Field Performance of a Genetic Algorithm in the Settlement Prediction of a Thick Soft Clay Deposit in the Southern Part of the Korean Peninsula 2015 E

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Field Performance of a Genetic Algorithm in the Settlement Prediction of a

Thick Soft Clay Deposit in the Southern Part of the Korean Peninsula

Hyun Il Park, Kyu-Sun Kim, Ha Young Kim

PII: S0013-7952(15)30021-1

DOI: doi:10.1016/j.enggeo.2015.07.012

Reference: ENGEO 4104

To appear in: Engineering Geology

Received date: 27 August 2014

Revised date: 15 July 2015

Accepted date: 16 July 2015

Please cite this article as: Park, Hyun Il, Kim, Kyu-Sun, Kim, Ha Young, Field Per-formance of a Genetic Algorithm in the Settlement Prediction of a Thick Soft ClayDeposit in the Southern Part of the Korean Peninsula,Engineering Geology (2015), doi:10.1016/j.enggeo.2015.07.012

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http://dx.doi.org/10.1016/j.enggeo.2015.07.012http://dx.doi.org/10.1016/j.enggeo.2015.07.012http://dx.doi.org/10.1016/j.enggeo.2015.07.012http://dx.doi.org/10.1016/j.enggeo.2015.07.012


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Field Performance of a Genetic Algorithm in the Settlement Prediction of a Thick Soft Clay

Deposit in the Southern Part of the Korean Peninsula

Hyun Il Park1

, Kyu-Sun Kim2, and Ha Young Kim

3

Abstract

Long-term settlement data of a thick soft clay deposit improved by vertical drains frequently

shows different field settlement behavior from laboratory-driven predictions by conventional

theories due to the variability and uncertainty of the soil properties, modeling simplifications,

and types of prediction methods. This paper presents the field application of a back-analysis

method based on a genetic algorithm (GA) to evaluate the performance of a new settlement

prediction method compared with conventional graphical settlement prediction methods, such as

the hyperbolic method and the Asaoka method. The GA back-analysis method shows better

flexibility in modifying surcharging plans and adaptability to multi-layered thick soft soil

deposits at the early stages of post-construction settlements. Thus, this new settlement prediction

method enables geotechnical engineers to subsequently modify the heights of surcharge fills

subsequently in accordance with field settlement data monitored in the interim for rapid and cost-

effective construction. The comparative results show that the GA back-analysis method is

capable of superior field performance in settlement predictions compared with two conventional

1 Principal Research Engineer, Construction Technology Division, Samsung C&T Corporation, Seoul 135-935

Korea, Email:[email protected] Principal Research Engineer, Construction Technology Division, Samsung C&T Corporation, Seoul 135-935

Korea. E-mail:[email protected] (Corresponding author)3 General Manager, Technical Advisory Team, Samsung C&T Corporation, Seoul 137-956 Korea. E-mail:

[email protected]
mailto:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]


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graphical methods, within a margin of less than 200 mm in a thick soft clay deposit with multiple

layers under complex loading conditions.

Highlights

We assessed the field performance of the GA method in settlement prediction. The GA method shows a higher performance in a multi-layered thick soft soil deposit. The GA method predicts the final settlement at the early stage of construction.

KeywordsBack analysis; Consolidation; Genetic algorithm; Settlement prediction; Soft clay; Vertical drain

1. Introduction

Over the past two decades, several construction projects in Korea have been built on thick soft

soil deposits to create new national infrastructure, such as container ports and a new international

airport, which were built on reclaimed areas. As a cost-effective soil improvement method,

embankment preloading associated with vertical drains is commonly used to accelerate the

consolidation process and predict the consolidation settlements of foundation soils due to

surcharge loading. Settlement prediction in a large-construction project is an important issue for

rapid and cost-effective construction; however, it is frequently reported that geotechnical

engineers fail to obtain accurate predictions of the long-term field settlements of soft soils even if

a robust calculation with advanced geotechnical technology was implemented during the design

stage. Significant errors in settlement predictions during ground improvement work have been

reported in many construction sites in Korea. Recent construction records in the southern part of

Korea indicate that the observed settlements are is generally 1.5 to 3 times greater than the

predicted settlements predicted in the design stage. It is thought expected that these errors in

settlement prediction errors are mainly caused by the variability and uncertainty of the soil

properties (Kim, 2008; Chung et al., 2009; Park et al., 2009; Chung et al., 2012).

