Wojciech Pachelski

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).

Optimal design of geodetic network is a basic subject of many engineering projects. An observation plan is a concluding part of the process. Any particular observation within the network has through adjustment a different contribution and impact on values and accuracy characteristics of unknowns. The problem of optimal design can be solved by means of computer simulation. This paper presents a new method of simulation based on sequential estimation of individual observations in a step-by-step manner, by means of the so-called filtering equations. The algorithm aims at satisfying different criteria of accuracy according to various interpretations of the covariance matrix. Apart of them, the optimization criterion is also amount of effort, defined as the minimum number of observations required. A numerical example of a 2-D network is illustrated to view the effectiveness of presented method. The results show decrease of the number of observations by 66% with respect to the not optimized observation plan, which still satisfy the assumed accuracy.