Abstract
In urban areas, severe hazardous scenarios can occur with nonnegligible frequency. Large cities are complex systems, in which several complex subsystems interact. Some of the interacting sub-systems are only slightly influenced by the others, but can impact heavily themselves onto the others. The structures of civil and industrial constructions have such nature. The vulnerability reduction and control is a task that shall be programmed in condition of limited resources. Structural Condition Monitoring (CM) can help to manage it efficiently. Most of the strategic constructions to which a monitoring system can be applied are existing buildings, sometimes ancient, whose mechanical behavior is hard to assess due to large uncertainties. A reliable monitoring application shall be robust and resilient itself. Redundant distributed sensor networks, designed after an accurate risk analysis, shall have reasonably low cost. Data management, damage assessment and model updating procedures shall be stochastic and robust themselves. Holistic dynamics and multimodel optimization are effective methods with common characters.
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References
Bonato, P., Ceravolo R., De Stefano A., and Molinari F., 2000, Application of the time-frequency estimators method to the identification of masonry buildings, Mechanical Systems and Signal Processing 14:91-109.
Cempel, C., 2003, Multidimensional condition monitoring of mechanical systems in operation, Mechanical Systems and Signal Processing 17(6):1291-1303.
Cempel, C., and Tabszewski, M., 2007, Multidimensional condition monitoring of machines in non-stationary operation, Mechanical Systems and Signal Processing 21:1233-1241.
Cempel, C., Natke, H. G., and Yao, J. T. P., 2000, Symptom reliability and hazard for systems condition monitoring, Mechanical Systems and Signal Processing 14(3): 495-505.
Green, M. A., 1987, High Efficiency Silicon Solar Cells, Trans Tech Publications, Switzerland.
Lawless, J. F., 1982, Statistical Models and Methods for Lifetime Data, Wiley, New York.
Mishing, Y., 2004, in: Diffusion Processes in Advanced Technological Materials, D. Gupta, ed., Noyes/William Andrew, Norwich, NY.
Natke, H. G., and Cempel, C., 2001, System observation matrix for monitoring and diagnosis, Journal of Sound and Vibration 248:597-620.
Raphael, B., and Smith, I. F. C., 2002, A direct stochastic algorithm for global search, Applied Mathematics and Computation 146:729-758.
Saitta, S., Raphael, B., and Smith, I. F. C., 2005, Data mining techniques for impro-ving the reliability of system identification, Advanced Engineering Informatics 19:289-298.
Smith, I. F. C., Saitta, S., Ravindran, S., and Kripakaran, P., 2006, Challenges of data interpretation, in: Proc. 18th SAMCO Workshop, 37-57.
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De Stefano, A., Matta, E. (2008). Risk, Reliability, Uncertainties: Role and Strategies for the Structural Health Monitoring. In: Pasman, H.J., Kirillov, I.A. (eds) Resilience of Cities to Terrorist and other Threats. NATO Science for Peace and Security Series Series C: Environmental Security. Springer, Dordrecht. https://doi.org/10.1007/978-1-4020-8489-8_15
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DOI: https://doi.org/10.1007/978-1-4020-8489-8_15
Publisher Name: Springer, Dordrecht
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