Although the accuracy of numerical schemes and testing techniques has been significantly

improved, there are still errors between the predicted and observed magnitudes of settlement

during a prediction and during an observation. Possible reasons for these errors are the following


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as follows: (1) sampling disturbances and effective stress condition during sampling, delivery,

and trimming procedures; (2) a simplified analysis model, loading condition, boundary condition,

and initial condition used during the design stage; (3) measurement errors caused by operators

and systems; and (4) variability and uncertainty of the soil properties (Bjerrum, 1967;

Jamiolkowski et al., 1985; Terzaghi et al., 1996). Graphical methods using observed settlement

data are commonly used employed as practical tools for interpreting field settlement data to

reduce the variability and uncertainty of the consolidation parameters governing the magnitude

of the consolidation settlement (Asaoka, 1978; Tan and Chew, 1996).

Soil improvement performance levels are usually evaluated by analyzing analysis of the

monitored settlement data for comparison with laboratory-driven settlements determined during

the design stage. The magnitude of the additional surcharge load and the surcharge period can be

finally determined based on the interpretation of field settlement data. The consolidation

parameters in a thick soft clay deposit with vertical drains are the main points of interest in

predicting long-term consolidation settlements. The consolidation settlement of a vertical drain

installed in a soft soil layer is calculated based on Barrons equation. Several observational

methods based on settlement records are available to predict future settlement behavior,

specifically the hyperbolic method and the Asaoka method. By extrapolating extrapolation from

observed settlement data, many uncertainties regarding the variability of the soil and as well as

the magnitude and the distribution of loads can be overcome (Tan et al., 1991; Asaoka, 1978).

These graphical methods simply predict the final settlements by means of curve fitting from

observed settlement data. Therefore, observed settlement data after removal of surcharge loads

are used to predict the final settlements related to the degree of consolidation. However, these

methods are not suitable for use when predicting field settlements during unloading and


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reloading after the removal of the surcharge load and when adjusting the height and period of the

surcharging embankment (Park et al., 2009).

The BFGS (Broyden-Fletcher-Goldfarb-Shanno) method, the quasi-Newton method, and the

conjugate gradient method are conventional optimization methods for consolidation-related

back-analysis problems (Arai et al., 1983; Sakurai and Takeuchi, 1983; Shoji et al., 1990; Kim

and Lee, 1997; Park et al., 2009). These methods have been are used for consolidation problems

with a small numbers of consolidation parameters in one or two clay layers. However, the

number of consolidation parameters in a multi-layered problem is typically greater than that in a

single layer problem. This multi-dimensional optimization problem complicates the process of

estimating consolidation parameters when attempting to predict the consolidation settlement of

foundation soils accurately. Several optimization schemes generate either local or global

solutions depending on the initial values of the variables used. Furthermore, as the number of the

variables for the optimization process increases, the chance of a solution converging locally

rather than globally increases (Renders and Flasse, 1996; Leung and Wang, 2001). Therefore, in

a multi-layered soft clay deposit, it is necessary to use a robust technique that guarantees

convergence to a global solution. A back-analysis method based on a genetic algorithm (GA) can

be used as a parallel and global search tool that emulates natural genetic operators. GAs

generally show better performance when searching for a solution than conventional optimization

algorithms because GAs, which make use of an entire set of solutions spread throughout the

solution space, are less affected by local optima (Holland, 1975; Goldberg, 1989; Keane, 1995).

