132 Z. Wang and Y.-J. Cha extracting more appropriate damage-sensitive features from the acceleration histories. In addition, more research is needed to improve the efficiency of the DPFC method during the training process. The objective of such improvement is to reduce the workload of artificial parameter setting in the DPFC method and to apply this method to actual constructed buildings and bridges in real-life damage scenarios. References 1. Farrar, C.R., Worden, K.: Structural Health Monitoring: A Machine Learning Perspective. Wiley, Hoboken, NJ (2012) 2. Cha, Y.J., Trocha, P., Buyukozturk, O.: Field measurement based system identification and dynamic response prediction of a unique MIT building. Sensors. 16(7), 1016 (2016) 3. Barthorpe, R.J.: On model-and data-based approaches to structural health monitoring. Ph.D. thesis, University of Sheffield, Sheffield, UK (2010) 4. Ding, X., Li, Y., Belatreche, A., Maguire, L.P.: An experimental evaluation of novelty detection methods. Neurocomputing. 135, 313–327 (2014) 5. Walsh, S.B., Borello, D.J., Guldur, B., Hajjar, J.F.: Data processing of point clouds for object detection for structural engineering applications. Comput. Aided Civ. Inf. Eng. 28(7), 495–508 (2013) 6. Manson, G., Worden, K., Holford, K., Pullin, R.: Visualisation and dimension reduction of acoustic emission data for damage detection. J. Intell. Mater. Syst. Struct. 12(8), 529–536 (2001) 7. Noh, H.Y., Nair, K.K., Kiremidjian, A.S., Loh, C.H.: Application of time series based damage detection algorithms to the benchmark experiment at the National Center for Research on Earthquake Engineering (NCREE) in Taipei, Taiwan. Smart Struct. Syst. 5(1), 95–117 (2009) 8. Long, J., Buyukozturk, O.: Automated structural damage detection using one-class machine learning. Dyn. Civil Struct. 4, 117–128 (2014) 9. Khoa, N.L.D., Zhang, B., Wang, Y., Chen, F., Mustapha, S.: Robust dimensionality reduction and damage detection approaches in structural health monitoring. Struct. Health Monit. 13(4), 406–417 (2014) 10. Reynolds, D.: Gaussian mixture models. In: Encyclopedia of Biometrics, pp. 827–832. Springer, New York (2015) 11. Masud, M.M., Gao, J., Khan, L., Han, J., Thuraisingham, B.: Classification and novel class detection in concept-drifting data streams under time constraints. IEEE Trans. Knowl. Data Eng. 23(6), 859–874 (2011) 12. Rodriguez, A., Laio, A.: Clustering by fast search and find of density peaks. Science. 344(6191), 1492–1496 (2014) 13. Benoudjit, N., Archambeau, C., Lendasse, A., Lee, J.A., Verleysen, M.: Width optimization of the Gaussian kernels in radial basis function networks. In: Proceedings of the 10th European Symposium on Artificial Neural Networks, vol. 2, pp. 425–432 (2002) 14. Cha, Y.J., Buyukoztur, O.: Structural damage detection using modal strain energy and hybrid multiobjective optimization. Comput. Aided Civ. Inf. Eng. 30(5), 347–358 (2015) 15. Cha, Y.J., Wang, Z.: Unsupervised novelty detection-based structural damage localization using a density peaks-based fast clustering algorithm. Struct. Health Monit. (2017). doi:10.1177/1475921717691260
RkJQdWJsaXNoZXIy MTMzNzEzMQ==