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New Inclinometer Device for Monitoring of Underground Displacements and Landslide Activity

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The paper illustrates the theoretical basis of a new device called MUMS - Modular Underground Monitoring System - and one of its applications. MUMS has been designed for monitoring of underground displacements through a continuous and automated data acquisition system. MUMS instrumentation can be used to monitor the deformation of natural and artificial slopes as well as geotechnical structures. The device consists of a series of nodes located at known distances along a connecting rope and is installed within a vertical borehole. Each node measures its local rotation relative to the vertical axis by means of a 3D micro electro-mechanical acceleration sensor (MEMS). The direction cosines of each node are calculated in order to determine the 3D shape and deformation of the entire borehole. The paper illustrates an interesting application of MUMS in natural slopes and points out the benefits of the system.
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1. Description of MUMS instrumentation
The MUMS instrumentation [1] consists of a ‘chain’ of nodes
which are located at known distances from each other and which
are connected by an aramid fibre rope. Each node is equipped
with both a 3D micro electro-mechanical acceleration sensor
(MEMS) and a 3D digital magnetic sensor. The first sensor
measures its orientation relative to the vertical (gravitational
acceleration direction, Fig. 1 a), while the second one enables
to determine the heading (azimuth) of each node related to the
magnetic North, (Fig. 1 b).
Raw data are affected by several errors principally related to
sensor limitations and accuracy, to instrument assembly, as well
as to local magnetic anomalies that may be present.
Instrumental errors (due to sensor technology and assembly)
can be reduced through the calibration procedure [2] which is
carried out at the production laboratory. Errors related to local
magnetic anomalies can be corrected once the vertical axis
direction is known from accelerometer measurement since the
node is supposed to be still at each reading and gravity is the only
acceleration being present.
Each node is also equipped with a temperature sensor
particularly useful on sites characterized by severe and changing
environmental conditions.
Thanks to the three previously exanimated sensors, each node
returns at least 7 data sets. Increasing the complexity of nodes,
the amount of data to be transferred increases as well; therefore,
an innovative connecting system has been applied. This system
uses a supply cable of 4 poles with serial queries and transmits
data of each node.
Fig. 1 Modular Underground Monitoring System a) Schematic
representation. Xb, Yb and Zb are the global reference system axes (as
further defined) while Xbt, Ybt and Zbt are the local reference systems
at each node centre; b) Schematic representation of the single MUMS
orientation node
NEW INCLINOMETER DEVICE FOR MONITORING
OF UNDERGROUND DISPLACEMENTS
AND LANDSLIDE ACTIVITY
NEW INCLINOMETER DEVICE FOR MONITORING
OF UNDERGROUND DISPLACEMENTS
AND LANDSLIDE ACTIVITY
Andrea Segalini - Luca Chiapponi - Marian Drusa - Benedetta Pastarini *
The paper illustrates the theoretical basis of a new device called MUMS – Modular Underground Monitoring System – and one of its
applications. MUMS has been designed for monitoring of underground displacements through a continuous and automated data acquisition
system. MUMS instrumentation can be used to monitor the deformation of natural and artificial slopes as well as geotechnical structures.
The device consists of a series of nodes located at known distances along a connecting rope and is installed within a vertical borehole. Each
node measures its local rotation relative to the vertical axis by means of a 3D micro electro-mechanical acceleration sensor (MEMS). The
direction cosines of each node are calculated in order to determine the 3D shape and deformation of the entire borehole. The paper illustrates
an interesting application of MUMS in natural slopes and points out the benefits of the system.
Keywords: Landslide and geotechnical monitoring, early warning systems, MEMS technology.
