Development of heterogeneous multisensor data fusion system to improve evaluation of concrete structures

References

• Abdollahi, F., 2011. Multi-layer perceptron [online], Technical document. Amirkabir University of Technology. Available from: http://ele.aut.ac.ir/~abdollahi/Lec_2_NN11.pdf [Accessed 31 May 2013].
   Google Scholar
• Adeli, H., 2001. Neural networks in civil engineering: 1989–2000. Computer-Aided Civil and Infrastructure Engineering, 16 (2), 126–142. doi:10.1111/0885-9507.00219
   Web of Science ®Google Scholar
• Akiwowo, A. and Eftekhari, M., 2013. Feature-based detection using Bayesian data fusion. International Journal of Image and Data Fusion, 4 (4), 308–323. doi:10.1080/19479832.2013.824029
   Google Scholar
• Awcock, G.W. and Thomas, R., 1996. Segmentation. In: G.J. Awcock and R. Thomas, eds. Applied image processing. 1st ed. New York: McGraw-Hill, 300–325.
   Google Scholar
• Bao, Y. and Li, H., 2007. Application of information fusion and Shannon entropy in structural damage detection. In: Proceedings of health monitoring of structural and biological systems, 19–22 March San Diego, CA.
   Google Scholar
• Basir, O. and Yuan, X., 2007. Engine fault diagnosis based on multi-sensor information fusion using Dempster-Shafer evidence theory. Information Fusion, 8 (4), 379–386. doi:10.1016/j.inffus.2005.07.003
   Web of Science ®Google Scholar
• Binal, A., 2004. A new experimental method and device for measuring alkali–silica gel pressure in mortar. In: T. Mingshu and D. Min, eds. Proceedings of the 12th ICAAR, 15–19 October Beijing. Beijing: International Academic Publishers/World Publishing Corporation, 266–271.
   Google Scholar
• Binal, A., 2008. The determination of gel swelling pressure of reactive aggregates by ASGPM device and a new reactive-innocuous aggregate decision chart. Construction and Building Materials, 22 (1), 1–13. doi:10.1016/j.conbuildmat.2007.07.021
   Web of Science ®Google Scholar
• Brouwers, H.J.H. and van Eijk, R.J., 2003. Alkali concentrations of pore solution in hydrating OPC. Cement and Concrete Research, 33 (2), 191–196. doi:10.1016/S0008-8846(02)01022-0
   Web of Science ®Google Scholar
• Byvatov, E., et al., 2003. Comparison of support vector machine and artificial neural network systems for drug/nondrug classification. Journal of Chemical Information and Computer Sciences, 43, 1882–1889.
   PubMedGoogle Scholar
• Capra, B. and Sellier, A., 2003. Orthotropic modelling of alkali-aggregate reaction in concrete structures: numerical simulations. Mechanics of Materials, 35, 817–830. doi:10.1016/S0167-6636(02)00209-0
   Web of Science ®Google Scholar
• Cooper, J.C.B., 1999. Artificial neural networks versus multivariate statistics: an application from economics. Journal of Applied Statistics, 26 (8), 909–921. doi:10.1080/02664769921927
   Web of Science ®Google Scholar
• Du, C., 2010. Dealing with alkali-aggregate reaction in hydraulic structures, renewable energy world magazine [online]. Available from: http://www.renewableenergyworld.com/rea/news/print/article/2010/08/dealing-with-alkali-aggregate-reaction-in-hydraulic-structures [Accessed 15 February 2013].
   Google Scholar
• Durrant-Whyte, H., 2000. A beginner's guide to decentralized data fusion, technical document of Australian centre for field robotics. University of Sydney.
   Google Scholar
• Durrant-Whyte, H. and Henderson, T., 2008. Multisensor data fusion. In: S. Bruno and K. Oussama, eds. Springer handbook of robotics. 1st ed. Berlin: Springer, 585–608.
   Google Scholar
• Fung, T. and Ledrew, E., 1988. The determination of optimal threshold levels for change detection using various accuracy indices. Photogrammetric Engineering and Remote Sensing, 54 (10), 1449–1454.
   Web of Science ®Google Scholar
• Garrels, R.M. and Christ, C.L., eds., 1965. Activity-concentration relations. In: Solutions, minerals and equilibrium. Boston, MA: Jones & Bartlett, 450–467.
   Google Scholar
• Gdoutos, E.E., Rodopoulos, C.A., and Yates, J.R., 2003. Critical stress intensity factor fracture criterion. In: E.E. Gdoutos, ed. Problems of fracture mechanics and fatigue – a solution guide. Dordrecht: Kluwer Academic, 618–637.