Park et al. (2009) showed that the GA back-analysis method has the advantage of robustly

searching for a global solution while avoiding local solutions compared to with conventional


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optimization schemes in a multi-dimensional consolidation problem with three consolidation

layers.

In this study, the GA back-analysis method was implemented to improve the field

performance of settlement predictions. The accuracy and capability of the settlement prediction

methods were investigated to evaluate the performance of settlement predictions settlement

prediction performance using field settlement data from a large construction site in Busan on

thick soft clay layers consisting of multiple-clay layers with a complex geological history.

2. Site Description

2.1 Soil Characterization

The studied site is located near in the southern coastal area of the Korean peninsula. The site

was mainly formed by an accumulation of a deltaic deposit near the Nakdong River. The thick

deltaic deposit area was formed as a valley caused by the vertical movement of the Yangsan fault

during the Cenozoic era. Various types of geo-materials have been deposited in the valley via the

river stream flow (Kim,2008). A representative sectional profile near the studied site is shown in

Figure 1. The sand Sand and gravel materials were deposited in the valley first. , and then, Then,

the soft soil deposit was followed by the deposition of different geo-materials affected by marine

transgression and regression during a the area's geological history. The sedimentary deposit can

be categorized into four layers: upper clay, silty sand, lower clay, and bottom sand and gravel

layers. The bottom sand and gravel layer is thick enough to hold fresh water in the an aquifer.

The silty sand layer in the middle is sandwiched by between the upper and lower clay layers.

Soft soil layers with different engineering characteristics have been deposited by affecting by the

flow of the river water, as well as the marine transgression and regression. The soft soil layers


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can be categorized into two distinct silty clay layers as the upper and lower clay layers. The basic

characteristic of the upper clay layer basically shows a normally consolidated state with a soft to

medium consistency; however, the lower clay layer shows stiff to very stiff characteristics. The

total thickness of the whole upper and lower clay layers ranges from approximately 50 m to 80 m

(Chung et al.,2002; 2012; Kim,2008).

The silty clay soils in the studied site are generally classified as CL and CH soils in

accordance with the Unified Soil Classification System (USCS). Figure 2 shows the profiles of

liquid and plastic limits, water content, initial void ratio, compression index, and

overconsolidation ratio of the upper and lower clay layers. There is a distinct separation around

the expected border of the upper and lower clay layers in the soil profiles. The values tend to

increase with depths above 25 m; however, they tend to reversely decrease below depths of 25 m.

When considering this distinct difference between two groups of data in the property profiles, the

clay layer can be divided into two representative layers as the upper clay layer and lower clay

layer. The laboratory-driven design values of the compression index (C c) are 0.9 for the upper

clay layer and 0.6 for the lower clay layer. The laboratory-driven horizontal coefficient of

consolidation (ch) is 8.5 10-3

m2/day for both the upper and lower clay layers. The

overconsolidation ratio of the upper clay layer ranges from 1.0 to 3.0 with depth, indicating

normally consolidated to lightly overconsolidated conditions, respectively. Generally, the

overconsolidated conditions of the upper clay layer are assumed to be normally consolidated due

to the conservative design in estimating the magnitude of consolidation settlement.

2.2 Construction Plan of Studied Site


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plates. Two graphical settlement prediction methods (i.e., hyperbolic method, and Asaoka

method) and the GA back-analysis method were implemented to compare the results of

settlement predictions results. The field settlements were was monitored for about 540 days for

this comparative performance evaluation in of settlement predictions.