* 1Andrea Segalini, 1Luca Chiapponi, 2Marian Drusa, 1Benedetta Pastarini
1Department of Civil and Environmental Engineering and Architecture, University of Parma, Italy
2Department of Geotechnics, Faculty of Civil Engineering, University of Zilina, Slovakia
E-mail: andrea.segalini@unipr.it
https://doi.org/10.26552/com.C.2014.4.58-62
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2. Data collection and error handling
2.1 Raw data structure and sources of uncertainty
Each node returns the three components of gravitational
acceleration, measured along the sensor axes (Fig. 1b), linearized
and normalized to g. This implies that an axis aligned (direction
and orientation) with the gravitational acceleration returns
a value equal to 1 and, vice versa, an axis perpendicular to the
gravitational acceleration returns a value equal to 0. Likewise,
the magnetometer returns the three magnetic field components
within the same coordinate system. These values are expressed in
Gauss and not normalized. Each node returns its temperature too.
The raw data are saved in a file structured as a matrix where each
row corresponds to a specific instant (date and time) recorded in
column one. The second column of the file (the first after date
and time) contains the power supply level at the reading time.
Each MUMS node generates seven column of data, reported
sequentially starting from node 1 (bottom of the chain). The
data order for each node is: 1. three components of gravitational
acceleration; 2. three components of the magnetic field; 3.
temperature. An example of the output file - limited to the first
node of the chain - is shown in Table 1.
The readings (recovered from each node) are poorly dispersed
because of the precision limits of the MEMS [2]. In addition,
a drift affects the output dataset over time, a part of which is
typical of this kind of sensors (characteristic of static applications
and not related to any external input) and the remaining part is
related to temperature variations. In order to accurately describe
and compensate these errors, it is useful to analyse the output
signal related to the first group of (3-4) nodes. Such nodes are
installed and cemented within the lower (and stable) portion of
the slope and they should not move at all. Therefore, the variation
of the output signal (for these nodes) can be related exclusively to
the intrinsic noise and drift of the sensor. Fig. 2a shows the raw
data series and the typical drift of a ‘stable’ node. By subtracting
It is possible to equip each node with different sensors in
order to measure other physical entities (i.e. pore pressure,
resistivity, etc.).
The chain is pre-assembled in laboratory and its design is
customized to the needs of each installation. During the pre-
assembly, all the nodes are manufactured, connected and tested
for a correct response; they are then molded in resin along an
aramid fibre rope and cable at known distances (usually 0.5 m).
Each node is waterproof tested up to 2 MPa. Once the
MUMS chain is complete, it is wrapped around an appropriate
coil for transport. Nodes are calibrated simultaneously by moving
the coil in several positions at a constant temperature. Calibration
values obtained for each node are stored on the data logger
memory (SD card).
The on-site installation is carried out through a simple
procedure, analogous to the one of a standard inclinometer
casing. The only difference is that the MUMS chain has
a maximum diameter of 3 cm. This feature and the flexibility
of the instrument allow installation of the chain either in small
boreholes or inside old inclinometer casing that were partially
broken by previous landslide movements.
Once the installation site is reached, the chain is unwrapped
from the coil and lowered inside the borehole. The deeper part of
the chain must be placed in a stable portion of the slope and the
lower portion (1–2 m) has to be cemented within the borehole,
through the injection of appropriate fast hardening grout. In
this way it is possible to create a stable benchmark to which the
displacements measured along the chain refer. The remaining
portion of the borehole is then filled with clean gravel having
diameter of 3 to 4 mm.
Subsequently, the chain is connected to the data acquisition
system placed at the surface near the borehole. This proprietary
system is characterized by a data logger that manages the
readings, the local storage and the remote communication of data
(using GPRS, UMTS or HDSPA technology).
