Defect detection in reinforced concrete using random neural architectures

Computer-Aided Civil and Infrastructure Engineering

Volumn 29, Issue 3, 2014, Pages 191-207

  Butcher, J B ^a   Day, C R ^a   Austin, J C ^a   Haycock, P W ^a   Verstraeten, D ^b   Schrauwen, B ^b  

^a KEELE UNIVERSITY   (United Kingdom)

^b GHENT UNIVERSITY   (Belgium)

Author keywords

[No Author keywords available]

Indexed keywords

DETECTING DEFECTS; ECHO STATE NETWORKS; ELECTROMAGNETIC PROPERTIES; EXTREME LEARNING MACHINE; NEURAL ARCHITECTURES; NONINVASIVE TECHNIQUE; REINFORCING STEELS; TRAINING PROCEDURES;

CONCRETE BUILDINGS; CONCRETE CONSTRUCTION; DATA VISUALIZATION; ELECTROMAGNETISM; RECURRENT NEURAL NETWORKS; REINFORCED CONCRETE;

CONCRETES;

ARCHITECTURAL DESIGN; ARTIFICIAL NEURAL NETWORK; CONCRETE STRUCTURE; DEFECT; ELECTROMAGNETIC FIELD; REINFORCED CONCRETE; STEEL;

ULMUS;

EID: 84892818956     PISSN: 10939687     EISSN: 14678667     Source Type: Journal    
DOI: 10.1111/mice.12039     Document Type: Article

References (60)

