Reliability and Precision of a Network for Monitoring Very Slow Movements with a Total Station
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Donatella Dominici, Vincenzo Massimi, and Lucia Simeoni
Abstract
This paper describes the monitoring system planned to measure the displacements of an extremely slow landslide in an alpine glacial valley in Northern Italy. Because of the smallness of the displacements, normally less than 1 cm/year, great attention has been paid to evaluate the reliability of the measurements by evaluating their precision and accuracy. For this purpose it was essential to make the system redundant by measuring the displacements with at least two different techniques: the inclinometers to monitor the subsurface displacements and the Total Station for measuring the surface displacements. With the increase of redundancy of the measures, there are more information to describe the landslide in terms of displacements, directions and rate, both superficial and deep. In this way, movements are better investigated and it is possible to highlight eventual disagree between the different techniques of measurement and improve the result’s accuracy. The paper focuses mainly on the characteristics of the network used with the Total Station, in order to define how they affect the precision and accuracy of the measurements. Thus, the aim of this research has been to derive some conclusions about the optimal monitoring surveying network in terms of reliability and precision, compatible with the type of slope movements and the morphology of the site. The strategies of elaboration and results obtained are presented in this paper.
19.1
Introduction
The studies of slow, very slow and extremely slow landslides should establish if the landslides have moved slowly since the past, and by inference are likely to continue to move slowly. Usually the studies are based largely on geomorphologic evidence, historic observations of deformation behaviour, and long-term deformation monitoring where available (Glastonbury and Fell 2008). In the latter case displacements are measured either at the ground surface, mainly by Total Station, extensometers or by remote sensing (SAR or GPS), or in the subsurface, usually by using inclinometers, or both. The measurements of displacements
D. Dominici V. Massimi (&) L. Simeoni University of L’Aquila (DICEAA), via Gronchi 18, 67100, L’Aquila, Italy e-mail:
[email protected]
are also used to define and calibrate a model of the landslide evolution (Vulliet and Hutter 1988; Puzrin and Schmidt 2012). In this case it is fundamental to be aware of the reliability degree of the measurements. This paper describes the monitoring system planned to measure the displacements of an extremely slow landslide, named T64, in an alpine glacial valley in Northern Italy (Simeoni et al. 2014). The landslide is a mass of tuff and ignimbrite debris set in a sandy to clayey matrix, its volume is of approximately 2.5 million of cube meters and occupies an area of about 100.000 square meters (Fig. 19.1). The sliding surface was located by inclinometers at a depth ranging from 23 to 36 m in the Northern part (inclinometers V12, T11, T8 and S4), and from 36 to 63 m in the Southern part (inclinometers S1, S9 and S2). Usually the Northern part of the landslide moves faster than the Southern one: the maximum average rate of displacement on the sliding surface was estimated at inclinometer S4 from December 2000 to June 2001 and was of about 1.5 cm in 6 months (Simeoni
G. Lollino et al. (eds.), Engineering Geology for Society and Territory – Volume 2, DOI: 10.1007/978-3-319-09057-3_19, © Springer International Publishing Switzerland 2015
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Fig. 19.1 Inclinometer and topographic network
and Mongiovì 2007) Since the inclinometer measurements include systematic errors (Pincent and Blondeau 1978; Mikkelsen 2003; Simeoni 2006) in 2004 the monitoring system was made redundant by extending it to the ground movements. A network by using a Total Station with a topographic monitoring network of 12 concrete monuments, 9 on the landslide and 3 on the opposite slope (Fig. 19.1), plus 3 prisms located on road structures at the toe of the landslides (between monuments M13 and M14) and 2 prisms installed to two rock blocks close to the SouthEastern scarp was planned. The aim of this research was to derive some conclusion about the optimal monitoring surveying network in terms of reliability and precision (Caspary 1987; Dominici 1989; Dominici et al. 1995). To achieve this objective, the raw data obtained from each campaign of measurements has been elaborated with a scientific software STARNET which apply the least square adjustment and statistic test based on Gauss Markov model allowing the estimation of the adjusted observation and analyze the residual distribution. In the following chapter the strategies of elaboration and the results obtained are presented.
19.2
Data Elaboration
19.2.1 Instrumentation and Measurements The measurements were carried out using a total station Leica TCA2003, which allow to obtain a good accuracy both in angle and in distance measurements. To minimize the movement of the instrumentation during the measurement operations a several number of sideburn and prism has been used. The structure of the concrete pillar guaranteed the perfect forced centering on planimetry, while for the altimetry was necessary to measure the height of the prisms and total station respect to the reference plane on the concrete pillar. Each measurement was reiterated three times reducing the value of the random error of prism collimation and the error of circle graduation. In 4 years, from 2005 till 2009, seven campaigns of measurement were carried out, the first on 21 April 2005, and the others on 22 September 2005, 21 November 2005, 22 March 2006, 14 May 2007, 9 June 2008 and 2 October 2009.
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Reliability and Precision of a Network for Monitoring
The number of measurements and relative configuration were kept fixed for each campaign of measurements making statistically comparable the results of adjustment between the various campaigns and therefore facilitating the application of the variance propagation law on the displacement computation between the various epoch (see Sect. 19.3).
