GeolCarp_Vol62_No5_477

www.geologicacarpathica.sk

GEOLOGICA CARPATHICA

, OCTOBER 2011, 62, 5, 477—485 doi: 10.2478/v10096-011-0034-7

Introduction

Context and literature review

In geotechnical engineering, it is not uncommon to encounter
problems  that  are  very  complex  and  not  well  understood.  In
most cases, the mathematical models used to solve such prob-
lems attempt to make up for our lack of physical understand-
ing by either simplifying the problem or incorporating several
assumptions.  Such  models  may  also  be  designed  to  produce
solutions with a specific structure, chosen in advance, which
may be suboptimal for the case at hand. Consequently, many
mathematical  models  fail  to  simulate  the  complex  behaviour
of  geotechnical  engineering  problems  (Rizzo  &  Dougherty
1994). Analyses based on the finite element method provide a
better  assessment  of  geotechnical  engineering  problems,  but
these are often costly and time-consuming.

These limitations of mathematical and finite element mod-

els  make  the  problem  of  landslide  prediction  exceedingly
difficult. Constitutive programs to determine the stability of
slopes  require  many  free  parameters  and  a  large  amount  of
input data to calibrate. Therefore, practicing engineers prefer
to  employ  simplified  methods  to  assess  subsurface  layering
and  landslide  risk.  Such  procedures  are  very  useful  in  the
preliminary  design  stages  of  a  new  project.  If  the  landslide
risk is high, then finite element analysis can be carried out to
determine the stability of the slope with accuracy suitable to
the  subsequent  design  of  structures.  Artificial  Neural  Net-
works (ANNs) provide a very different approach to geotech-
nical prediction, with many advantages.

ANNs are model-independent, nonlinear systems capable

of learning physical patterns from a set of input—output data
pairs (Gardner & Dorling 1998). Since their results are based
entirely on the data provided, ANNs neither simplify the
physical nature of the problem nor incorporate any assump-

Predicting subsurface soil layering and landslide risk

with Artificial Neural Networks: a case study from Iran

FARZAD FARROKHZAD

, AMIN BARARI

, LARS B. IBSEN

and ASSKAR J. CHOOBBASTI

Department of Civil Engineering, Babol University of Technology, Babol, Mazandaran, Iran

Department of Civil Engineering, Aalborg University, Sohnga°rdsholmsvej 57, 9000 Aalborg, Denmark; *ab@civil.aau.dk,

*amin78404@yahoo.com

(Manuscript received November 9, 2010; accepted in revised form March 17, 2011)

Abstract: This paper is concerned principally with the application of Artificial Neural Networks (ANN) in geotechnical
engineering. In particular the application of ANN is discussed in more detail for subsurface soil layering and landslide
analysis. Two ANN models are trained to predict subsurface soil layering and landslide risk using data collected from a
study area in northern Iran. Given the three-dimensional coordinates of soil layers present in thirty boreholes as training
data, our first ANN successfully predicted the depth and type of subsurface soil layers at new locations in the region. The
agreement between the ANN outputs and actual data is over 90 % for all test cases. The second ANN was designed to
recognize the probability of landslide occurrence at 200 sampling points which were not used in training. The neural
network outputs are very close (over 92 %) to risk values calculated by the finite element method or by Bishop’s method.

Key words: Modelling, subsurface soil layering, landslide, Artificial Neural Network.

tions. At worst, the data provided to train the network are bi-
ased. However, ANNs can always be updated to obtain bet-
ter results by presenting additional training examples as new
data become available (Ghaboussi & Sidarta 1998).

Many authors have studied applications of ANNs to geo-

technical engineering. A frequent theme is the prediction of
load capacity based on pile driving data. Ellis et al. (1992) de-
veloped an ANN model for sands based on grain size distribu-
tion and stress history. Chan et al. (1995) developed a neural
network as an alternative to pile driving formulae. Lee & Lee
(1996) utilized neural networks to predict the ultimate bearing
capacity of piles. Teh et al. (1997) used a neural network to es-
timate the static capacity of precast, reinforced concrete piles
with a square section based on dynamic stress-wave data.
Penumadu & Jean-Lou (1997) used neural networks to repre-
sent the behaviour of sand and clay soils.

