Examination of ground settlement prediction using
machine learning
Motohei Kanayama & Kaisei Sato
Faculty of Agriculture – Iwate University, Morioka, Iwate, Japan
ABSTRACTThe purpose of this study is to construct a consolidation settlement prediction method using machine learning, and examine
the prediction accuracy when new observation data were applied to the trained model. Regarding the input / output
relationship of the neural network, the method of learning the settlement rate was adopted by using the input value as theelapsed time and the amount of settlement at that time and the output value as the settlement rate. It is found that thetrained model had high prediction accuracy, the average ratio of the predicted value to the measured value were in therange of 1.04 to 1.10, when different data from the training data were used and the difference in the final settlement amount
was small. However, when the final settlement amount during training was larger than the final settlement amount of thedata used for prediction, the prediction accuracy improved as the data used for prediction increased.
RÉSUMÉ
Le but de cette étude est de construire une méthode de prédiction de tassement de consolidation à l'aide de l'apprentissage
automatique et d'examiner la précision de la prédiction lorsque de nouvelles données d'observation sont appliquées aumodèle formé. En ce qui concerne la relation entrée/sortie du réseau neuronal, la méthode d'apprentissage du taux derèglement est adoptée en utilisant la valeur d'entrée comme le temps écoulé et la quantité de règlement à ce moment etla valeur de sortie comme taux de règlement. On constate que le modèle formé a une précision de prédiction élevée, lerapport moyen de la valeur prédite à la valeur mesurée est compris entre 1,04 et 1,10, lorsque différentes données dutemps de formation sont utilisées et que la différence dans le montant du règlement final est petit. D'autre part, lorsque lemontant de règlement final pendant l'apprentissage est supérieur au montant de règlement final des données utiliséespour la prédiction, la précision de la prédiction s'améliore à mesure que les données utilisées pour la prédiction
augmentent.

1

INTRODUCTION

The settlement of embankment structures built on soft
ground has long been a geotechnical problem and has
been extensively studied by many researchers. Softground continues to settle down over a long period of time
due to its high compressibility and low permeability.
Therefore, the management of ground settlement isextremely important for maintaining the function of facilities
and ensuring the safety of people. In recent years,
research has been conducted on a consolidation
settlement prediction method using a neural network as amethod based on dynamic observation results. At present,
it is known that this method can be used to makepredictions with higher accuracy than existing methods
such as the hyperbola method, and that predictions can bemade with relatively high accuracy for initial settlement
data.
Recent research works have been performed on
ground settlement prediction using an artificial neuralnetwork (ANN). Wang et al. (2007) presented a novel
method, combining FEM and an improved backpropagation neural network, for correction of soil
parameters in the numerical prediction of embankment
settlement. They showed that the proposed numericalback-analysis framework is very efficient in practicalengineering applications to calculate highway settlement.
Provenzano (2003) proposed a fuzzy neural network
method to predict the behaviour of structures built oncomplex cohesionless soils. In his results, a numericalexample showed the method’s effectiveness when soil
parameters are uncertain and gave suggestions for
successive applications. To obtain more accurate
settlement prediction using ANN, Shahin et al. (2002)
developed and verified the ANN model from a large
database of actual measured settlements, and compared
their results with the values predicted by three traditional
methods. They showed that their ANN model is a usefultechnique for predicting the settlement of shallow
foundations on cohesionless soils. Nejad et al. (2009) also
developed an ANN model for predicting pile settlement
based on approximately 1000 data sets of standard
penetration tests. They examined the network parameters
to obtain the optimum model and demonstrated that their
ANN model outperforms the traditional methods andprovides accurate pile settlement predictions. Without
using a large database, Kanayama et al. (2009 and 2014)
examined a neural network model for predicting
settlements. Using a learning pattern that focuses on theconvergence of the settlement rate, they showed that theprediction values were in good agreement with themeasurement values, using measurement records up to a
consolidation stage of 60% as teach data for the ANN. The
use of ANN for settlement prediction does have difficulties
in assessing the pre-consolidation pressure (Çelik and Tan
2005) and the displacement back-analysis to identify soil
parameters by using a genetic algorithm (Feng et al. 2004).
In this study, extending the neural network model
described by Kanayama et al. (2009 and 2014), theprediction accuracy of the prediction method using the