3. Observational Methods in Predicting Ground Settlement

In preloading embankments with vertical drains on soft soil deposits, differences between

observed and predicted settlements frequently are induced from by the variability and uncertainty

of the soil properties. To supplement address the these differences, the degree of consolidation

should be determined by the interpretation of the observed settlement data using graphical

methods or a back-analysis method. In this study, observed settlement data obtained from in a

construction site were used to predict final settlements by using the GA back-analysis method

(Park et al., 2009), the conventional hyperbolic method (Tan et al., 1991), and the Asaoka

method (Asaoka, 1978). Semi-empirical graphical methods (i.e., hyperbolic method, and

Asaokas method) are practical in for settlement predictions because they rely on observed

settlement data to predict the end of primary consolidation, which can be updated as more

observed settlement data become available using simple calculation and curve fitting. However,

the conventional methods sometimes have limitations in some aspects of the time interval of

observed settlement data and the minimum period duration of monitoring duration (Edil et al.,

1991; Tan et al., 1991). The hyperbolic method can predict final settlement using settlement data

at the start of construction; however, settlement predictions for long-term field behavior using

the early-stage settlement data sometimes mislead skew the results of settlement predictions.

Thus, Therefore, before using the hyperbolic method, it is recommended to collect settlement


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data when the data reach more than a 60% degree of consolidation before using the hyperbolic

method. The Asaoka method uses the end of construction settlement data, and settlement

predictions can only be made after the end of construction; thus, hence, the Asaoka method takes

a longer time to predict future settlement compared with the hyperbolic method (Tan and Chew,

1996; Chung et al., 2009). Thus, the performance of the selected observational methods should

be carefully evaluated from the characteristic of the curve plotted.

A back-analysis technique has been used as an alternative tool to replace conventional semi-

empirical methods not only to accurately predict final settlements but also to inversely evaluate

back-analyzed parameters (Sakurai and Takeuchi, 1983; Gioda and Sakurai, 1987). The

differences between the results of numerical simulation to estimate soil behavior and the actual

measured data are defined as an objective function; then, the back-analysis technique inversely

estimates input parameters using an optimization scheme to minimize the value of the objective

function. The consolidation parameters containing actual field settlement behavior can be

estimated through a back-analysis procedure using measured settlement data. Through this

procedure, the design quality can be evaluated by estimating residual settlements by loading and

unloading, and the optimized design is conducted determined by optimization of the height and

period for surcharge loading. Park et al. (2009) proposed the GA back-analysis method to

inversely analyze multiple design parameters for multi-layered consolidation settlement data and

then evaluated the performance of the proposed method with other optimization schemes for

model problems. In this study, the collected field settlement data collected during the

consolidation process were interpreted by graphical fitting methods and the GA back-analysis

method to evaluate the magnitudes of final settlements of thick soft soil layers caused by

embankment preloading.


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3.1 Settlement Prediction Model in Soft Soils Improved by Vertical Drains

Barron (1948) proposed an analytical solution to calculate the consolidation settlement of a

soil layer where a vertical drain is installed that considers free-strain and equivalent-strain

constraints based on Terzaghis one-dimensional consolidation theory. The consolidation

analysis based on the equivalent strain condition assumes that the initial surcharge loading is all

applied as excess pore water pressure, that the strain develops in a vertical direction, and that the

influence boundary of the vertical drainage is circular. The time factor of consolidation (Th) is

determined from Eq. (1-a), and the relationship between the average degree of consolidation

(Uavg) and the time factor of consolidation is expressed in Eq. (1-b). Using these relations, the

final settlement (sf) of a soft clay deposit is calculated from Eq. (1-d) in the design stage.

2

e

hh

d

tcT

(1-a)

)n(F

T8exp1U havg (1-b)

2

2

2

2

n4

1n3)nln(

1n

n)n(F

(1-c)

'0

'0

0

cf logH

e1

Cs (1-d)

where n is the spacing ratio (n = de/dw = 27.2), chis the horizontal consolidation coefficient, This

the time factor, de is the equivalent diameter of the drain (de = 1.13S = 1.36 m), dw is the

drainage diameter (dw = 0.05 m), Uavg is the average consolidation ratio, F(n) is the drainage

spacing influence factor, sf is the final settlement, Cc is the compression index, e0is the initial


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void ratio, H is the thickness of clay layer, r0 is the initial effective stress, 0 is the effective

stress, and is the surcharge load.

