Detecting the Unstable Points in Deformation Monitoring Geodetic Networks in Analysis Method of Subnetwork

Peyman Javadi

doi:10.5539/mas.v11n3p61

Abstract

One of the most crucial issues in engineering of structure and investigating ground deformation is deformation monitoring. The only thing which is strongly required is to create microgeodesy networks. An essential issue in microgeodesy networks is detecting unstable points of network. L1-Norm minimization and the global congruency can be noted as one of the classical methods for identifying network unstable points. In all previously conducted studies regarding this issue, results distinctly demonstrates that when displacement point vector is small, the number of points which have really displaced is more than that of true detection of displaced points using common deformation analysis ways. The probable reason for that can refer to spreading nature of the least squares estimation. Considering the results of recent studies in the detecting the network unstable points, to tackle the limitation the idea of subnetwork analysis is offered. In this case, some subnetworks including a subject point and the other source points appeared from dividing the deformation monitoring network. According to the unstable points, subnetworks will be there. This method will enable us to investigate the stable and unstable points. Having divided whole network to subnetworks, each network would be adjusted and unstable points of it would be detected. So, unstable points and their relations are cutoff and spreading effect of the least squares is fallen. This paper is on effort to evaluate the method in a simulated and a real network. The results prove that in a better and correct detection of unstable point can be successfully achieved by using subnetwork analysis compared to global congruency test all stimulates states proved the 35% of improvement on average. One percent of improvement in the results of subnetwork method to L1-Norm minimization cannot be acceptable. The algorithms of detecting unstable points in common methods and the method of analyzing subnetwork were conducted on a real network and the results are in line with simulated network results.

Highlights

Nowadays behavior evaluation of big and sensitive structures such as dams, power plants and towers is of very high importance
In second scenario in Global congruency test method the improvement will equal to 18 percent and in L1-norm minimization method it will equal to 2 percent
Global congruency test and L1-norm minimization were performed in two overall network and subnetwork analysis on some simulated data and the results were compared

Summary

Introduction

Nowadays behavior evaluation of big and sensitive structures such as dams, power plants and towers is of very high importance. Gauge tools of tension, shear and deflection inside the structure are installed during configuration and the data resulted from these gauges are continuously studied during and after optimization the structure in order to stability control These tools provide the possibility of internal control of structure. A network of points is created on the body and around the environment of structure and is monitored and controlled through geodetic observations mostly the length, angle and coordinates in different epochs These observations provide the possibility of the deformation monitoring of the outer structure

Methods

Results

Conclusion