Example of the output file limited to the first node of the MUMS chain Table 1
Date and Time Battery
Level (V)
Node 1
a
x
a
y
a
z
m
x
m
y
m
z
T [° Celsius]
13/12/12 2:03 13.6 0.9323 0.3082 0.1895 -0.4606 -0.0274 -0.0404 15.00
13/12/12 4:03 13.6 0.9286 0.3155 0.1954 -0.4624 -0.0283 -0.0404 15.25
13/12/12 6:03 13.6 0.9307 0.3126 0.1899 -0.4634 -0.0274 -0.0394 15.00
13/12/12 8:03 13.5 0.9310 0.3130 0.1880 -0.4624 -0.0310 -0.0413 14.75
13/12/12 10:03 13.6 0.9305 0.3148 0.1870 -0.4643 -0.0310 -0.0404 15.25
13/12/12 12:03 13.6 0.9304 0.3135 0.1898 -0.4596 -0.0274 -0.0384 15.00
13/12/12 14:03 13.6 0.9323 0.3098 0.1864 -0.4624 -0.0310 -0.0413 15.00
13/12/12 16:03 13.6 0.9311 0.3104 0.1918 -0.4624 -0.0292 -0.0394 14.50
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It is important to point out that a variation of few thousandths
in the acceleration components might induce the reconstruction
of significant displacements. Such phenomenon is partly related
to the initial inclination of the sensor, and it is explained by the
non-linearity of the trigonometric functions used to calculate
the displacement of the i-th node, S
i
. In general, neglecting the
information related to the direction of the displacements, the
module of S
i
can be calculated from the sub-vertical component
of the gravitational acceleration, a
z
, as:
sincos sincosSL
aa
a
iz
zz
11
T
=-+
--
^h
6
@
(1)
where L is the length of the chain portion referring to the
particular node and Da
z
is the variation of the a
z
value from the
first reading (zero reading). Depending on the original inclination
of the MEMS sensor, the same variation Da
z
can generate large
differences in the calculated values of S. This explains why (a) it
is necessary to process a data in order to reduce the dispersion of
the signal and why (b) the initial lying posture of the sensor could
reduce the sensitivity to the described noise.
When it is not possible to distinguish the instrumentation
drift from a slow and continuous deformation of the slope, it is
necessary to introduce the concept of instrumental resolution,
intended as the threshold below which the variation of the output
data must be ascribed to interferences and spurious effects other
than to a real displacement of the installed MUMS node.
2.2 Software filtering
As described above, the data can be downloaded when
necessary (either remotely or on site) and elaborated in order to
evaluate slope deformations.
It is possible to transform the gravitational and magnetic
field components into the displacement vector components
only after filtering the data. Data have to be filtered in order
to reduce their dispersion and to resolve the drift phenomena
described above. At first, the data dispersion can be reduced using
a moving average operator, with an amplitude window level to be
evaluated as a function of both the frequency of acquisition and
amplitude of the dispersion (noise level). This procedure would
significantly reduce the noise of the dataset. However it also
induces a loss of information. That implies the impossibility of
precisely identifying the time instant in which a “small discrete”
displacement of the node (and therefore of the slope) has taken
place. The data elaboration is carried out by a MATLAB
®
script,
developed using specific average functions (e.g., “smooth” with
the “rlowess” option, which executes a local regression of the data
using weighted linear least squares and a 1st degree polynomial
model. The function also assigns lower weight to outliers in the
regression). About the amplitude of the mobile window, two values
have been compared: 12 and 84 samples; in fact, for the examined
the measured trend from the data series we can obtain the signal
dispersion whose distribution is reported in Fig. 2b. The type
of this distribution is Gaussian, proving that the dispersion of
the data is due to electronic noise (and thus to the acquisition
system). Any “step” (intended as an abrupt change of the output
signal), detected by a single node at a well-defined point, will
univocally identify a slope movement occurred in the close
proximity of the examined node. Such circumstance will indicate
a kinematics which took place at a well-defined time.
a)
b)
Fig. 2 Measured data
a) Raw data series and extracted data trend of the z axis of one node;
b) Distribution of the signal dispersion around the average value
The analysis of the data trend of the first nodes is used to
quantify and correct the drift error affecting the whole chain.
This method is based (a) on the high similarity between MEMS
belonging to the same lot – therefore characterized by similar
behaviour – and (b) on the assumption that nodes in a chain are
exposed to the same environmental conditions. Each node will
present a drift not dissimilar to the representative one (estimated
as explained above).