2

MEASUREMENTSANDAPPLICATIONOFARTIFICIAL NEURAL NETWORK MODEL TOSETTLEMENT PREDICTION

2.1

Measurements

In this study, the settlement data (BLO) measured at the
Bloemendalerpolder near Amsterdam in the Netherlands
and the data measured at the settlement plates 1, 4, 5, 8
and 9 (respectively, TAM-1, TAM-4, TAM-5, TAM-8 andTAM-9) in the pre-loaded earth-fill in Tamana City,
Kumamoto Prefecture, Japan were used. The former
ground is mainly composed of peat, the embankment wasconstructed in five lifts up to a final level of 3.3 m. Prior tothe construction of the embankment, vertical wick drains
were installed in the soft soils underneath the embankment
to accelerate drainage. The measurements were taken
during 1 year, commencing from the start of construction.
The latter ground is composed of alluvial clay called Ariake
clay. In the Tamana Yokoshima Coastal Conservation
Project, the drainage gutter gate, which is the mainconservation facility, was completely relocated andrenovated. In order to prevent the residual subsidence ofthe foundation ground due to the embankment backfilling
after the construction of the main body of the gutter and theembankment of the mounting embankment, the preload
embankment method using the plastic board drain method
was adopted as the foundation treatment method. The
embankment is divided into four stages, and in this study,
the subsidence data for about one year from thecompletion of the final embankment to the removal of theembankment was used.
Figure 1 shows each settlement behavior. The final
settlements of BLO, TAM-1, TAM-4, TAM-5, TAM-8, andTAM-9 were 43.9, 58.9, 44.8, 53.6, 70.1, 25.6 cm,
respectively, and the settlement amount of TAM-8 was thelargest. BLO, TAM-5, and TAM-8 were used for learning,
and TAM-1, TAM-4, and TAM-9 were used for prediction.
This is to know the applicability of the network model todifferent settlement behaviors.
2.2

Application of ANN to Settlement Prediction

An artificial neural network (ANN) is an information
processing paradigm that is inspired by the way biologicalnervous systems, such as the brain, process information.
The perceptron model has the ability to recognize patterns,
and ground settlement prediction by a neural network is amethod that utilizes this ability. Specifically, the network
can recognize the mapping relationship from the input layer
to the output layer by modifying the weights with known
data described here as teach data. The unknown output
value can then be estimated by applying the known input
value corresponding to the predictive value introduced to
the network.
In this paper, the learning pattern and number ofdivisions for the teach data was adopted from Kanayama
et al. (2009 and 2014), as shown in Figure 2. It is notablethat the neural network learned the settlement rate, vi, fromthe elapsed time, ti, and the settlement, Si; the predictedvalue, Si+1, was calculated as Si+1 = Si + vi t; and thesuccessive time, ti+1, was updated by ti+1 = ti + t. During
data processing, the input and output data were normalized
by the maximum value in the teach data. The parametersin the neural network were as follows: gain is 1.0, learning

Settlement

neural network model was examined with the aim of
constructing a highly versatile consolidation settlement
prediction method.

Known values
Cubic spline curve
Teach data
Predicted values

Input Output
ti, Si ⇒ vi
i = 1, 2,…, 20

Input Output
ti, Si ⇒ vi
i = 21, 22,…, nti+1 = ti +  t
Si+1 = Si + vi  t

Learning

Predicting

Elapsed time0
Settlement (cm)Records for learning

Figure 2. Correction for equal time interval and learningand predicting method (from Kanayama et al. 2014)

20
BLO

40

Trained Data

TAM-5

60
TAM-8

80

Network Model Building

0
Settlement (cm)Records for predicting

20
TAM-9

40

Input Prediction Accuracy?

