Scholar 

  • Pan, H., Ge, R., Wang, J., Gong, N., and Lin, Z. (2016). “Integrated Wireless Sensor Networks with UAS for Damage Detection and Monitoring of Bridges and Other Large-Scale Critical Civil Infrastructures.” NDE/NDT for Highway and Bridges: Structural Materials Technology Portland, OR, USA.

    Google Scholar 

  • Pavlopoulou, S., Worden, K., and Soutis, C. (2016). “Novelty detection and dimension reduction via guided ultrasonic waves: Damage monitoring of scarf repairs in composite laminates.” Journal of Intelligent Material Systems and Structures, Vol. 27, No. 4, pp. 549–566. DOI: https://doi.org/10.1177/1045389x15574937.

    Article  Google Scholar 

  • Santos, A. E., Figueiredo, M. F. M., Silva, C. S., Sales, J. C., and Costa, W. A. (2016). “Machine learning algorithms for damage detection: Kernel-based approaches.” Journal of Sound and Vibration, Vol. 363, pp. 584–599, DOI: https://doi.org/10.1016/j.jsv.2015.11.008.

    Article  Google Scholar 

  • Sarikaya, R., Hinton, G., and Deoras, A. (2014). “Application of deep belief networks for natural language understanding.” IEEE/ACM Transactions on Audio, Speech, and Language Processing, Vol. 22, No. 4, pp. 778–784, DOI: https://doi.org/10.1109/taslp.2014.2303296.

    Article  Google Scholar 

  • Seyedpoor, S. M. (2012). “A two stage method for structural damage detection using a model strain energy based index and particle swarm optimization.” International Journal of Non-Linear Mechanics, Vol. 47, No. 1, pp. 1–8, DOI: https://doi.org/10.1016/j.ijnonlinmec.2011.07.011.

    Article  MathSciNet  Google Scholar 

  • Sharma, A., Amarnath, M., and Kankar, P. (2016). “Feature extraction and fault severity classification in ball bearings.” Journal of Vibration and Control, Vol. 22, No. 1, pp. 176–192, DOI: https://doi.org/10.1177/1077546314528021.

    Article  Google Scholar 

  • Shawe-Taylor, J. and Sun, S. (2011). “A review of optimization methodologies in support vector machines.” In Neurocomputing, Vol. 74, No. 17, pp. 3609–3618, DOI: https://doi.org/10.1016/j.neucom.2011.06.026.

    Article  Google Scholar 

  • Sohn, H., Farrar, C. R., Hunter, N. F., and Worden, K. (2001). “Structural health monitoring using statistical pattern recognition techniques.” Journal of Dynamic Systems, Measurement, and Control, Vol. 123, No. 4, pp. 706–711, DOI: https://doi.org/10.1115/1.1410933.

    Article  Google Scholar 

  • Suykens, J. A. K. and Vandewalle, J. (1999). “Least squares support vector machine classifiers.” Neural Processing Letters, Vol. 9, No. 3, pp. 293–300, DOI: https://doi.org/10.1023/a:1018628609742.

    Article  MATH  Google Scholar 

  • Tibaduiza, D. A., Mujica, L. E., Rodellar, J., and Güemes, A. (2015). “Structural damage detection using principal component analysis and damage indices.” Journal of Intelligent Material Systems and Structures, Vol. 27, No. 2, pp. 233–248, DOI: https://doi.org/10.1177/1045389x14566520.

    Article  Google Scholar 

  • Vesely, K., Ghoshal, A., Burget, L., and Povey, D. (2013). “Sequence-discriminative training of deep neural networks.” In Proceedings of Interspeech, Communication Assoation, pp. 2345–2349.

    Google Scholar 

  • Wang, H., Wang, G., Li, G., Peng, J., and Liu, Y. (2016). “Deep belief network based deterministic and probabilistic wind speed forecasting approach.” Applied Energy, Vol. 182, pp. 80–93, DOI: https://doi.org/10.1016/j.apenergy.2016.08.108.

    Article  Google Scholar 

  • Watters, D. G., Jayaweera, P., Bahr, A. J., and Huestis, D. L. (2002). “Design and performance of wireless sensors for structural health monitoring.” Proc., Review of Progress in Quantitative Nondestructive Evaluation, AIP, pp. 969–976, DOI: https://doi.org/10.1063/1.1472901.

    Google Scholar 

  • Widodo, A. and Yang, B.-S. (2007). “Application of nonlinear feature extraction and support vector machines for fault diagnosis of induction motors.” Expert Systems with Applications, Vol. 33, No. 1, pp. 241–250, DOI: https://doi.org/10.1016/j.eswa.2006.04.020.