It should be mentioned that over the last ten years, the au-

thors have undertaken a comprehensive research program us-
ing ANN (Choobbasti et al. 2009; Farrokhzad et al. 2011a,b).
It should also be noted that every ANN is highly specialized,
and entirely dependent on the input data of the particular
project. All aspects of training must be carefully controlled,
and we perform many robustness tests to show that the predic-
tions of the networks are independent of the dataset used.

Research goals

We will now discuss the central aspects of our research in

more detail, as a means of showcasing the broad applicability
of ANNs to geotechnical engineering problems. To this end, a
short introduction to subsurface soil layering is in order.

It is known that the engineering properties of soil vary

from point to point due to the complex and imprecise physi-
cal processes associated with its formation. In contrast, civil
engineering materials such as steel and concrete exhibit far

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GEOLOGICA CARPATHICA, 2011, 62, 5, 477—485

greater homogeneity and isotropy. In order to cope with the
complexity  of  geotechnical  behaviour  and  the  spatial  vari-
ability  of  soil,  traditional  engineering  design  models  have
been justifiably simplified (Jaksa 1995). Soil geology plays
an important role in core material selection (for example, the
construction  of  rock  fill  dams)  and  in  geotechnical  evalua-
tions preceding the construction of major structures.

Layering is caused by a variety of pedological and geological

processes,  and  has  important  consequences  for  the  interpreta-
tion  of  soils  and  landscapes.  Collecting  adequate  data  on  soil
layering (soil classification, thickness of layers) can be difficult
and  expensive,  sometimes  requiring  a  series  of  experiments.
Therefore,  a  method  of  predicting  the  structure  of  subsurface
soil layers from existing data would lead to significant cost re-
ductions in soil geology and improved operational planning.

Accordingly, our first goal in this paper is to develop an

ANN capable of predicting the type and depth of subsurface
soil layers from easily collected geological data. Specifically,
we will use the three-dimensional positions of layers observed
in previously collected boreholes to train an ANN, then test
the network’s capacity to predict soil layers under an arbitrary
location on the surface. The training and testing data were col-
lected from the Mazandaran province of northern Iran.

Our second goal is to train an ANN to correctly predict

landslide risk in the region based on more elaborate geo-
physical data. We will compare the outputs of the network to
landslide risk assessments made using the finite element
method and Bishop’s method.

The remainder of this paper is organized as follows. In

Section 2, we point out some relevant theoretical aspects of
landslides and ANNs. Section 3 describes in detail our meth-
od of designing and training an ANN for soil layer predic-
tion and the performance of the network. Section 4 describes
our ANN for landslide risk assessment, while it is followed
by Section 5 as conclusions.

Theory

Landslides

“Landslides” are mass motions of soil resulting from par-

tially drained shear failures of the surface and subsurface
layers. Relatively small disturbances may trigger landslides,
especially in areas where previous slides have reduced the
residual shear strength of soil masses along their correspond-
ing slip planes (Choobbasti et al. 2008).

The deformation of soil masses generally begins at or

close to the toe of the slope, because this is where the shear
strength of the soil is first exceeded. The deformation then
progresses retrogressively along the shear zones. Movements
of the unstable soil masses take place at points where a large
local strain leads to structural failure. The characteristics of
landslides depend on the geometry of the unstable soil mass,
the pore pressure distribution (which in turn usually depends
on the location of the phreatic surface), the pattern of defor-
mation, and the speed of the movements.

Two common underlying factors are the mechanical prop-

erties of the soil skeleton and the fraction of saturation by

groundwater (Terzaghi & Peck 1967). Applying Terzaghi’s
concept of the effective stress in saturated soil, the stress-
strain behaviours of drained soil and dry soil can be assumed
to be identical. Terzaghi’s approach is detailed below.

The total stress

can be separated into an effective stress

tensor

’

and the pore pressure u as follows:

’

+u.