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one by one). The analysis may be also focused on appropriately
chosen time intervals (e.g. seasonal period).
A MUMS device was installed in late spring of 2013 in
Boschetto (Parma river valley, Parma, Italy). On April 2013,
Boschetto village was affected by a landslide (Fig. 3) which
involved a thick debris accumulation already defined as dormant
landslide deposits of a complex landslide [7]. The debris is
composed by weathered arenaceous and marly rock particles in
a clay and silty matrix. The deep and stable portion of the slope
is characterized by the rock formation named Pink Tizzano marl
– Castelmozzano member [8] and [9].
Fig. 3 Boschetto landslide’s crown area. The provincial road that
crossed the slope just in front of the depicted house was completely
destroyed by the landslide, causing the seclusion of the inhabited
upper Parma river valley
The borehole instrumented with MUMS is located in the
upper portion of the colluvium deposit, about 20 m above
the landslide crown. That MUMS device is a 20.5 m long
inclinometer chain equipped with a piezometer positioned 1 m
above the presumed sliding surface.
Fig. 4 Combined representation of the recorded and elaborated data.
The plotted contours are indicating the amount of daily displacement
recorded at each depth (left axis) while the dotted rectangles
are indicating one week time span. The water table level
variation is related to the left axis as well while the daily
cumulated rainfall is related to the right axis.
case, the data were acquired every two hours and, therefore, the
amplitude of the mobile window corresponds to one day or one
week, respectively. In the first case, the correlation between the
filtered and the original signal is higher, while in the second, data
stability is higher and the determination of node displacement
becomes more reliable. In order to correct the instrumental drift it
is possible to execute the “detrend” of data, as above mentioned,
remembering that such operation could prevent recognizing
a prolonged, and constant and slow, and small slope movement.
To confirm the obtained results, it is possible to conduct
a second type of analysis. The calculation of nodes displacements
at regular time intervals (i.e. daily displacements) makes it possible
to build an output statistics and to determine the movement
threshold beyond which a displacement should be considered real
(generated by a slope movement) and it is not to be considered
dependent on the instrumental errors. To do this the dimension
of the sample has to be evaluated and a corresponding value
of the Student’s t must be chosen, accordingly to a predefined
reliability level. The displacements will be considered reliable
only when they are higher than a certain number of standard
deviations added to the local mean value. The average value of
the displacements should, once again, be subtracted from the
determined reliable displacement, in order to account for the
instrumental drift.
3. An interesting application of MUMS
instrumentation
Various test installations of MUMS instruments have been
placed in natural slopes affected by instability phenomena [3]
and [4]. Nowadays, the area of future installation is highly
demanded for monitoring of transport constructions in difficult
geological conditions, e.g. embankments of railway line for high-
speed on soft soils [5]. Accurate information from innovative
monitoring methods allows calibration of the results of new
numerical methods [6] on real transport structures. Underground
displacements measured by MUMS show that the great amount
of information – collected by an automated and continuous
monitoring – gives an evaluation of the ongoing physical process
which is more detailed than the one obtained by traditional
inclinometers. Those data may be elaborated and analysed
for various purposes in order to highlight different relations
between the physical entities involved. The main objective of data
analysis concerning slope instability is to relate the displacement
values with other natural phenomena that can be considered as
triggering events (rainfalls and related increase of pore pressure,
seismic loading etc.).
The detailed and frequent recording of data allows to
determine how the variation in underground displacement is
related to other physical entities (whose effects are considered
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4. Conclusion
The recent evolution of electronic micro-sensors provides
invaluable support for the development of innovative monitoring
systems which are accurate and cost-efficient. The electronic
components provide digital outputs that require appropriate
analysis and treatment in order to obtain reliable, repeatable and
accurate physical responses.
Once the appropriate data analysis is carried out, the
obtained results lead to a completely new interpretation of
the investigated physical phenomena, offering the basis for an
independent validation of previous theoretical assumptions and
for the introduction of new and original interpretations.
