Prediction of PCCP Failure Based on Hydrophne Detecting

[1]S. Ge and S. K. Sinha, “Effect of Wire Breaks on Prestressed Concrete Cylinder Pipe (PCCP) Reinforced with Steel Liners—A Case Study”, in Pipelines 2012@ sInnovations in Design, Construction, Operations, and Maintenance, Doing More with Less, pp. 1297-1306, 2012. Search in Google Scholar

[2]S.-p. Sun and M. Wang “,Characteristic of prestressed concrete cylinder pipe,” Municipal Engineering Technology, 2006. Search in Google Scholar

[3]J. J. Galleher and A. E. Romer, “Who Says You Need Multiple Wire Breaks for a PCCP Pipe to Fail?”, in Pipelines 2012@ sInnovations in Design, Construction, Operations, and Maintenance, Doing More with Less, pp. 278-287, 2012. Search in Google Scholar

[4]M. Higgins and P. Paulson, “Fiber optic sensors for acoustic monitoring of PCCP”, in Proceedings of the 2006 Pipeline Division Specialty Conference-Pipelines 2006: Service to the Owner, 2006.10.1061/40854(211)10 Search in Google Scholar

[5]N. D. Faber, M. R. Coghill, and J. J. Galleher, “Beyond the Wires: A Sustainable Approach to Prestressed Concrete Cylinder Pipe Management”, in Pipelines 2012@ sInnovations in Design, Construction, Operations, and Maintenance, Doing More with Less, pp. 1214-1222, 2012. Search in Google Scholar

[6]M. S. Zarghamee, D. W. Eggers, R. Ojdrovic, and B. Rose, “Risk analysis of Prestressed concrete cylinder pipe with broken wires”, Pipeline Engineering and Construction International Conference 2003, pp. 599-609,2004.10.1061/40690(2003)81 Search in Google Scholar

[7]R. You and H. B. Gong, “Failure Analysis of PCCP with Broken Wires”, Applied Mechanics and Materials, vol. 193, pp. 855-858, 2012.10.4028/www.scientific.net/AMM.193-194.855 Search in Google Scholar

[8]A. Biggar, “Detecting Wire Breaks from the Outside of PCCP”, in Proceedings of the ASCE International Pipelines Conference 2010: Climbing New Peaks to Infrastructure Reliability—Renew, Rehab, and Reinvest 2010.10.1061/41138(386)59 Search in Google Scholar

[9]M. S. Zarghamee and R. P. Ojdrovic, “Risk Assessment and Repair Priority of PCCP with Broken Wires”, 2004. Search in Google Scholar

[10]G. E. Bell and P. Paulson, “Measurement and Analysis of PCCP Wire Breaks, Slips and Delaminations”, Pipelines 2010: Climbing New Peaks to Infrastructure Reliability - Renew, Rehab, and Reinvest - Proc. of the Pipelines 2010 Conference, vol. 386, pp. 1016-1024, 2010. Search in Google Scholar

[11]P. Cliff Moore, “Use of Acoustic Monitoring Data for PCCP Condition Assessment”, Pipelines 2009: Infrastructure’s Hidden Assets - Proceedings of the Pipelines 2009 Conference, vol. 360, pp. 45-54, 2009. Search in Google Scholar

[12]M. Higgins and P. Paulson, “Fiber optic sensors for acoustic monitoring of PCCP”, in Proceedings of the 2006 Pipeline Division Specialty Conference-Pipelines 2006: Service to the Owner, 2006.10.1061/40854(211)10Search in Google Scholar

[13]N.Afsarimanesh & P. Z. Ahmed, “LabVIEW Based Characterization and Optimization of Thermal Sensors”, International Journal On Smart Sensing and Intelligent Systems, vol. 4, No. 4, pp. 726-739, 2011.10.21307/ijssis-2017-466Search in Google Scholar

[14]R. Long, M. Lowe, and P. Cawley, “Attenuation characteristics of the fundamental modes that propagate in buried iron water pipes”, Ultrasonics, vol. 41, No. 7, pp. 509-519, 2003.10.1016/S0041-624X(03)00166-5 Search in Google Scholar

