Large buildings and skyscrapers are vulnerable to environmental, temporal, and anthropological stresses, generating wear and tear that can lead to this social and economic impediment's collapse. The technological improvements of the fourth industrial revolution have resulted in changes in the connection between physical space and man, known as the cyber physic model, which necessitates monitoring systems to protect the structural branch and so correct this structural vulnerability. Thus, the structural health monitoring system is the exact measure of the evolution required by the cyber physic model in construction and the protection of the monumental buildings, ensuring not only their economic development but also the safety of society. Therefore, this research work presents the innovative proposal of the cyber-physical structural health monitoring system aimed at buildings and skyscrapers, based on and differentiated by intelligent computing techniques, using the negative selection algorithm to perform the analysis and monitoring of structural integrity, overcoming the existing traditional work. The cyber-physical structural health monitoring system will be applied to experimental data obtained from the shear building model that represents these imposing skyscrapers. An artificial immune system will be developed and used in the decision-making process based on the acquisition and processing of the obtained signals to perform the identification, localization, and quantification of possible structural damage. Observing the results, this work proved to be efficient, robust, and economically feasible, having high performance and overcoming the shortcomings of traditional techniques. It represents the perfect measure of cyber physics in the monitoring of large buildings and skyscrapers.
Magruk A. Uncertainty in the Sphere of the Industry 4.0-Potential Areas to Research. Business, Management and Education 2016; 14(2): pp. 275-291. https://doi.org/10.3846/bme.2016.332
Popkova EG, Ragulina YV, Bogoviz AV. Industry 4.0: Industrial Revolution of the 21st Century, Springer International, 2019. https://doi.org/10.1007/978-3-319-94310-7
Huxtable J, Schaefer D. On Servitization of the Manufacturing Industry in the UK. Procedia CIRP 2016; 52: pp. 46-51. https://doi.org/10.1016/j.procir.2016.07.042
Chen Y, Li Y. Computational Intelligence Assisted Design in Industrial Revolution 4.0, CRC Press, Boca Raton, 2018. https://doi.org/10.1201/9781315153179
Abreu CCE, Chavarette FR, Alvarado FV, Duarte MAQ, Lima FPA. Dual-Tree Complex Wavelet Transform Applied to Fault Monitoring and Identification in Aeronautical Structures, International Journal of Pure and Applied Mathematics 2014; 97: pp. 89-97. https://doi.org/10.12732/ijpam.v97i1.9
Moro TC, Chavarette FR, Roéfero LGP, Outa R. Detection of Structural Failures of a Two Floor Building Using an Artificial Immunological System. In Colloquium Exactarum. 2019; 11(4): pp. 73-84. https://doi.org/10.5747/ce.2019.v11.n4.e298
Balageas D, Fritzen CP, Güemes A. Structural health monitoring, 90, John Wiley Sons, 2010.
Farrar CR, Worden K. Structural Health Monitoring: A Machine Learning Perspective, John Wiley, Chichester, 2013. https://doi.org/10.1002/9781118443118
Gopalakrishnan S, Ruzzene M, Hanagud S. Computational Techniques for Structural Health Monitoring, Springer-Verlag, London, 2011. https://doi.org/10.1007/978-0-85729-284-1
 Dhapekar NK, Chopkar DM. Structural health monitoring of ordinary portland cement concrete structures using X-ray diffraction. International Journal of Applied Engineering Research, 2016; 11(9): pp. 6128-6131.
Wang X, Hatziargyriou N, Tsoukalas L. A new methodology for nodal load forecasting in deregulated power systems, 2002. https://doi.org/10.1109/39.999661
Yang MH, Kriegman DJ, Ahuja, N. Detecting faces in images: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002; 24(1). https://doi.org/10.1109/34.982883
Forrest S, Perelson AS, Allen L, Cherukuri R. Self-nonself discrimination in a computer, In: Proceeding of the IEEE Symposium on Research in Security and Privacy, Oakland, 1994; pp. 202-212.
Bradley DW, Tyrrell AM. Immunotronics - novel finite-state-machine architectures with built-in self-test using self-nonself differentiation, IEEE Transactions on Evolutionary Computation, 2002; 6(3): pp. 227-238. https://doi.org/10.1109/TEVC.2002.1011538
Dasgupta D, Niño LF. Immunological Computation: Theory and Applications, Taylor and Francis Group, Boca Raton, 2009. https://doi.org/10.1201/9781420065466
Lima FPA, Chavarette FR, Souza ASE, Souza SSF, Opes MLM. Artificial imune systems with negative selection applied to health monitoring of aeronautical structures, Advanced Materials Research, 2013; 871: pp. 283-289. https://doi.org/10.4028/www.scientific.net/AMR.871.283
De Castro LN, Timmis J. Artificial immune systems as a novel soft computing paradigm, Soft Computing Journal, 2003; 7(8): pp. 526-544. https://doi.org/10.1007/s00500-002-0237-z
Atalla N, Sgard F. Finite element and boundary methods in structural acoustics and vibration. CRC Press, 2015. https://doi.org/10.1201/b18366
Kanai K. An empirical formula for the spectrum of strong earthquake motions, Bulletin earthquakes research institute, University of Tokyo 1961; 39: pp. 85-95.
Xenos HG. Gerenciando a manutenção produtiva. Belo Horizonte: Editora de desenvolvimento gerencial, 1998.
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