TAM-4

60
TAM-1

80

0

100

200
Elapsed day (day )Newly Recorded Data

300

Figure 1. Settlement behavior recorded at each site

Figure 3. Outline of this study

showing the variation of APR, and the closer the value wasto 0, the smaller the variation.

ratio is 1.0, number of hidden layers is 10, and number oflearning iterations is 100,000.
Figure 3 is a diagram showing the outline of thecontents of this research. By saving the model obtained bylearning, the applicability of the prediction method
examined in this study was examined when data outside
the learning range was given or when settlement prediction
at different points was performed. In this study, teach datawith settlement ratio R = 35%, 50%, and 60% were createdfor learning and prediction. R was defined as the ratio of
each measured value to the final settlement, and the effectof the number of data used for learning on the prediction
results was examined. The average prediction ratio, APR,
and coefficient of variance, CV, were used as evaluationindexes for the accuracy of settlement prediction. APR was
the average value of the prediction ratio. If it was larger
than 1, it indicated an overestimation, and if it was smaller
than 1, it indicated an underestimation. CV was an index
0

3
3.1

40
60

(b) Applying R=60% data to R=50% data-trained model

Settlement (cm)

20
40
60

(c) Introducing re-learning criteria in case (a)

20
40
60

(d) Introducing re-learning criteria in case (b)

20
40
60

0

100

200
Elapsed day (day)300

Figure 4. Prediction results and accuracy improvement oftrained-model with same point data
Teach

Applicability of ANN model to settlement data atsame point

The applicability of the network model to settlement
prediction was examined, when data outside the learning
range was given at the same point. Ten sets of predictions
were derived for each calculation. Because the initial
weights on the hidden and output layers were set byrandom numbers, the reproducibility of prediction accuracy
should be confirmed.
Figure 4 shows the results of the settlement predictions
using the basic neural network model, applying R = 50 %data to a trained model with R = 35 % data (case (a)) andR = 60 % data to a trained model with R = 50 % data (case(b)). Additionally, the figures of case (c) and (d) show thatthe prediction accuracy of case (a) and (b) was improved,
respectively. According to the results of the case (a) and(b), as the APR values are 1.23 and 1.10 and the CV values
were 7.4 and 2.8 %, respectively, it is found that the bothpredictions were overestimated vary widely and theaccuracy were low.
To improve this prediction accuracy, the acceptancecriterion for utilizing predicted values as teach data wasintroduced (Kanayama et al. 2014). Figure 5 shows theschematic depiction about improving the network and thecriterion for adding average predicted values to teach data.
The criterion was based on the coefficient of variance,
CVP, for the predicted values, and the discrimination was
conducted in terms of relative variation. The value of CVP
was specified by less than 1.0 %. The calculation in thisnetwork model was done as shown in Figure 5. At first, thenetwork could learn about initial teach data 100,000 times
and compute the 10 sets of settlement prediction. Using
these predicted settlements, the CVP was checked
whether satisfied the criterion or not. In the part of thepredicted settlements that the parameter satisfied thecriterion, the average value was calculated from 10 sets of(a) Applying R=50% data to R=35% data-trained model
original datateach data
predicted data