(i, j=1, 2, 3) (1)

where

is the Kronecker delta. The deformation of the soil

skeleton only depends on changes in the effective stresses

’

Shear failures occur along the shear planes forming the

boundaries between moving masses of water-saturated dense
soils or rocks. These failures may be caused by the deforma-
tion of partially undrained masses (Bekele et al. 2010). The
word “landslide” is employed by the authors to describe
such failures. The term implies more than deformation of the
masses; one or more blocks of practically solid soil or rock
must actually move (Yesilnacar & Topal 2005). A shear fail-
ure is generally assumed to occur when the average shear
stress along the sliding (or slip) surface is equal to the shear
strength of the soil or rock. The shear strengths can be evalu-
ated by field or laboratory tests (Terzaghi & Peck 1967).

Given that landslides are a consequence of uneven draining

in saturated soils and rocks, they lead to progressive failure of
the structure and erode the residual strength of the layers.
Thus, existing landslides may continue to slip at an average
shear stress considerably less than the drained peak strength of
the soil or rock. The drained peak strength can be measured by
conventional tests (such as triaxial or direct shear tests of the
completely drained material – Yilmaz 2009a).

The first sign of an imminent landslide is the appearance

of surface cracks in the upper part of the slope. These cracks
are perpendicular to the direction of movement. They may
gradually fill with water, reducing the effective normal stress
in the soil along the shear planes. This process may further
degrade the soil’s resistance. The speed of a landslide during
the primary failure is controlled mainly by the nature of the
materials involved and the overall shape (including steep-
ness) of the failure surface.

With respect to the materials involved, it is known that soil

with  more  loosely  packed  particles  has  a  larger  difference
between the drained and undrained strengths. This is true for
both  sands  and  clays.  When  saturated,  these  materials  are
also  known  as  “quicksand”  and  “quick  clay”;  even  a  small
disturbance can cause their structures to collapse. Both types
of  soils  are  characterized  by  liquefaction  failure  (Lee  et  al.
2004).

In this paper, both subsurface layering and landslide anal-

ysis are assessed using artificial neural networks. Before
continuing, we will therefore briefly introduce this form of
modelling.

Artificial Neural Networks

Neural networks are intuitively appealing structures based

on  a  crude,  low-level  model  of  biological  neural  systems.
Even  a  simple  network  is  capable  of  modelling  extremely
complex  nonlinear  functions.  They  also  excel  at  bypassing
the  “dimensionality  problem”  that  bedevils  other  forms  of

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non-linear modelling, which often require a very large num-
ber of variables.

Neural networks learn by example. The researcher gathers

representative data, then invokes a training algorithm to find
a network structure that reproduces the data. A typical feed-
forward network is shown in Fig. 1. The nodes are arranged
in layers, with any given node transmitting its information to
all nodes in the layer below. The input layer serves only to
introduce the values of the input variables. The output layer
is used to classify the data.

When the network is presented with data, the input vari-

able  values  are  placed  in  the  input  nodes.  These  values  are
used to generate signals in the first hidden layer, and then the
hidden values are used to generate the output layer. The net-
work calculates the signal for a hidden node or output node
by  performing  a  weighted  sum  of  the  outputs  from  units  in
the preceding layer and subtracting a threshold. This value is
then  passed  through  an  activation  function  to  produce  the
outputs of the node. The output layer signals are therefore a
result of the entire network (Farrokhzad et al. 2011b).

Predicting subsurface soil layering with ANNs

Data collection

Babol (Fig. 2), a city in the Mazandaran province of north-

ern Iran, is our study area for subsurface soil layering. The
city is located approximately 20 kilometers south of the Cas-
pian Sea, on the west bank of the Babolrood River. The re-
gion receives abundant annual rainfall. Babolrood has two
types of river terraces, denoted H

and H

. H

terraces have a

low level surface level, with heights of 1 to 2.5 m and widths
of  0  to  150 m.  In  some  sections  of  the  river,  these  terraces
mark the boundary of the active (yearly) flood plain. In other
sections,  they  represent  an  alternative  flood  plain  (diacritic
of  active-alternative  flood  plain  from  20-year  flood  plain).