   Google Scholar
• Gros, X.E., Bousigue, J., and Takahashi, K., 1999. NDT data fusion at pixel level. NDT & E International, 32 (5), 283–292. doi:10.1016/S0963-8695(98)00056-5
   Web of Science ®Google Scholar
• Guo, H. and Zhang, L., 2006. A weighted balance evidence theory for structural multiple damage localization. Computer Methods in Applied Mechanics and Engineering, 195 (44–45), 6225–6238.
   Web of Science ®Google Scholar
• Guo, H.Y., 2006. Structural damage detection using information fusion technique. Mechanical Systems and Signal Processing, 20 (5), 1173–1188. doi:10.1016/j.ymssp.2005.02.006
   Web of Science ®Google Scholar
• Guo, H.Y. and Li, Z.L., 2009. A two-stage method to identify structural damage sites and extents by using evidence theory and micro-search genetic algorithm. Mechanical Systems and Signal Processing, 23 (3), 769–782. doi:10.1016/j.ymssp.2008.07.008
   Web of Science ®Google Scholar
• Hahn, H.G., 1976. Fracture mechanics. Stuttgart: Universität Stuttgart Press, 133–165.
   Google Scholar
• Hall, D.L., 1992. Introduction to multisensor data fusion. In: D.L. Hall and S.A.H. McMullen, eds. Mathematical techniques in multi-sensor data fusion. 1st ed. Boston, MA: Artech House, 3–18.
   Google Scholar
• Jiang, S.-F., Zhang, C., and Koh, C.G., 2006. Structural damage detection by integrating data fusion and probabilistic neural network. Advances in Structural Engineering, 9 (4), 445–458. doi:10.1260/136943306778812787
   Web of Science ®Google Scholar
• Johnson, E.A., et al., 2000. A benchmark problem for structural health monitoring and damage detection. In: F. Casciati and G. Magonette, eds. Proceedings of international symposium on engineering mechanics, 6–8 July Paris. Singapore: World Scientific, 317–324.
   Google Scholar
• Kabir, S., 2010. Imaging-based detection of AAR induced map-crack damage in concrete structure. NDT & E International, 43 (6), 461–469. doi:10.1016/j.ndteint.2010.04.007
   Web of Science ®Google Scholar
• Kanta, A., et al., 2008. Artificial neural networks vs. fuzzy logic: simple tools to predict and control complex processes – application to plasma spray processes. Journal of Thermal Spray Technology, 17 (3), 365–376. doi:10.1007/s11666-008-9183-3
   Web of Science ®Google Scholar
• Kawamura, M. and Iwahori, K., 2004. Some theoretical considerations on expansive pressure of ASR Gel. In: Proceedings of the 12th ICAAR, 15–19 October Beijing. Beijing: International Academic Publisher/World Publishing Corporation, 135–142.
   Google Scholar
• Kumar, P., et al., 2011. Performance evaluation of decision tree versus artificial neural network based classifiers in diversity of datasets. In: Proceedings of World Congress on Information and Communication Technologies (WICT), 11–14 December Mumbai. IEEE Explore, 798–803. doi:10.1109/WICT.2011.6141349
   Google Scholar
• Li, K. and Coussy, O., 2002. Concrete ASR degradation: from material modelling to structure assessment. Concrete Science and Engineering, 4, 35–46.
   Google Scholar
• Makhtar, M., et al., 2011. Comparing multi-class classifiers: on the similarity of confusion matrices for predictive toxicology applications. In: Y. Hujun, W. Wenjia, and R.-S. Victor, eds. Intelligent data engineering and automated learning. 1st ed. Berlin: Springer, 252–261.
   Google Scholar
• Mansfeld, T., 2008. Swelling behaviour of alkali–silica gels with consideration of their structure and composition. Thesis (PhD), Bauhaus-Universität, Weimar.
   Google Scholar
• Mowafi, M.Y. and Aljoby, W.A., 2013. Lightweight target classification for wireless multimedia sensor networks. International Journal of Image and Data Fusion, 4 (4), 293–307. doi:10.1080/19479832.2013.829128
   Google Scholar
• Murakami, Y., 1987. Finite width plate containing two-dimensional cracks. In: Y. Itoh, ed. Stress intensity factors handbook. New York: Pergamon Press, 115–126.
   Google Scholar
• Nery, G., et al., 2010. Development of a remote monitoring strategy for concrete building foundations affected by alkali-aggregate reaction. In: Proceedings of 6th international conference on concrete under severe conditions environment & loading, 7–9 June Merida. Stockholm: Sweden ScienceNet, 1211–1218.