19.2.2 Network Adjustment The raw data obtained from each campaign of measurements were processed with a scientific software STARNET which applies the least square adjustment and statistic test based on Gauss Markov model allowing the estimation of the adjusted observation and analyzes the residual distribution. The residual analysis is really important because allows to highlight the possible errors or weak point of the model. The input data consist in the distance, horizontal and vertical angle value which represents the observation equations, while the unknowns are represented by the value of the coordinates of the vertex of the monitoring network. Due Fig. 19.2 Adjusted network
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to the fact that there is no centering forced on altimetry, also the measurements of the height of the station and targets respect to the reference plane of the concrete pillar were used as input data for the adjustment. Regarding the standard error of the measurements, for the distance has been considered the value suggested by “technical details” of TCA2003 equal to 1 mm + 1 ppm while for horizontal and vertical angle has been considered the standard error of the mean as result of the strata elaboration. The adjustment was performed with the technic of fixed network, with the origin on point B3 and X axis oriented in the direction of the point B2 (Fig. 19.1) according to the reference system expected stable over time since points B1, B2, and B3 are located in the opposite slope of the landslide. As a result of the least square 3-D adjustment a general small value of standard deviation was obtained. It was less than a millimeter for all the planimetrical components of the coordinates, while it resulted with an average value of 1.5 mm for the altimetry, except for the points 12, 23, 25, and 26 which presented an average standard deviation of some millimeters, both in planimetry which altimetry due to the fact that the redundancy was smaller respect to the other
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vertex of the network. An example of adjusted network is shown in Fig. 19.2 with an exaggerated scale regarding the ellipses. The graph shows the high redundancy of the measurements and relative distribution. The Chi square test at 5 % of significance level demonstrated that the residuals are due to normal random errors since their values are similar to the initial standard error.
Fig. 19.3 Planimetrical displacements and relative uncertainties
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19.3
Displacement Computation
The displacements of the vertex of the monitoring network were computed as differences of coordinates between the different campaign of measure and applying the propagation of variance and covariance to evaluate the error propagation. In particular, the standard deviation respect to each component of displacement was computed applying the linear propagation law:
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Fig. 19.4 Altimetrical displacements and relative uncertainties
X DxDx
¼ A
X
AT
xx
where A ∑xx
design matrix. Variance/covariance matrix of the two different epochs.
It is reasonable to consider each epoch of measurement uncorrelated and then the ∑xx matrix is a diagonal matrix that contains the variance/covariance matrix obtained with each adjustment. Due to the fact that the precision and accuracy of the planimetrical components of the coordinate are different respect to the altimetrical ones, the three dimensional
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displacement was studied by separating planimetry and altimetry at the statistical level . Since the planimetrical displacement was computed with the non-linear function Δplan = (Δx2 + Δy2)1/2, the nonlinear propagation law to the single components Δx and Δy of displacement was applied to evaluate the standard deviation. In terms of equation can be expressed as: X Dplan
¼ J
X
JT
References
DxDx
With J = [d(Δplan)/dx; d(Δplan)/dy] (Jacobian matrix) The displacement and relative uncertainties both for planimetry and altimetry are shown on Figs. 19.3, 19.4. The graphs confirmed the high level of significance on planimetry for almost all the points except for the 25 and 26 in which the value of uncertainties is bigger and in some case exceeds the values of the displacement. This is due to the lower redundancy. The high quality of the network on planimetry is not confirmed in altimetry, since the value of uncertainties is bigger and then many vertical displacement cannot be considered significant.
19.4
investigations are necessary to understand if the displacements of the point B1, which should be outside of the landslide body, can be considered significant. In this case, the stability of the local reference system will be verified using other possible techniques of surface measurement as GNSS and DInSAR.
Conclusions
In conclusion this work confirmed that the reliability of the monitoring network enhance when the influence of random and systematic errors are reduced using precise measure instruments and an adequate strategies of measure with high redundancy. With this experience has been highlighted also the importance of three-dimensional centering forced on the displace monitoring to improve the precision and accuracy of the measurement on the vertical component. More
Caspary WF (1987) Concepts of network and deformation analysis. Monograph 11, School of Surveying, The University of South Wales, Kensington, Australia Dominici D (1989) Tecniche di analisi di reti tridimensionali di controllo rilevate con metodi classici e GPS, Phd Thesis, pp 135–139 Dominici D, Radiciomi F, Stoppini A, Unguendoli M (1995) Precision and reliability versus surplus measurement in GPS networks, bollettino di geodesia e scienze affini N. 4 Glastonbury J, Fell R (2008) Geotechnical characteristics of large slow, very slow, and extremely slow landslides. Can Geotech J 45 (7):984–1005 Mikkelsen PE (2003) Advances in inclinometer data analysis. In: Proceedings of the 6th international symposium on field measurements in geomechanics, Oslo, Norway, pp 555–567, 15–18 Sept 2003 Pincent B, Blondeau F (1978) Detection et suivi des glissements de terrain. In: Proceedings of the 3th international congress IAEG, Madrid, 1978, vol 1, issue 1, pp 252–266 Puzrin AM, Schmidt A (2012) Evolution of stabilised creeping landslides. Géotechnique 62(6):491–501 Simeoni L (2006) Effects of the instruments bias on the reliability of manual inclinometer measures. In: Proceedings of the XIII DanubeEuropean conference on geotechnical engineering, active geotechnical design in infrastructure development, Ljubljana, pp 509–514, 29–31 May 2006 Simeoni L, Mongiovì L (2007) Inclinometer monitoring of the Castelrotto landslide in Italy. J Geotech Geoenviron Eng 133(6):653–666 Simeoni L, Ronchetti F, Corsini A, Mongiovì L (2014) Interaction of extremely slow landslides with transport structures in the alpine glacial Isarco valley. In: Proceedings of the XII IAEG congress, section 2.3, Torino (Italy), 15–19 Sept 2014 Vulliet L, Hutter K (1988) Viscous-type sliding laws for landslides. Can Geotech J 25(3):467–477