Fig. 1. Structure of Artificial Neural Network.

Fig. 2. Map of areas under study.

terraces consist of fine-grained and unconsolidated allu-

vial materials. H

terraces are referred to as river terraces.

They have higher surface levels, 4 to 6 m, and support com-
pact vegetation. They consist of Aeolian deposits (loess).

This research makes use of data from 40 borehole logs col-

lected in the study area by a different institute of geotechni-
cal  engineering  and  seismology,  for  different  research
purposes. The boreholes cover a region of about 7.8 square
kilometers,  separated  into  five  zones  by  imaginary  lines
(Fig. 3).  Thirty  of  the  logs,  with  depths  of  10 m  to  30 m,
were used to train and test our ANN (Fig. 4).

The properties of the soil layers were determined by field ex-

amination  and  by  laboratory  index  testing  of  benchmark  soils
following  standard  procedures.  During  the  survey,  we  bored
many shallow holes, examined and classified the samples, and
created a soil map of the region. The samples were tested in the
laboratory  to  determine  their  grain  size  distribution,  plasticity,
and  compaction  characteristics  (Allen  &  Tadesse  2003).  Ta-
ble 1 shows the estimated properties of the soil types, including
grain size distributions and Atterberg limits, engineering classi-
fications, and the physical properties of the major layers.

The soil layer ANN

To train the Artificial Neural Network, we need a set of

known input and output pairs (Basheer 1998). The available
input—output pairs are usually divided into two sets. The
learning or training set is used to determine the connection
weights (w

)

(Agrawal et al. 1997) in the network. The test-

ing set is used to measure the performance of the neural net-
work after training. In this study, 70 % of the data were used
for training and 30 % were used for testing. We tried other
ratios, but this one yielded the best results.

(

)

(

)

f y k

w y

∑

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Table 1: Samples of data collected from western part of Babol.

Table 2: Results of research in order to Learning/training algorithm
selection.

Table 3: Results of research in order to select the number of neu-
rons in the hidden layer.

Fig. 5. Network architecture for predicton of subsur-
face soil layering in Babol.

memory requirement than most algorithms, and
usually reaches an acceptable level of error very
quickly. However, it can be very slow to con-
verge to the error minimum.

We tested networks with 3, 5, 7, 8, 9, 10, 11,

12 neurons in the hidden layer. Table 3 shows
the results of this comparison. The network with
8 hidden neurons produced the smallest RMS er-
ror in this case study. This network’s architec-
ture is shown in Fig. 5.

The training phase is divided into epochs. In

each epoch, every input—output pair in the train-
ing set is fed through the network once and used
to  adjust  the  network  weights  and  thresholds.
After each epoch the state of the network and the
performance of the network on the testing set are
recorded, so that if over-learning occurs the best
network  discovered  during  training  can  be  re-
trieved. The number of epochs required to opti-
mize the network in this research varied between
100 and 500. It is good practice to “shuffle” the
training set in each epoch, presenting the input—
output  pairs  to  the  network  in  a  random  se-
quence.  (If  the  sequence  never  changed,  the
network might perform better for pairs at the end
of the epoch.)

In addition to the primary choices described

above, back-propagation models can be tuned us-
ing two parameters. The learning rate controls the
size  of  the  weight  changes  made  by  the  training
algorithm. A larger learning rate may lead to fast-
er convergence, provided that the error surface is
not  too  noisy  (Penumadu  &  Zhao  1999).  In  the
present work, we select a learning rate of 0.5. The
momentum  parameter  causes  the  back-propaga-
tion  algorithm  to  “pick  up  speed”  if  consecutive
training pairs push the weights in the same direc-
tion. This property helps the network reach a glo-
bal  optimum  by  preventing  it  from  getting
trapped in local minima of the parameter space.