3.2 Hyperbolic Method

The hyperbolic method is the most popular settlement prediction method. The method

assumes that the shape of the future settlement curve is hyperbolic. Since a hyperbolic

relationship can generate future settlement behavior with only the initial settlement data, this

method can be a powerful tool for settlement predictions. However, geotechnical engineers need

to make sure to must collect sufficient settlement data to improve the accuracy of their prediction

in the final settlement to prevent unexpected excess settlement at the end of construction (Tan et

al., 1991; Tan, 1993; 1995).

The ratio of elapsed time (t-t0) and the settlement during elapsed time (st-s0) can be plotted as

shown in Fig. 3(a). , and then, Then, slope and intercept in the y-axis are determined by a

linear regression. Finally, the final settlement (sf) is calculated by Eq. (2-b):

tss

t

0t

(2-a)

1

ss 0f (2-b)

where stis the settlement at time t, s0is the settlement at the end of surcharging at t 0, and and

are the empirical constants of the hyperbolic equation.

3.3 Asaoka Method

Asaoka (1978) proposed a graphical method to predict the final settlement (sf) based on an

observation procedure. The Asaoka method has been broadly used to predict the magnitude and


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value was determined at the generation number of 30 to obtain consistent results. Figure 6 shows

the variations of consolidation parameters during the optimization procedure by back calculation

with a 30-day interval. After 220 days (U 40%), the induced consolidation parameters tended

to be constant regardless of measurement periods.

Figure 7 shows the comparative compares the results of the predicted settlement curve by

design, measurement, as well as the back-analysis stages depending on in relation to a specified

time period. The GA back-analysis method predicts predicted the settlement behavior very well

compared to with the measured settlement data. The difference between the predicted final

settlements predicted by back calculation at a 35% degree of consolidation and back calculation

at a 65% degree of consolidation is was less than 200 mm. This result shows that the GA back-

analysis method shows a has better prediction capability at the early stage of the post-

construction settlement even if the degree of consolidation is less than 50%.

The back-analyzed parameters at each settlement plate are summarized in Table 2. The

results show that the consolidation parameters induced from the field settlement data are were

significantly different from the laboratory-driven parameters. In the upper clay layer, the back-

analyzed compression index is was generally underestimated in comparison with the laboratory-

driven compression index; however, the results in the lower clay layer showed a reverse trend. In

addition, the coefficient of consolidation is was generally overestimated in at the design stage

compared with the laboratory-driven parameter. The back-analyzed compression index of the

upper clay layer ranges ranged from 0.18 to 0.58, and that of the lower clay layer ranges ranged

from 0.71 to 1.10. The compression indexes of the upper clay layer and lower clay layer at the

design stage were originally 0.9 and 0.6, respectively. However, the results of the back analysis

showed a reverse trend compared with the design values. This reverse trend includes complex


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measured settlements. On the other hand, the Asaoka method generally underestimates

underestimated the final settlement, which may cause lead to the application of an additional

surcharge fill to satisfy the design criterion. Thus, when geotechnical engineers consider

determining the completion of a consolidation settlement of clay deposit (e.g., U > 90%), they

need to know the characteristics of a selected settlement prediction method in order to avoid

additional construction cost. The comparative results A comparison of the performance in

settlement predictions performance results shows that the GA back-analysis method most

accurately predicts provides the most effective results in predicting the final settlement. The GA

back-analysis method provides the lowest amount of error variance when predicting final

settlement amounts (< 200 mm), followed by the Asaoka method (< 500 mm) and the hyperbolic

method (< 600 mm).

