<abstract xmlns="http://www.w3.org/1999/xhtml"><p> 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).</p></abstract>

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).

Real Time Search Algorithm for Observation Outliers During Monitoring Engineering Constructions

Reports on Geodesy and Geoinformatics

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

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...

Real Time Search Algorithm for Observation Outliers During Monitoring Engineering Constructions

Online veröffentlicht: 23. Jan. 2018

Seitenbereich: 103 - 114

Eingereicht: 18. Okt. 2016

Akzeptiert: 14. Dez. 2017

DOI: https://doi.org/10.1515/rgg-2017-0019

Schlüsselwörter
time series analysis, construction monitoring, automation of data analysis

© 2018

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Real Time Search Algorithm for Observation Outliers During Monitoring Engineering Constructions

Online veröffentlicht: 23. Jan. 2018

Seitenbereich: 103 - 114

Eingereicht: 18. Okt. 2016

Akzeptiert: 14. Dez. 2017

DOI: https://doi.org/10.1515/rgg-2017-0019

Schlüsselwörtertime series analysis, construction monitoring, automation of data analysis

© 2018

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Schlüsselwörter
time series analysis, construction monitoring, automation of data analysis