Volume 15, Issue 1

Predictive maintenance and safety assessment during the construction and operational phase are becoming more and more important in modern tunnelling. However, traditional measurement methods are often time-consuming, expensive and partially require an interruption of the tunnel traffic. In this article, we present a tunnel monitoring approach based on distributed fibre optic sensing (DFOS), which delivers hundreds of strain and temperature sensing points inside the structure and gives completely new information about the behaviour of the tunnel lining. Measurements can be performed automatically without the need of access to the tunnel and hence, monitoring can be made without disturbing the tunnel construction or operation. The developed system was installed within the shotcrete tunnel lining of a railway tunnel under construction in Austria. In addition to the critical installation process, this article discusses the main monitoring results and compares them to conventional measurements.

Despite the increasing interest in kinematic data acquisition, Robotic Total Stations (RTSs) are still relatively seldom used. No matter if Mobile Mapping Systems or Control & Guidance, GNSS is mostly used as position sensor, which limits the application to outdoor areas. For indoor applications, a combination of relative sensors is usually employed. One reason why RTSs are not used is the challenging time referencing and synchronization. In this work we analyze the challenges of a synchronized kinematic application of RTSs and present solutions. Our approach is based on a wireless network synchronization to establish a precise temporal reference frame. The achievable synchronization quality is thoroughly examined. In addition we develop a kinematic model of spherical measurements, that incorporates timing related parameters. To estimate these parameters we propose a temporal calibration utilizing an industrial robot. Both parts of our approach are evaluated using a test setup of two total stations, proofing an overall synchronization accuracy of 0.2 ms. An overall horizontal kinematic point accuracy of 2.3 mm reveals the potential of sufficiently synchronized RTSs.

Mobile mapping vehicles, equipped with cameras, laser scanners (in this paper referred to as light detection and ranging, LiDAR), and positioning systems are limited to acquiring surface data. However, in this paper, a method to fuse both LiDAR and 3D ground penetrating radar (GPR) data into consistent georeferenced point clouds is presented, allowing imaging both the surface and subsurface. Objects such as pipes, cables, and wall structures are made visible as point clouds by thresholding the GPR signal’s Hilbert envelope. The results are verified with existing utility maps. Varying soil conditions, clutter, and noise complicate a fully automatized approach. Topographic correction of the GPR data, by using the LiDAR data, ensures a consistent ground height. Moreover, this work shows that the LiDAR point cloud, as a reference, increases the interpretability of GPR data and allows measuring distances between above ground and subsurface structures.

The paper concentrates on the iterative Getchell’s method (formulated in 1972) and its alternative Newtonian implementation for conversion of Cartesian geocentric coordinates into geodetic coordinates. The same basic equation formulated in the Getchell’s method is used in both cases. The equation has a stable form in the whole range of argument (latitude) variation ⟨−π/2,π/2⟩\langle -\pi /2,\pi /2\rangle . The original Getchell’s method (somehow “forgotten”) has a simple geometric interpretation and its applications turn out to be particularly effective. Many studies on iterative algorithms usually omit theoretical proofs of convergence replacing them with conclusions based on numerical examples. This paper presents theoretical proofs of algorithms convergence both for the Getchell’s method and the Newton procedure. The convergence parameter and numerical error of results were estimated in each case. Numerical tests were carried out for a set of points distributed on the Earth’s space, also for extreme h values. For typical practical applications of the Getchell’s method, sufficiently accurate results are obtained after 1–3 iterations, while in the Newton procedure already after one iteration, assuming the same numerical error and initial conditions. The accuracy of the geodetic coordinates determinations meets all practical requirements with some margin. For example an absolute numerical error for latitude is approx. 0.4·10−130.4\cdot {10^{-13}} [rad] i. e. about 0.00026 mm in the length of the meridian arc. The proposed methods were compared with other methods (algorithms), including in terms of stability and non-singularity in the entire usable space of the Earth, but excluding the near geocenter, which has no practical significance. Both the modification of the Getchell method and its Newtonian alternative are very good determined in this area (in the Earth’s poles, the final solution is directly the starting value of iterative algorithms). The discussed algorithms were implemented in the form of procedures in DELPHI language.

Long term GNSS observations provided by networks of the continuously operating reference stations (CORS) allow for determination of the global and local tectonic plate movements and seasonal variations. In recent years, PPP (Precise Point Positioning) technique has become increasingly popular and most likely in the future will replace relative positioning with CORS stations. In this paper, we discuss the difference of the velocity and seasonal component estimates of 25 Latvian CORS stations on the basis of daily PPP solutions from the Nevada Geodetic Laboratory and double-difference solutions from the Institute of Geodesy and Geoinformatics of the University of Latvia. Time series of each coordinate component for 9-year time period were determined by the usage of the Tsview software and seasonal existence of linear, annual, semi-annual factors and their uncertainties were determined. Breaks (e. g., antenna and receiver changes) were also taken into account. We then assessed the noise characteristics of these time series with the use of overlapping Hadamard variance (OHVAR). The result shows that OHVAR is computationally cheap, and the dominating power-law noise, including flicker and random walk. However Hadamard deviation of the PPP and double-difference solutions scatters differently for a whole year averaging time due to the different GNSS data strategies.

Satellite signal strength sometimes decreases when multipath exists. This effect reduces signal quality and can lead to a large static positioning error, even the survey-grade receivers are used. Three signal selection methods based on signal-to-noise ratio (SNR) measurements were proposed. The first was the conventional method, based on elevation-dependent average SNR, the second used a moving average of SNR fluctuation and the third method used NLOS exclusion based on SNR residual clustering by the K-means algorithm. To evaluate the positioning accuracy improvement, the static 1 Hz single-point positioning (SPP) test was performed in real-time in two different multipath environments using both dual and quad- constellation GNSS receivers. Trimble and CHC receivers were used at each point to examine the effect on each measurement. Results indicated that the three proposed methods mainly reduced multipath error in horizontal direction compared with the normal SPP.