Real time monitoring of engineering structures in case of an emergency of disaster requires collection of a large amount of data to be processed by specific analytical techniques. A quick and accurate assessment of the state of the object is crucial for a probable rescue action. One of the more significant evaluation methods of large sets of data, either collected during a specified interval of time or permanently, is the time series analysis. In this paper presented is a search algorithm for those time series elements which deviate from their values expected during monitoring. Quick and proper detection of observations indicating anomalous behavior of the structure allows to take a variety of preventive actions. In the algorithm, the mathematical formulae used provide maximal sensitivity to detect even minimal changes in the object’s behavior. The sensitivity analyses were conducted for the algorithm of moving average as well as for the Douglas-Peucker algorithm used in generalization of linear objects in GIS. In addition to determining the size of deviations from the average it was used the so-called Hausdorff distance. The carried out simulation and verification of laboratory survey data showed that the approach provides sufficient sensitivity for automatic real time analysis of large amount of data obtained from different and various sensors (total stations, leveling, camera, radar).
Baszkiewicz, K., B. Kolanowski, D. Latos, D. Krembuszewski, W. Pachelski & R. Sołoducha (2014). The selected methods of measuring deformations as a part of a system for monitoring structures, in Selected papers the 9th International Conference on Environmental Engineering, Vilnius, 2014.
Baszkiewicz, K., B. Kolanowski, D. Latos, W. Pachelski & R. Sołoducha (2014). Application of mathematical modeling and computer simulation to assess the technical parameters of sensors for measurement, Theoretical foundations of civil engineering, volume VI, Surveying measurement systems, Warsaw, Poland, 2014.
Baszkiewicz, K., B. Kolanowski, D. Latos, R. Sołoducha & W. Pachelski (2014). The concept of the system monitoring the State of the object and to assist the conduct of the rescue operation, Logistics 5/2014, 2014.
Box G. E. P. & G.M. Jenkins (1976). Time series analysis: Forecasting and control, Holden-Day, San Francisco, 1976.
Chatfield, C. (2013). The Analysis of Time Series: An Introduction, Sixth Edition, CRC Press, 2013.
Douglas, D. & T. Peucker (1973). Algorithms for the reduction of the number of points required to represent a digitized line or its caricature, The Canadian Cartographer, pp. 112-122, 1973.
Glass, G. V., V. l. Willson & J.M. Gottman (2008). Design and Analysis of Time-series Experiments, The Information Age Publishing, Inc., 2008.
Heckert, N. A. & J.J. Filliben (2003). NIST/SEMATECH e-Handbook of Statistical Methods, 2003/2013.
Herburt, I. & M. Moszyńska (2014). Tool Geometry, Center for the study of Advanced at the Warsaw University of Technology, 2014.
Liu, L. M. & G.B. Hudak (1992). Forecasting and time series analysis using the SCA statistical system, Scientific Computing Associates Corp., Chicago, Illinois 60607- 3528, 1992-1994.
Machura, Ł. (2012). Time-series Analysis, University of Silesia, Katowice, 2012.
Ramer, U. (1972). An iteractive procedure for the polygonal approximation of plane curves, Computer Graphics and Image Processing, p. 244-256, 1972.
Revel, M. (2012). Statistics in Bayesian terms, Katowice: UPGOW-Silesian University, 2012.
Skłodowski, M. (2009). Contemporary monitoring of construction, Construction Review, pp. 37-46, # 3 2009.
Smith, de M. J. (2015). Statistical Analysis Handbook http://www.statsref.com/HTML/index.html, 2015.
Sunday, D. (2012). Polyline decimation. Available online: http://geomalgorithms.com/a16-_decimate-1.html. (Date of access: 12 01 2016).
The National Institute of Standards and Technology, http://www.nist.gov/public_affairs/nandyou.cfm, 19.08.2009. (Online). Available: http://www.itl.nist.gov/div898/handbook/index.htm. (Date of access: 8.9.2015).