Supervised

learning/

training

algorithms

Back

propagation

Conjugate

gradient

descent

Levenberg–

Marquardt

Quick

propagation

Delta-

bar-

Delta

RMS (%)

8 11

13.3

10.7

11.5

Number of neurons in the

hidden layer

3 5 7 8 9 10 12

RMS (%)

14 11.7 10.9 7.5 9.8 12.1 13.3

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GEOLOGICA CARPATHICA, 2011, 62, 5, 477—485

Results of the soil layer ANN

Figure 6 presents the ANN’s predictions for the thickness

of  subsurface  soil  layers  in  three  boreholes,  and  the  actual
layers obtained from digging the boreholes. As mentioned in
the  previous  section,  soils  from  the  Babol  study  area  were
classified  into  four  types:  gravel,  sand,  silt  and  clay.  In  the
case  study,  the  input  variables  were  simply  the  subsurface
coordinates (x, y, z). For a given value of x and y, the net-
work  yields  different  soil  types  for  different  values  of  z
(depth).  The  neural  network  results  are  very  similar  to  the
real data. In fact, the ANN model properly predicts the types
of  soil  present  in  all  three  boreholes.  The  predicted  thick-

Fig. 6. a, b, c – Comparison between predicted results of ANN and soil profiles obtained from test
boreholes.

Fig. 7. Errors in the ANN for prediction of subsurface soil layers in test samples obtained from test boreholes.

nesses of the three layers are 1280 cm, 330 cm and 150 cm.
The corresponding actual thicknesses are 1300 cm, 300 cm
and 160 cm.

Figure 7 plots the ratio of the predicted values to the actual

values based on soil investigation for all samples in the test-
ing set. If the predicted value and the true value are similar in
all cases, then the points should all lie near the line y = 1. The
average correlation of the ANN predictions with the true
data in all cases is over 90 %. Thus, it can be concluded that
the ANN method can accurately predict the depths of subsoil
layers. This ANN is a viable prediction tool that can assist
geotechnical engineers in making accurate and realistic pre-
dictions of the subsoil structure.

An assessment of

landslide risk in Shirgah

Shirgah (Fig. 2) is located in

the  north  of  Iran,  in  Mazanda-
ran province. The annual mean
temperature  of  the  terrain  is
12.5 °C,  and  the  annual  mean
precipitation  is  about  800 mm.
The climate (from the Dommar-
tan  method)  is  humid.  From  a
geological  point  of  view,  most
of the units in this region are re-
lated to the Cenozoic Era. Marl,
shale  and  silty  stone  are  there-
fore prevalent, and the region is
susceptible to landslides.

Before assessing risk on this

slope, we dug several boreholes
to  obtain  essential  information
such  as  details  of  the  strata,
moisture content, and the stand-
ing  water  level.  Piezometer
tubes  were  installed  in  the
ground  to  measure  changes  in
water  level  over  time.  These
field investigations also includ-
ed  in  situ  and  laboratory  tests,
photographs, a study of geolog-
ical maps and memoirs to indi-
cate  probable  soil  conditions,

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visiting and observing the slope, examining records of previ-
ous instabilities, and plotting the topography (Fig. 8).

As with the case study described in Section 3, we tested sev-

eral network structures, training parameters, and other criteria to
find the ANN model that was most suitable for predicting land-
slide hazard in the region. After a number of trials, the best-per-
forming ANN had four layers: an input layer of 8 neurons, an
output layer of 2 neurons, and two hidden layers. All other as-
pects of the ANN model are identical to that used in Section 3.

In selecting the input variables (Fig. 9a,b) we hope to recog-

nize the conditions which caused the slope to become unstable

Fig. 8. Topography of study area and unstable zone.

Fig. 9. a – Network architecture, b – Sample’s coordinate and Stable and Unstable sample description.

and the processes which triggered that movement. If the net-
work can achieve an accurate diagnosis, it will be possible to
understand the landslide mechanisms and propose effective
remedial measures. In the proposed model, the input parame-
ters are the coefficient of cohesion (C), the angle of the slope
( ), the angle of internal friction ( , distance from the slope
edge (X), the unit weight ( ), the slope elevation (H), the ef-
fective stress (

’), and the length of the slope (L). The output

variable is the slope stability.