An application of a novel instrumentation for landslide
monitoring (based on MEMS and developed around a completely
new concept of diffuse and continuous monitoring) is presented
in this paper. The basic concepts regarding the sensor application
and the analysis of some data are illustrated. Such results lead to
an original interpretation of the relation between other natural
phenomena (like rainfalls and related increase of pore pressure)
and landslide behaviour.
Underground displacements data and ground-water level
have been recorded every two hours and cumulated at daily
intervals. Cumulated daily rainfalls have been obtained from the
Regione Emilia-Romagna website [10]. Data collected from June
2013 to February 2014 are shown in Fig. 4. Daily underground
deformations are represented in a contour plot where different
(and darker) colours correspond to different (and greater) values
of displacement. It is possible to recognize a main shear band
located about 10 m under the surface, as shown in Fig. 4. The
displacements occurring in the recognized shear band are related
to the variations of the ground-water level (i.e. pore pressure).
The ground-water level trend shows three different steps. Those
steps correspond to a rapid increase of the pore pressure and
they are followed by a subsequent increase in the displacement
values. Such landslide movements take place approximately one
week later than the corresponding variation in underground-water
level. The dotted rectangles highlight the time interval occurring
between ground-water level steps and increasing velocity of
underground displacements.
References
[1] SEGALINI, A, CARINI, C.: Underground Landslide Displacement Monitoring: A New MMES Based Device, Landslide Science
and Practice, vol. 2, 2013, Early Warning, Instrumentation and Monitoring - Margottini, Canuti & Sassa Eds. : Springer London,
pp. 87-93 - doi: 10.1007/978-3-642-31445-2
[2] SEGALINI, A., CHIAPPONI, L., CARINI, C.: Evaluation of a Novel Inclinometer Device Based on MMES Technology through
Comparison with Traditional Inclinometers in Landslide Applications. Geophysical Research Abstracts, vol. 15, EGU2013-1993,
2013. EGU General Assembly 2013
[3] SEGALINI, A., CHIAPPONI, L., PASTARINI, B., CARINI, C.: Automated Inclinometer Monitoring Based on MEMS Technology:
Applications and Verifications - accepted for publication in the proc. of the III World Landslide Forum 3, June, 2014, Beijing
[4] SEGALINI, A., CHIAPPONI, L., PASTARINI, B.: Application of Modular Underground Monitoring System (MUMS) to
Landslides Monitoring: Evaluation and New Insights - accepted for publication in the proc. of the XII IAEG Congress, September
2014, Turin
[5] VLCEK, J. et al.: Analysis of Earth Pressure at Retaining Walls Reinforced with Geosynthetics, Proc. of SGEM 2014, ISSN 1314-2704,
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[6] MUZIK, J., KOVARIK, K., SITANYIOVA, D.: Meshless Analysis of an Embankment Using Local Galerkin Radial Point
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ISSN:1335-4205
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[8] CERRINA, FERONI, A., FONTANESI, G., MARTINELLI, P., OTTRIA, G.: Elements of Stratigraphic Correlation between
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Northern Italian Appennine (in Italian). Atti Tic. Sc. della Terra, vol. Sp., 1: 117-122, 1994, Pavia
[9] IACCARINO, S., FOLLINI, M. P.: Calcareous Nanoplankton of the Cretaceous M. Caio Flysch Formation and of the Tizzano
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sim/?osservazioni_e_dati/dexter.
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The groundwater flow problems are often solved using numerical models. The numerical models based of known fundamental solution, known as Trefftz methods, represents one of the efficient approaches used to solve potential flow phenomena described by the Laplace differential equation. The Trefftz-like methods, such as the boundary element method (BEM), represent a very efficient approach to solving groundwater flow problems. However, the implementation of BEM is cumbersome because of the fundamental solution singularity, and also there is a very limited number of software suites using BEM. The localized boundary knot method (LBKM) eliminates the drawback of boundary meshing and evaluation of singularity of fundamental solution as in BEM. The LBKM uses the known general solution to avoid the singularity evaluation, and the dual reciprocity approach to evaluate the true solution residual. This paper proposes a numerical model that implements the localized boundary knot method (LBKM) for 2D groundwater-free surface flow simulation.