[15]R. Long, K. Vine, M. Lowe, and P. Cawley, “Monitoring acoustic wave propagation in buried cast iron water pipes”, in AIP Conference Proceedings, p. 1202, 2001.10.1063/1.1373891 Search in Google Scholar

[16]R. Long, P. Cawley, and M. Lowe, “Acoustic wave propagation in buried iron water pipes”, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences, vol. 459, No. 2039, pp. 2749-2770, 2003. Search in Google Scholar

[17]B. K. Sinha, T. J. Plona, S. Kostek, and S. K. Chang, “Axisymmetric wave propagation in fluid - loaded cylindrical shells. I: Theory”, The Journal of the Acoustical Society of America, vol. 92, p. 1132, 1992.10.1121/1.404040 Search in Google Scholar

[18]F. Stulen and J. Kiefner, “Evaluation of acoustic emission monitoring of buried pipelines”, in 1982 Ultrasonics Symposium, pp. 898-903, 1982.10.1109/ULTSYM.1982.197964 Search in Google Scholar

[19]S. G. Mallat, “A theory for multiresolution signal decomposition: the wavelet representation”, Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol. 11, No. 7, pp. 674-693, 1989.10.1109/34.192463 Search in Google Scholar

[20]Y. Meyer and D. H. Salinger, “Wavelets and operators” vol. 1: Cambridge university press, 1995. Search in Google Scholar

[21]L. I. Kuncheva and J. J. Rodríguez, “Interval feature extraction for classification of event-related potentials (ERP) in EEG data analysis”, Progress in Artificial Intelligence, pp. 1-8, 2013.10.1007/s13748-012-0037-3 Search in Google Scholar

[22]H.Hashim, S.Ramli, N.Wahid, & M.S. Sulainman , “Recognition of psoriasis features via Daubechies D8 wavelet technique”, International Journal On Smart Sensing and Intelligent Systems, vol.6, No.2, pp.711-732,2013.10.21307/ijssis-2017-562 Search in Google Scholar

[23]B. Lechner, M. Lieschnegg, O. Mariani, M. Pircher, & A. Fuchs,”A wavelet-based bridge weigh-in-motion system”, International Journal On Smart Sensing and Intelligent Systems vol.3, No.4, pp. 573-591, 2010.10.21307/ijssis-2017-409 Search in Google Scholar

[24]I. Daubechies, “Ten lectures on wavelets vol. 61: SIAM, 1992.10.1137/1.9781611970104 Search in Google Scholar

[25]V. Vapnik, “The nature of statistical learning theory: springer, 2000.10.1007/978-1-4757-3264-1 Search in Google Scholar

[26]B. E. Boser, I. M. Guyon, and V. N. Vapnik, “A training algorithm for optimal margin classifiers”, in Proceedings of the fifth annual workshop on Computational learning theory, pp. 144-152, 1992.10.1145/130385.130401 Search in Google Scholar

[27]W. Li, P. Fu, and W. Cao, “Study on feature selection and identification method of tool wear states based on SVM” , International Journal On Smart Sensing and Intelligent Systems, vol.6, No.2, pp.448-465.2013.10.21307/ijssis-2017-549 Search in Google Scholar

[28]A.-M.Cretu & P.Payeur, “Biologically-inspired visual attention features for a vehicle classification task”, International Journal On Smart Sensing and Intelligent Systems, vol.4, No.3, pp.402-423,2011.10.21307/ijssis-2017-447 Search in Google Scholar

[29]W. Zu, J. Yuan, and W. Zhang, “Determining method for reliability distribution function of transformer fault diagnosis based on SVM”, Heilongjiang Dianli Jishu(Heilongjiang Electric Power), vol. 35, No. 2, 2013. Search in Google Scholar

[30]L. Jian, K. Weikang, S. Jiangbo, W. Ke, W. Weikui, Z. Weipu, et al., “Determination of Corrosion Types from Electrochemical Noise by Artificial Neural Networks”, Int. J. Electrochem. Sci, vol. 8, No. pp. 2365-2377, 2013.Search in Google Scholar