1. Atzlesberger, J. & Zagar, B. (2010), Magnetic flux leakage measurement setup for defect detection, Procedia Engineering, 5, 1401-04.
2. Babbar, V., Bryne, J. & Clapham, L. (2005), Mechanical damage detectio using magnetic flux leakage tools: modeling the effect of dent geometry and stresses, NDT&E International, 38, 471-77.
3. Babbar, V., Shiari, B. & Clapham, L. (2003), Mechanical damage detection with magnetic flux leakage tools: modeling the effect of localised residual stresses, IEEE Transactions on Magnetics, 40(1), 43-49.
4. Bakhary, N., Hao, H. & Deeks, A. (2007), Damage detection using artificial neural network with consideration of uncertainties, Engineering Structures, 29(11), 2806-15.
5. Bakhary, N., Hao, H. & Deeks, A. (2010), Structure damage detection using neural network with multi-stage substructuring, Advances in Structural Engineering, 13(1), 1-16.
6. Balabanic, G., Bicanic, N. & Durekovic, A. (1996), The influence of w/c ratio, concrete cover thickness and degree of water saturation on the corrosion rate of reinforcing steel in concrete, Cement and Concrete Research, 26(5), 761-69.
7. Baldi, P., Brunak, S., Chauvin, Y., Andersen, C. & Neilsen, H. (2000), Assessing the accuracy of prediction algorithms for classification: an overview. Bioinformatics Review, 16(5), 412-24.
8. Barai, S.V. & Pandey, P. C. (1997), Time-delay neural networks in damage detection of railway bridges. Advances in Engineering Software, 28(1), 1-10.
9. Benin, A., Semenov, A. & Semenov, S. (2010), Modelling of fracture process in concrete reinforced structures under steel corrosion, Journal of Achievements in Materials and Manufacturing Engineering, 39(2), 168-75.
10. Butcher, J., Verstraeten, D., Schrauwen, B., Day, C. & Haycock, P. (2010), Extending reservoir computing with random static projections: a hybrid between OP-ELM and RC, in Proceedings of the European Symposium on Artificial Neural Networks (ESANN), Brugges, Belgium, pp. 303-08.
11. Butcher, J. B. (2012), Reservoir computing with high non-linear separation and long-tem memory for time-series data analysis. Ph.D. thesis, Institute for the Environment, Physical Sciences and Applied Mathematics (EPSAM), Keele University, Staffordshire, United Kingdom.
12. Butcher, J. B., Lion, M., Day, C. R., Haycock, P. W., Hocking, M. J. & Bladon, S. (2009), A low frequency electromagnetic probe for detection of corrosion in steel-reinforced concrete, in M. Grantham, C. Majorana, and S. Valentina (eds.), Concrete Solutions, CRC Press, USA, pp. 417-24.
13. Butcher, J. B., Verstraeten, D., Schrauwen, B., Day, C. R. & Haycock, P. W. (2013), Reservoir computing and extreme learning machines for non-linear time-series data analysis, Neural Networks, 30, 76-89.
14. Cannas, B., Carcangiu, S., Cau, F., Fanni, A., Montisci, A. & Testoni, P. (2007), Artificial neural networks for non destructive testing of concrete structures, in Proceedings of the 10th International Conference on Engineering Applications of Neural Networks (EANN), Thessaloniki, Greece.
15. Cusson, D., Lounis, Z. & Daigle, L. (2011), Monitoring life cycle performance of aging concrete highway bridges built in corrosive environments, Computer-Aided Civil and Infrastructure Engineering, 26(7), 524-41.
16. Dao, L., Dao, V., Kim, S. & Ann, K. (2010), Modeling steel corrosion in concrete structures. Part 2: A unified adaptive finite element model for simulation of steel corrosion, International Journal of Electrochemical Science, 5(3), 314-26.
17. Day, C. R., Austin, J. C., Butcher, J. B., Haycock, P. W. & Kearon, A. T. (2009), Element-specific determination of X-ray transmission signatures using neural networks. NDT&E International, 44(5), 446-51.
18. Elman, J. L. (1990), Finding structure in time, Cognitive Science, 14(2), 179-211.
19. Fawcett, T. (2006), An introduction to ROC analysis, Pattern Recognition Letters, 27, 861-74.
20. Flood, I. (2006), Next generation artificial neural networks for civil engineering, Journal of Computing in Civil Engineering, 20(5), 305-07.
21. Graf, W., Freitag, S., Kaliske, M. & Sickert, J.-U. (2010), Recurrent neural networks for uncertain time-dependent structural behaviour, Computer-Aided Civil and Infrastructure Engineering, 25, 322-33.
22. Graf, W., Freitag, S., Sickert, J.-U. & Kaliske, M. (2012), Structural analysis with fuzzy data and neural network-based material description, Computer-Aided Civil and Infrastructure Engineering, 27(9), 640-54.
23. Haycock, P. W. & Lambert, R. (2008), Rust detection-The SciSite Method, Concrete, 42, 42-43.
24. Haykin, S. (2009), Neural Networks and Learning Machines, 3rd edn., Pearson Prentice Hall, NJ.
25. Hillemeier, B. & Walther, A. (2007), Fast non-destructive localisation of prestressing steel fractures in post-tensioned concrete bridges, in C. U. Grosse (ed.), Advances in Construction Materials, Springer, Berlin, Heidelberg, pp. 563-74.
26. Huang, G., Zhu, Q. & Siew, C. (2006), Extreme learning machines: theory and applications, Neurocomputing, 70(1-3), 489-501.
27. Jaeger, H. (2001), The "Echo State" Approach to Analysing and Training Recurrent Neural Networks, Technical Report, German National Research Institute for Computer Science.
28. Jaeger, H. & Haas, H. (2004), Harnessing nonlinearity: predicting chaotic systems and saving energy in wireless communication, Science, 304(5667), 78-80.
29. Jiang, X. & Adeli, H. (2007), Pseudospectra, MUSIC, and dynamic wavelet neural network for damage detection of highrise buildings, International Journal for Numerical Methods in Engineering, 71, 606-29.
30. Jiang, S., Zhang, C. & Zhang, S. (2011), Two stage structural damage detection using fuzzy neural networks and data fusion techniques, Expert Systems with Applications, 38(1), 511-19.