Article
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This short paper illustrates some theoretical aspects concerning the accuracy and reliability of continuous measurements of underground slope displacements. The Modular Underground Monitoring System (further referred as MUMS), is an automated monitoring system which collects and remotely sends data, it does not require the presence of a field operator and it was designed to have a lifetime longer than that of traditional inclinometers. We present the results of some early generation MUMS to highlight different problems in the analysis of collected data. Finally, the results obtained by a new installation are shown to highlight the achieved progresses.
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Full-text available
The paper deals with use of the meshless method for slope stability analysis. There are many formulations of the meshless methods. The article presents the Local Galerkin Radial Point Interpolation method (LGRPIM) - local weak formulation of the equilibrium equations. The main difference between meshless methods and the conventional finite element method (FEM) is that meshless shape functions are constructed using randomly scattered set of points without any relation between points. The shape function construction is the crucial part of the meshless numerical analysis in the construction of shape functions. The article presents the radial point interpolation method (RPIM) for the shape functions construction. The numerical example of the slope stability was calculated using meshless computer code and compared with FEM results.
Article
This paper describes the analysis of the effectiveness and reliability of a new type of inclinometric chain, which is still under development by the authors, and is intended to be applied in the underground slope monitoring field. In the first part, the paper describes the new instrumentation which should allow for a deeper and detailed understanding of the type, location and origin of slope movements that should, in turn, help in understanding the triggering causes and the evolution mechanisms of landslides, and provide an innovative and substantial contribution to their stability analysis and control. The second portion of the paper is dedicated to a comparison between the classic instruments and the new MUMS device, demonstrating the advantages of measurement automation and economy in the use of the proposed device, which could also be equipped with other electronic instruments that would allow the measurement of other interesting physical quantities (such as pore pressure, temperature, stresses, etc.) together with displacement components.
Conference Paper
Recent design methods of reinforced retaining walls are based on several approaches at the earth pressure determination. Newer methods are developed to bring the design model closer to real conditions. The monitoring of the structures reinforced with the geosynthetics but shows some anomalies at wall displacements and reinforcement loads. A series of real structure monitoring and numerical modelling was involved to verify the recent design methods. This paper represents the results of these analyses aimed at the earth pressure due to the backfill of the wall and at the stress distributions along the reinforcements.
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The paper describes the application of the Modular Underground Monitoring System (further referred as MUMS) (Segalini et al. 2013) in two active landslides located in the Northern Italian Apennines. In particular, the aim of the paper is to demonstrate the efficiency and accuracy of the system and to examine the advantages of an automated semi-continuous monitoring for the comprehension of the mechanical behavior of landslides, the definition of their triggering factors and the correct evaluation of their short term velocity. The mechanical behavior of slow moving landslides is generally evaluated on the basis of traditional surveys which are carried out at long time interval and therefore are lacking of detailed information about the links between triggering causes and mechanical effects. The main advantage of an automated monitoring system resides in the observation frequency and in the simultaneous recording of several physical entities such as deformation, precipitation, pore pressure and so on. This large amount of data can be used for the numerical evaluation of the landslide behavior and for the definition of the most significant triggering cause(s). The obtained knowledge is of fundamental importance when there is a need of establishing an hazard threshold for the particular landslide and, when this assumption is made, the automated monitoring system can immediately become a real time control and alarm triggering device. For this purpose, it is important that the lifespan of the instrumentation is as long as possible, in relation with the expected displacements, in order to maintain the real time monitoring effective and economical as well as to build a reliable database for statistical analysis. .