    Article  Google Scholar 

  • Worden, K. and Dulieu-Barton, J. M. (2004). “An overview of intelligent fault detection in systems and structures.” Structural Health Monitoring, Vol. 3, No. 1, pp. 85–98, DOI: https://doi.org/10.1177/1475921704041866.

    Article  Google Scholar 

  • Worden, K. and Lane, A. J. (2001). “Damage identification using support vector machines.” Smart Materials and Structures, Vol. 10, No. 3, p. 540, DOI: 0964-1726/10/i=3/a=317.

    Article  Google Scholar 

  • Worden, K., Farrar, C. R., Manson, G., and Park, G. (2007). “The fundamental axioms of structural health monitoring.” Proceedings of the Royal Society A: Mathematical, Physical and Engineering Science, Vol. 463, No. 2082, pp. 1639–1664, DOI: https://doi.org/10.1098/rspa.2007.1834.

    Article  Google Scholar 

  • Yan, F. and Lin, Z. (2016). “New strategy for anchorage reliability assessment of GFRP bars to concrete using hybrid artificial neural network with genetic algorithm.” Composites Part B: Engineering, Vol. 92, pp. 420–433, DOI: https://doi.org/10.1016/j.compositesb.2016.02.008.

    Article  Google Scholar 

  • Yan, F., Lin, Z., Wang X., Azarmi F., and Sobolev, K. (2016). “Evaluation and prediction of bond strength of GFRP-bar reinforced concrete using artificial neural network optimized with genetic algorithm.” Composite Structures, Vol. 161, pp. 441–452, DOI: https://doi.org/10.1016/j.compstruct.2016.11.068.

    Article  Google Scholar 

  • Yao, R. and Pakzad, S. N. (2012). “Autoregressive statistical pattern recognition algorithms for damage detection in civil structures.” Mechanical Systems and Signal Processing, Vol. 31, pp. 355–368, DOI: https://doi.org/10.1016/j.ymssp.2012.02.014.

    Article  Google Scholar 

  • Yeesock, K., Jo Woon, C., Ki, H. C., and Kim, J. (2013). “Wavelet-based AR-SVM for health monitoring of smart structures.” Smart Materials and Structures, Vol. 22, No. 1, pp. 015003, DOI: 0964-1726/22/i=1/a=015003.

    Article  Google Scholar 

  • Ying, Y., Jr., J. H. G., Oppenheim, I. J., Soibelman, L., Harley, J. B., Shi, J., and Jin, Y. (2013). “Toward data-driven structural health monitoring: Application of machine learning and signal processing to damage detection.” Journal of Computing in Civil Engineering, Vol. 27, No. 6, pp. 667–680, DOI: https://doi.org/10.1061/(ASCE)CP.1943-5487.0000258.

    Article  Google Scholar 

  • Zang, C. and Imregun, M. (2001). “Structural damage detection using artificial neural networks and measured frf data reduced via principal component projection.” Journal of Sound and Vibration, Vol. 242, No. 5, pp. 813–827, DOI: https://doi.org/10.1006/jsvi.2000.3390.

    Article  MATH  Google Scholar 

  • Zeiler, M. (2014). Hierarchical convolutional deep learning in computer vision, Ph.D. Thesis, New York University.

    Google Scholar 

  • Zhao, R., Yan, R., Chen, Z., Mao, K., Wang, P., and Gao, R. X. (2015). “Deep learning and its applications to machine health monitoring: A survey.” Journal of Latex Class Files, Vol. 14, No. 8.

    Google Scholar 

  • Zou, Y., Tong, L., and Steven, G. P. (2000). “Vibration-based model-dependent damage (Delamination) identification and health monitoring for composite structures — A Review.” Journal of Sound and Vibration, Vol. 230, No. 2, pp. 357–378, DOI: https://doi.org/10.1006/jsvi.1999.2624.

    Article  Google Scholar 

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  1. School of Architecture and Civil Engineering, Jinggangshan University, Ji’an, Jiangxi, 343009, China

    Hong Pan & Guoqing Gui

  2. School of Civil Engineering, Tongji University, Shanghai, 20092, China

    Hong Pan & Guoqing Gui

  3. Dept. of Civil and Environmental Engineering, North Dakota State University, Fargo, ND, 58105, USA

    Hong Pan & Zhibin Lin

  4. Dept.of Computer Science, North Dakota State University, Fargo, ND, 58105, USA

    Changhui Yan

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Correspondence to Zhibin Lin.

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Pan, H., Gui, G., Lin, Z. et al. Deep BBN Learning for Health Assessment toward Decision-Making on Structures under Uncertainties. KSCE J Civ Eng 22, 928–940 (2018). https://doi.org/10.1007/s12205-018-1301-2

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