20

RESULTS AND DISCUSSION

Predicted Data

t1

t2

…

ti

ti+1

S1

S2

…

Si

Si+1

j

j

ti+2

j

Si+2

j

ti+4

…

Si+4

…

ti+3

j

Si+3

j

j

j

tn-1

j

Sn-

tn

j

Sn

1

v1

v2

…

vi

j

vi+1

j

vi+2

j

vi+3

j

vi+4

New Teach Data
t1

t2

…

ti

ti+1

S1

S2

…

Si

Si+1

v2

…

vi

*

vi+1

j

vn-1

New Predicted Data
ti+2

ti+3

*

*

Si+

Si+

2

v1

…

*

vi+2

j

ti+4

…

Si+4

…

j

j

j

Sn-

3
j

vi+3

1
j

vi+4

…

j

tn-1

j

vn-1

tn

j

Sn

Criterion for adding average
predicted values to teach data
CVP of jS < 1.0 %( j = 1, 2, …, 10)
For example, jSi+2 and jSi+3 satisfy
the above criterion, the averagevalues are added into teach data
as following.
*
Si+2= jSi+2 /10, *Si+3= jSi+3 /10*
vi+1= jvi+1 /10, *vi+2= jSi+2 /10
The network can compute newprediction data with using teachdata containing previous
predicted values repeatedly.

Figure 5. Improvement of network model and criterion for adding average predicted values to teach data (fromKanayama et al. 2014)

predicted values and added into teach data as new teachdata. When not satisfied, the corresponding data wasturned down. Then the network could continue to learn andpredict again and again. Finally, the calculation was
stopped if the parameter of all predicted settlements
satisfied the criterion.
As can be seen from the case (c) and (d), the prediction
accuracy improved significantly for both 35 % and 50 %0(1)R = 35%

020

Untrained Model
TAM-4
20
teach data
predicted data

40

40

BLO Trained Model
TAM-4
teach data
predicted data

data. Because of prescribing the variance of predicted
settlement and using as teach data, the resultant prediction
values had less variance. Focusing on the high accuracyof short-term settlement prediction, the improvement wasdone for installing the short term predicted values thatsatisfied the clear criterion to the network teach data.
From this result, it was found that the trained model canbe applied to the data at the same point.
020
40

Settlement (cm)(4)R = 35%

60

TAM-5 Trained Model
TAM-4
teach data
predicted data

040(7)R = 35%(10)R = 35%

60

60

60

20

20

20

20

40

40

(2)R = 50%

40(5)R = 50%

6040(8)R = 50%(11)R = 50%

60

60

60

20

20

20

20

40

40

(3)R = 60%

40(6)R = 60%

60

0

100

200

300

60

0

TAM-8 Trained Model
TAM-4
teach data
predicted data

2040(9)R = 60%

100

200

60
300
0
100
Elapsed day (day)(12)R = 60%

200

300

60

0

100

200

300

Figure 6. Prediction results of another recorded data (TAM-4) by using the trained models
0

Untrained Model
TAM-1
teach data
predicted data

20

Settlement (cm)400

BLO Trained Model
TAM-1
teach data
predicted data

20
40

60
0

60
0

(4)R = 35%

20

20

20

40

40

40

60
0

60
0

(2)R = 50%600(5)R = 50%

20

20

20

40

40

40

60

60

(3)R = 60%

0

100

200

300

TAM-5 Trained Model
TAM-1
teach data
predicted data

40

60
0

(1)R = 35%

0
20

60

(6)R = 60%

0

100

200

0

TAM-8 Trained Model
TAM-1
teach data
predicted data

20
40
60
0

(7)R = 35%(10)R = 35%

20
40
60
0

(8)R = 50%(11)R = 50%

20
40
60

(9)R = 60%

300
0
100
Elapsed day (day)

200

300

(12)R = 60%

0

100

200

300

Figure 7. Prediction results of another recorded data (TAM-1) by using the trained models
0

Untrained Model
TAM-9teach data
predicted data

10
20

0

BLO Trained Model
0
TAM-9
10
teach data
predicted data 201020(4)R = 35%

Settlement (cm)(1)R = 35%

30

30

10

10

20

20
(2)R = 50%(5)R = 50%

30

30

10

10

20

20
(3)R = 60%

30

0

100

(6)R = 60%

200

300

30

0

100

200

30
40
50
10
20
30
40
50
10
20
30
40
50

TAM-5 Trained Model
0
TAM-9
10
teach data
predicted data 20(7)R = 35%(8)R = 50%(9)R = 60%