Article
The paper illustrates the efficiency of a novel inclinometer device system - based on MEMS technology - by comparing results obtained from a couple of installed prototypes and those derived from classic inclinometers. The new device, called MUMS (Modular Underground Monitoring System) is intended to be applied for natural and artificial slope deformation monitoring and landslides dynamics control, assessment and forecasting. In its initial part, the paper briefly describes the new instrumentation which should allow for a better understanding of the type, location and origin of unstable slope movement. The MUMS instrumentation was born from the idea of replacing the standard measurement procedure by locating nodes at known distances from each other along a connecting cable placed within a vertical borehole. Each node is able to measure its local orientation from the vertical (gravitational acceleration) by means of a micromechanical 3D digital linear acceleration sensor (MEMS). This will allow us to determine the direction cosines of the borehole axis in each node and, by means of linear geometry and trigonometry, calculate its 3D shape and deformation along the whole borehole. The basic hypotheses to be considered for this procedure are: a) the lower node must be located in a stable portion of the soil/rock and must be accurately cemented to it and b) the distance between two subsequent nodes along the pipe must not vary. This configuration - with 3D inclinometers only - would require that all of the nodes must be originally aligned along a single diametric plane of the cable. This mechanical condition could be achieved using a connecting pipe which would, however, generate installation problems and a possible incorrect assembly of the nodes; this inconvenience could degrade the evaluation of the displacement heading and therefore compromise their final measurement integration from the bottom up along the borehole. To avoid it, a 3D digital MEMS magnetic sensor was added to each 3D digital linear acceleration sensor, enabling us to determine the heading (azimuth) of each node related to the magnetic North. This added MEMS element eliminates the uncertainties and any errors due to spiraling or to system assembly imprecisions. Following, the paper deals with a series of significant errors like biases, drift and noises that are affecting the final output of MEMS sensor and illustrates how to achieve valuable and reliable outputs which allow the use of these sensors in the landslide monitoring field. Finally, a couple of example application are presented where prototypes of this system are installed on well documented and traditionally monitored slow moving active landslides. In these examples the MUMS results are compared with those obtained by traditional systems evidencing the new system potentials and effectiveness in terms of sensitivity, precision, reliability and automation.
Automated Inclinometer Monitoring Based on MEMS Technology: Applications and Verifications -accepted for publication in the proc
  • A Segalini
  • L Chiapponi
  • B Pastarini
  • C Carini
SEGALINI, A., CHIAPPONI, L., PASTARINI, B., CARINI, C.: Automated Inclinometer Monitoring Based on MEMS Technology: Applications and Verifications -accepted for publication in the proc. of the III World Landslide Forum 3, June, 2014, Beijing
Elements of Stratigraphic Correlation between the Bersatico Member (Tizzano Val Parma Pink Marl Formation) and the Poviago Member (Val Luretta Formation) within the Northern Italian Appennine (in Italian)
  • Feroni Cerrina
  • A Fontanesi
  • G Martinelli
  • P Ottria
CERRINA, FERONI, A., FONTANESI, G., MARTINELLI, P., OTTRIA, G.: Elements of Stratigraphic Correlation between the Bersatico Member (Tizzano Val Parma Pink Marl Formation) and the Poviago Member (Val Luretta Formation) within the Northern Italian Appennine (in Italian). Atti Tic. Sc. della Terra, vol. Sp., 1: 117-122, 1994, Pavia
Calcareous Nanoplankton of the Cretaceous M. Caio Flysch Formation and of the Tizzano Val Parma Paleocene "Pink Marl" Formation (Northern Italian Appenine) -in Italian
  • S Iaccarino
  • M P Follini
IACCARINO, S., FOLLINI, M. P.: Calcareous Nanoplankton of the Cretaceous M. Caio Flysch Formation and of the Tizzano Val Parma Paleocene "Pink Marl" Formation (Northern Italian Appenine) -in Italian, Italian J. of Paleontology, 76 (4): 579-618, Milano, 1970.