Structural Health Monitoring is an exciting opportunity to use real time quantitative data of a structure’s response in analysis and evaluation. However, this technology has yet to achieve common use in practice and remains linked to research of iconic buildings. This paper discusses the challenges and opportunities for use of SHM for widespread projects with damaged buildings and limited budgets. The SHM approach used was long term low frequency (static) data collection of both environmental inputs and structural responses. This data was used to develop relationships between loads and responses that could be effectively used to determine safety of the building and where in the structure deterioration continues.
 Pines, D. and Aktan, A.E. Status of structural health monitoring of long‐span bridges in the United States. Prog. Struct. Engng Mater. (2002), 4: 372-380.
 Gulgec N.S., Shahidi G.S., Matarazzo T.J., and Pakzad S.N. Current Challenges with BIGDATA Analytics in Structural Health Monitoring. In: Niezrecki C. (Eds.): Structural Health Monitoring & Damage Detection, Volume 7. Conference Proceedings of the Society for Experimental Mechanics Series. Springer, Cham (2017).
 Oliveira, D., Ramos, L., Lourenco, P., and Roque, J. Structural Monitoring of the monastery of Jeronimos. Proceeding of the international conference on the 250th Anniversary of the 1755 Lisbon Earthquake (2005), pp.466-473.
 Ramos, L.F. Damage Identification on Masonry Structures Based on Vibration Signatures. PhD Thesis: Universidad do Minho – Escola de Engenharia (2007).
 Morrison, T.E. Advanced Numerical Tool to Analyze Monitoring Data, Master’s Thesis: University of Minho (2008).
 Peng, C., & Zhang, B. A note on goodness-of-fit test of continuation ratio logistic regression models under case-control data. Journal of Statistical Planning and Inference (2008) pp. 2355-2365.
 Oliveira, D.V. Experimental and Numerical Analysis of Blocky Masonry Structures Under Cyclic Loading. Universidade do Minho (2003).
 Soong, T. T. Fundamentals of Probability and Statistics for Engineers. Chichester, West Sussex, England: Wiley-Interscience (2004).
 Smith, S.W. Digital Signal Processing: A practical Guide for Engineers and Scientists. London: Newnes – An Imprint of Elsevier Science (2003).
 Peeters, B. System Identification and Damage Detection in Civil Engineering. PhD Thesis: Catholic University of Leuven, Belgium (2000).
 Green, P., & Silverman, B. Non-parametric Regression and Generalized Linear Models: A roughness penalty approach. London: Chapman & Hall / CRC (2005)
 Hosmer, D. W., & Lemeshow, S. Applied Logistic Regression: Second Edition. Chichester: Wiley-Interscience (2000).
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