300
0
100
Elapsed day (day)

200

300

30
40
50
10
20
30
40
50
10
20
30
40
50

TAM-8 Trained Model
TAM-9teach data
predicted data

(10)R = 35%(11)R = 50%(12)R = 60%

0

100

200

Figure 8. Prediction results of another recorded data (TAM-9) by using the trained models

300

Table 1. Prediction accuracy of the untrained and trained models
Data
TAM-4

TAM-1

TAM-9

3.2

R (%)
35
50
60
35
50
60
35
50
60

Untrained Model
APR
CV (%)
2.10
6.34
1.94
7.75
1.00
1.93
0.99
1.82
0.95
1.42
0.98
0.84
0.79
3.42
0.83
0.85
0.82
0.51

BLO Trained Model
APR
CV (%)
0.93
0.18
0.96
0.21
0.93
0.22
0.87
0.20
0.82
0.26
0.86
0.22
1.19
0.85
0.99
0.89
0.92
0.79

Applicability of ANN model to settlement data at
different point

Next, the applicability of the network model that learned thesettlement data at other points was examined. Specifically,
let the network model train the total settlement dataobtained at a certain point. Using the network constructedby the learning, the applicability of the network model was
examined when predicting the data newly observed atother points.
Figures 6 to 8 show the results of predicting thesettlement data of TAM-4, TAM-1, and TAM-9 using an
untrained network model and network models that learned
BLO, TAM-5, and TAM-8, and Table 1 shows the prediction
accuracy of each result.
From the results of the untrained model in Figures 6(1)to (3), the APR value of case (1) to (3) was 2.10, 1.94, and
1.00, and similarly the CV was 6.34, 7.75, and 1.93 %,respectively. When too little measurements were used asteach data, for cases (1) and (2), clear differences wereobserved between the predicted and recorded values, andthe variance of predicted values became larger with anincrease in the elapsed time. In case (3), the predicted
settlement was in good agreement with the measured data,
but the variance was still large over time.
However, according to the results of the trained modelin Figures 6(4) to (12), it can be seen that the variation in
the predicted values was eliminated as the CV values werein the range of 0.09 to 0.22. Especially for the TAM-5 andTAM-8 trained model, Figures 6(7) to (12), the larger the Rvalue, the better the prediction accuracy, the APR value
tended to be close to 1.00. However, the results of the BLO
trained model were in underestimation as the APR value
was in the range of 0.93 to 0.96, it is considered that thedifficulty existed to expect an improvement in prediction
accuracy when making predictions for settlement data
larger than the learning range.
From Figures 7(1) to (3), the regularity of theobservation data, with less variance in value itself, washigh, even the predicted values of the untrained model
were in good agreement with the measurements. The APR
values were in 0.95 to 0.99 and the CV values were in 0.84
to 1.82 %. From this result, it can be seen that the regularityof the settlement data used for the prediction was importantin the settlement prediction using the neural network.
In the prediction results by the trained models, Figure7(4) to (12), the TAM-8 trained models produced the better
prediction accuracy than the other models, and the APR
values were in the range of 1.04 to 1.07 and the CV values

TAM-5 Trained Model
APR
CV (%)
1.10
0.10
1.08
0.10
1.04
0.11
0.97
0.09
0.92
0.11
0.93
0.10
1.56
1.05
1.26
1.30
1.13
1.30