31. Joshi, A., Udpa, L., Udpa, S. & Tamburrino, A. (2006), Adaptive wavelets for characterizing magnetic flux leakage signals from pipeline inspection, IEEE Transactions on Magnetics, 42(10), 3168-70.
32. Kranc, S. C. & Sags, A. A. (2001), Detailed modeling of corrosion macrocells on steel reinforcing in concrete, Corrosion Science, 43(7), 1355-72.
33. Lam, H. F., Yuen, K. V. & Beck, J. L. (2006), Structural health monitoring via measured ritz vectors utilising artificial neural networks, Computer-Aided Civil and Infrastructure Engineering, 21(4), 232-41.
34. Li, X., Li, X., Chen, L., Qin, G., Feng, P. & Huang, Z. (2008), Steel pipeline testing using magnetic flux leakage method, IEEE Conference on Industrial Technology.
35. Li, Y., Wilson, J. & Tian, G. Y. (2007), Experiment and simulation study of 3D magnetic field sensing for magnetic flux leakage defect characterization, NDT&E International, 40, 179-84.
36. Lim, Z. W., Tan, C. Y., Seah, W. G. & Tan, G. H. (2009), Detection of failures in civil structures using artificial neural networks, in Proceedings of the 19th International Conference on Artificial Neural Networks: Part II, ICANN '09, Springer-Verlag, Berlin, Heidelberg, pp. 976-85.
37. Liu, Y. Y., Ju, Y. F., Duan, C. D. & Zhao, X-F. (2011), Structure damage diagnosis using neural network and feature fusion, Engineering Applications of Artificial Intelligence, 24, 87-92.
38. Lukosevicius, M. & Jaeger, H. (2009), Reservoir computing approaches to recurrent neural network training, Computer Science Review, 3(3), 127-49.
39. Matthews, B. (1975), Comparison of the predicted and observed secondary structure of T4 phage Iysozyme, Biochemical Biophysics Acta, 405, 442-51.
40. Montgomery, D. & Peck, E. (1982), Introduction to Linear Regression Analysis, Probability and Mathematical Statistics, Wiley, USA.
41. Morcous, G. & Lounis, Z. (2005), Prediction of onset of corrosion in concrete bridge decks using neural networks and case-based reasoning, Computer-Aided Civil and Infrastructure Engineering, 20(2), 108-17.
42. Niu, L. (2009), A time-delay neural networks architecture for structural damage detection, in Proceedings of the International Conference on Advanced Computer Control, ICACC '09, pp. 571-75.
43. Obst, O., Wang, X. & Prokopenko, M. (2008), Using echo state networks for anomaly detection in underground coal mines, in Proceedings of the International Conference on Information Processing in Sensor Networks, IEEE Computer Society, pp. 219-29.
44. Osornio-Rios, R. A., Amezquita-Sanchez, J. P., Romero-Troncoso, R. J. & Garcia-Perez, A. (2012), MUSIC-neural network analysis for locating structural damage in truss-type structures by means of vibrations, Computer-Aided Civil and Infrastructure Engineering, 27(9), 687-98.
45. Parthiban, T., Ravi, R., Parthiban, G. T., Srinivasan, S., Ramakirshnan, K. R. & Raghavan, M. (2005), Neural network analysis for corrosion of steel in concrete, Corrosion Science, 47, 1625-42.
46. Qiao, L., Esmaeily, A. & Melhem, H. G. (2012), Signal pattern-recognition for damage diagnosis in structures, Computer-Aided Civil and Infrastructure Engineering, 27(9), 699-710.
47. Rees, D. (2000), Essential Statistics, 4th edn. Chapman and Hall, London, UK.
48. Shafei, B., Alipour, A. & Shinozuka, M. (2013), A stochastic computational framework to investigate the initial stage of corrosion in reinforced concrete superstructures, Computer-Aided Civil and Infrastructure Engineering, DOI: 10.1111/mice.12019.
49. Shaw, M. R., Millard, S. G., Molyneaux, T. C. K., Taylor, M. J. & Bungey, J. H. (2005), Location of steel reinforcement in concrete using ground penetrating radar and neural networks, NDT&E International, 38, 203-12.
50. Sinha, S. K. & Knight, M. A. (2004), Intelligent system for condition monitoring of underground pipes, Computer-Aided Civil and Infrastructure Engineering, 19, 42-53.
51. Taffe, A., Hillemeier, B. & Walther, A. (2010), Condition assessment of a 45-year old prestressed concrete bridge using NDT and verification of the results, NDT.net. Available at: http://www.ndt.net/article/ndtnet/2010/SMT2010_taffe.pdf, accessed December 12, 2012.
52. The Concrete Centre (2009), The Concrete Industry Sustainability Performance Report, Technical Report, Riverside House, 4 Meadows Business Park, Station Approach, Blackwater, Camberley, Surrey GU17 AB.
53. Vanderley, J. (2003), On the sustainability of the concrete, Industry and Environment, 26(2-3):62-63.
54. Verstraeten, D., Dambre, J., Dutoit, X. & Schrauwen, B. (2010), Memory versus non-linearity in reservoirs, in Proceedings of the International Joint Conference on Neural Networks (IJCNN), Barcelona, Spain, pp. 2669-76.
55. Verstraeten, D., Schrauwen, B., D'Haene, M. & Stroobandt, D. (2007), An experimental unification of reservoir computing methods, Neural Networks, 20(3), 391-403.
56. Wang, Z. D., Gu, Y. & Wang, Y. S. (2012), A review of three magnetic NDT technologies, Journal of Magnetism and Magnetic Materials, 324, 382-88.
57. Wheat, H. G. (2009), Long-term investigation of alternative reinforcement materials for concrete, in M. Grantham, C. Majorana, and S. Valentina (eds.), Concrete Solutions, CRC Press, USA, pp. 227-31.
58. Wu, Z., Xu, B. & Yokoyama, K. (2002), Decentralized parametric damage detection based on neural networks, Computer-Aided Civil and Infrastructure Engineering, 17, 175-84.
59. Xu, B., Chen, G. & Wu, Z. S. (2007), Parametric identification for a truss structure using axial strain, Computer-Aided Civil and Infrastructure Engineering, 22(3), 210-22.
60. Xu, H. & Humar, J. (2006), Damage detection in a girder bridge by artificial neural network technique, Computer-Aided Civil and Infrastructure Engineering, 21(6), 450-64.