   Google Scholar
• Oberholster, R.E. and Davies, G., 1986. An accelerated method for testing the potential alkali reactivity of siliceous aggregates. Cement and Concrete Research, 16 (2), 181–189. doi:10.1016/0008-8846(86)90134-1
   Web of Science ®Google Scholar
• Oruc, S., Sijs, J., and Van den Bosch, P.P.J., 2009. Optimal decentralized Kalman filter. In: Proceedings of 17th Mediterranean conference on control and automation, 24–26 June Thessaloniki. IEEE Explore, 803–808. doi:10.1109/MED.2009.5164642
   Google Scholar
• Quadri, S.A. and Sidek, O., 2013. Decentralized data fusion algorithm using factor analysis model. Applied Mechanics and Materials, 278–280, 1182–1186. doi:10.4028/www.scientific.net/AMM.278-280.1182
   Google Scholar
• Reinhardt, H.W. and Mielich, O., 2011. A fracture mechanics approach to the crack formation in alkali-sensitive grains. Cement and Concrete Research, 41 (3), 255–262. doi:10.1016/j.cemconres.2010.11.008
   Web of Science ®Google Scholar
• Sargolzahi, M., et al., 2010. Effectiveness of non-destructive testing for the evaluation of alkali-silica reaction in concrete. Construction & Building Materials, 24 (8), 1398–1403. doi:10.1016/j.conbuildmat.2010.01.018
   Web of Science ®Google Scholar
• Sidek, O. and Quadri, S.A., 2012. A review of data fusion models and systems. International Journal of Image and Data Fusion, 3 (1), 3–21. doi:10.1080/19479832.2011.645888
   Google Scholar
• Sidek, O., Kabir, S., and Quadri, S.A., 2011. Multi-sensor data fusion system for enhanced analysis of deterioration in concrete structures. In: Progress Electromagnetic Research Symposium Proceedings [online], 12–16 September Suzhou. Cambridge, MA: Electromagnetics Academy, 637–641. Available from: http://piers.org/piersproceedings/download.php?file=cGllcnMyMDExU3V6aG91fDJQNV8wNjM3LnBkZnwxMTA0MjcwMzEyMzc= [Accessed 7 March 2014]. 
   Google Scholar
• Stark, J. and Wicht, B., eds., 2001. Alkali aggregate reaction. In: Durability of concrete. Basel: Birkhauser, 340–352.
   Google Scholar
• Struble, L.J. and Diamond, S., 1981. Swelling properties of synthetic alkali silica gels. Journal of the American Ceramic Society, 64 (11), 652–655. doi:10.1111/j.1151-2916.1981.tb15864.x
   Web of Science ®Google Scholar
• Tada, H., Paris, P.C., and Irwin, G.R., eds., 1985. Stress analysis results for common test specimen configurations. In: The stress analysis of cracks handbook. 1st ed. St. Louis, MO: Del Research Corporation, 34–57.
   Google Scholar
• Ulm, F.J., et al., 2000. Thermo-chemo-mechanics of ASR expansion in concrete structures. Journal of Engineering Mechanics, 126 (3), 233–242. doi:10.1061/(ASCE)0733-9399(2000)126:3(233)
   Web of Science ®Google Scholar
• Xu, D., et al., 2010. Identifying technology for structural damage based on the impedance analysis of piezoelectric sensor. Construction and Building Materials, 24, 2522–2527. doi:10.1016/j.conbuildmat.2010.06.004
   Web of Science ®Google Scholar
• Yang, B.-S. and Kim, K.J., 2006. Application of Dempster-Shafer theory in fault diagnosis of induction motors using vibration and current signals. Mechanical Systems and Signal Processing, 20 (2), 403–420. doi:10.1016/j.ymssp.2004.10.010
   Web of Science ®Google Scholar
• Zhang, C. and Wang, H., 2010. Decentralized multi-sensor data fusion algorithm using information filter. In: Proceedings of International Conference on Measuring Technology and Mechatronics Automation (ICMTMA), 13–14 March Changsha City. IEEE Explore, 890–893. doi:10.1109/ICMTMA.2010.506
   Google Scholar
• Zhao, X., et al., 2010. Structural health monitoring with data fusion method. In: G. Song and R.B. Malla, eds. Proceedings of earth and space 2010: engineering, science, construction, and operations in challenging environments, March Honolulu. Reston, VA: American Society of Civil Engineers, 2509–2517. doi:10.1061/41096(366)234
   Google Scholar