TAM-8 Trained Model
APR
CV (%)
1.27
0.09
1.23
0.09
1.17
0.15
1.07
0.08
1.04
0.14
1.04
0.14
1.98
0.99
1.60
1.38
1.41
1.50

were in 0.08 to 0.14. The results by the BLO and TAM-5
trained models were affected by the out of range of the
training settlement data and therefore the APR values wereless then 1 and seemed to be constant. These results weresame trend as the Figures 6(4) to (6).
In the case of the TAM-9 settlement measurement,
Figure 8, the behavior of this settlement was uniquecompared to the other settlement behavior. While the othersettlement curves showed a gentle curve shape with thepassage of time, the TAM-9 settlement data showed analmost linear increase with the passage of time. Moreover,it can be seen that the irregularities of the observed values
were remarkable, and the variations of the measured
values were large. The observations with this tendency are
not uncommon and are often observed in the field. It is
important to use such settlement data in order tounderstand the application limits of this method.
In the results by the untrained model, Figure 8(1) to (3),the settlement predictions were in underestimation. This isbecause the gradients at the beginning and end of theobserved values were different, and it is shown that theprediction cannot be made accurately only with theinformation contained at the beginning of the observed
values. However, focusing on the teach data and the output
value, it can be seen from the figure that the values output
from this model reproduce irregular teach data. Thus, it isfound that the learning of this model was functioning
effectively.
According to the results by the trained models, not goodresults were achieved in all predictions, Figures 8(4) to(12). The first point is that the output value of the model
could not reproduce the teach data. This indicates that themodel could not cope with irregular data such as the TAM9 because the model learned relatively highly regularsettlement data. The second is that the predicted value
showed a settlement behavior completely different from theobserved value. As the predicted settlement showed thegradual curve shape, it is presumed that the training of themodel was performed effectively and the output value
sufficiently reproduced the settlement data used fortraining. However, when using the settlement data that
tended to be different from the teach data, it is necessary
to pay attention to use this model. The APR values of the
trained models were in 0.92 to 1.98, the CV values were in
0.79 to 1.50 %, and these values were unacceptable aspredicted values.
From the above results, it was found that the accuracyof the settlement prediction using this model depended on
the similarity between the settlement data used for trainingand the settlement data used for prediction. Specifically, if
the learned settlement data was larger than the settlement
data used for prediction, the output value of the model
tended to be equal to or overestimated, and the prediction
accuracy tended to improve as the data range used
increased. However, when the learned settlement data wassmaller than the data used for prediction, the predictedvalue was underestimated from the observed value, andthe prediction accuracy remained constant even if thenumber of data used for prediction increased. Finally, if thesettlement data used for prediction had a large irregularity
and the settlement behavior was different from the training
data, it was found that this model was not applicable. To
improve this difficulty, it is necessary to simply categorize
the settlement behavior of the ground into several types
and let the model learn them so that the model candetermine which pattern the target settlement behavior issuitable for. Further investigation on the applicability of themethod should be considered.
4

CONCLUSION

In this paper, extending the neural network model
described by Kanayama et al. (2009 and 2014), theprediction accuracy of the prediction method using theneural network model was examined with the aim of
constructing a highly versatile consolidation settlement
prediction method.
The applicability of the network model to settlement
prediction was examined, when data outside the learning
range was given at the same point. Introducing theacceptance criterion for utilizing predicted values as teachdata, and the prediction accuracy improved significantly, it
was found that the trained model could be applied to thedata at the same point.
Additionally, the applicability of the network model thatlearned the settlement data at other points was examined.
It was found that the accuracy of the settlement prediction
using this model depended on the similarity between thesettlement data used for training and the settlement data
used for prediction. When the learned settlement data waslarger than the settlement data used for prediction, theoutput value of the model tended to be equal to or
overestimated, and the prediction accuracy tended to
improve as the data range used increases. However, when
the learned settlement data was smaller than the data used
for prediction, the predicted value was underestimatedfrom the observed value, and the prediction accuracy
remained constant even if the number of data used forprediction increased. Finally, if the settlement data used forprediction had a large irregularity and the settlement
behavior was different from the training data, it was foundthat this model was not applicable.
ACKNOWLEDGEMENTS
Partial financial supports for this study have been provided
by the Ministry of Education, Science and Culture, Japan(Grant in Aid for Scientific Research, No. 15K07641).
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