In this research, we assume that the horizontal acceleration

of an earthquake is constant. During an earthquake, the hori-

zontal  acceleration  on  the
blocks  may  cause  instability.
However,  this  acceleration
also  reduces  the  normal
stresses  on  the  contact  plane
and  hence  the  friction  to
shear  along  the  plane.  The
contribution  of  the  cohesion
coefficient  to  shear  strength
may be real (due to cementa-
tion)  or  apparent  (due  to  as-
perities  on  the  discontinuity
plane). During an earthquake
cementation  may  be  broken,
and asperities may be broken
or overridden leading to non-
fitting  roughness  patterns.  In
all  cases  cohesion  and  fric-
tion are permanently reduced.
Hence,  an  earthquake  not
only  adds  unfavourable  forc-
es  to  a  slope  but  may  also
permanently reduce the shear
strength  along  discontinuity
planes (Yilmaz 2009b).

The ANN models devel-

oped  in  this  research  were
used  to predict  the  slope  sta-
bility  for  7  sections  (labeled

) in our study area

(Fig. 8). Approximately 1000
input—output  pairs,  including
inputs and outputs, were col-
lected  for  the  prediction  of
landslide  risk.  Among  these
data,  80 %  were  used  for
training  and  20 %  were  used
to validate the ANN.

The data cover a wide

spectrum  of  soil  Fish  pa-
rameters. In order to test the
performance  of  the  ANN,
21  random  cases  were  se-
lected  from  the  testing  set
for  detailed  predictions  us-
ing the finite element meth-
od  or  Bishop’s  method  (see
Table 4).

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GEOLOGICA CARPATHICA, 2011, 62, 5, 477—485

Figures 10 and 11 show the accuracy of the testing set

(200 sampling points). In all cases the accuracy of the ANN
model is over 92 %.

Conclusion

In this investigation, we developed and trained two ANN

models to demonstrate the suitability of the method for prob-
lems in geotechnical engineering. The first ANN predicts
subsurface layering in the Babol region of Iran, and the sec-
ond assesses landslide risk based on the physical characteris-

Table 4: Selected case studies showing the actual modes of failure and prediction of the ANN.

Fig. 11. Prediction of FOS versus true value.

Fig. 10. Accuracy of ANN in prediction of slope stability.

Sample

(kPa)

(m)

(°)

(m)

(

)

(

)

(m)

Stability

(Actual

condition)

FOS

(Actual

condition)

Stability

(Prediction

of ANN)

FOS

(Prediction

of ANN)

-1

21.2

15.96

instable

0.68

instable

0.72

-2

21.2

39.91

instable

0.73

instable

0.79

-3

21.2

79.82

instable

0.80

instable

0.82

-1

21.2

15.96

instable

0.77

instable

0.83

-2

21.2

39.91

instable

0.68

instable

0.69

-3

21.2

79.82

instable

0.71

instable

0.77

-1

6.62

instable

0.91

instable

0.86

-2

16.57

stable

1.03

instable

1.8

-3

33.14

stable

1.10

instable

1.09

-1

21.2

9.21

instable

0.91

instable

0.97

-2

21.2

23.02

stable

1.10

instable

1.02

-3

21.2

46.05

stable

1.16

instable

1.05

-1

8.24

instable

0.87

instable

0.80

-2

20.60

instable

0.89

instable

0.92

-3

41.21

instable

1.04

instable

1.03

-1

14.82

instable

0.76

instable

0.85

-3

37.05

instable

0.81

instable

0.84

-3

74.10

instable

0.82

instable

0.84

-1

21.2

17.81

instable

0.72

instable

0.68

-2

21.2

44.53

instable

0.76

instable

0.71

-3

21.2

89.06

instable

0.75

instable

0.76

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tics of the slope and soil. Both neural networks provide very
accurate results. The first network was tested on hand-col-
lected borehole data, and the second network was compared
to two classical mathematical models of landslide risk. The
trained neural networks developed in this study are capable
of predicting subsurface soil layering, the stability of slopes,
and landslide risk in a specific study area with an acceptable
level of confidence.

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