5. Field Application of Modified Surcharging Plan

Field geotechnical engineers are required to check and modify a design surcharging plan

during construction to reflect the actual field conditions in the of settlement management. Thus,

Therefore, they should check and predict whether the field settlement will be completed with

achieving above 90% consolidation using a reliable settlement prediction method. The beauty of

the GA back-analysis method is its flexibility to easily modify the surcharging fill using the

back-analyzed consolidation parameters. For example, the original and modified surcharging

plans and the settlement curves of the plate SP-12 are shown in Fig. 9. One-hundred and ninety

days after loading, field settlement was greater than the showed excessive settlement compared

to settlement predicted in the design stage. Thus, it was expected that the settlement could not

satisfy the design criterion of 90% consolidation settlement within the given construction period.


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Thus, the height of the surcharge fill was recalculated using the consolidation parameters back-

analyzed from the settlement measured for 190 days using the GA. and then, Then, it was

determined that an additional 5 m surcharge fill would be required to satisfy the design criterion

of 90% consolidation settlement within a specified construction period. The final result shows

that the GA back-analysis method can modify the surcharging plan and predict future settlement

behavior using back-analyzed consolidation parameters at the early stage of construction (260

days, U < 50%).

6. Conclusion

A comparative evaluation of the performances of settlement prediction methods was

conducted for a site on a multi-layered thick soft clay deposit in Korea. Settlement data from 13

settlement plates at the site during embankment preloading were used to evaluate the accuracy of

the settlement prediction methods. The results showed that the GA back analysis method offers

better accuracy when used to predict the final settlement even when the surcharging plan is

modified at the early stage of construction. The specific findings of this study are summarized as

follows:

1) Among the three settlement prediction methods assessed, the hyperbolic method tends to

overestimate the final settlements compared to with the other two methods; however, the

variances of the predicted settlements in the hyperbolic method are smaller than those in the

Asaoka method.

2) In the upper clay layer, the back-analyzed compression index is generally underestimated in

comparison with the laboratory-driven compression index; however, the results in the lower

clay layer show a reverse trend. In addition, the coefficient of consolidation is generally


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overestimated in the design stage compared with the laboratory-driven parameter. These

discrepancies may be caused by due to the conservative design concept in the lightly

overconsolidated region and the effect of sampling disturbances.

3) The GA back-analysis method gave achieved the most reliable and consistent settlement

predictions for most of the 13 settlement records within a margin of less than 200 mm

compared with recorded settlements measurements.

4) The GA back-analysis method shows the lowest amount of error variance when predicting

final amounts of settlement amounts (< 200 mm), followed by the Asaoka method (< 500

mm) and the hyperbolic method (< 600 mm).

5) The GA back-analysis method can predict the final settlements with back-analyzed

parameters at an early stage of construction (U < 50%) regardless of modifications to the

surcharging plan.

The overall results showed that the GA back-analysis method offers better accuracy when used

to predict the final settlement even when the surcharging plan is modified at the early stage of

construction. However, a small number of variables (Ccand ch) in each layer were implemented

to the GA back-analysis method of this study to minimize the calculation time for the practical

use of a settlement management tool at the construction site. Thus, if we need to obtain more

accurate settlement predictions, we may consider using more variables during the GA back

analysis to predict much closer curve-fitting to the actual settlement behavior under complex

loading conditions on multi-layered soft soil deposits.

Acknowledgements


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The authors acknowledge the support of the Construction Technology Division of Samsung

C&T Corporation. The authors appreciate the constructive comments and suggestions from the

anonymous reviewers.

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Terzaghi, K., Peck, R.B., Mesri, G., 1996. Soil Mechanics in Engineering Practice. John Wiley

and Sons, New York.


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Table 1.Basic parameters for optimization using the genetic algorithm.

Parameters Values

Number of individuals 1000

Probability of reproduction (%) 70 %

Probability of mutation (%) 0.5 %

Number of maximum generations 30


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Table 2.Sectional information and back-analyzed consolidation parameters.

Settlement

Plate

Thickness (m)

Height ofsurcharge

fill (m)

Back-analyzed parameters

Upper

claylayer

Lower

claylayer

Ccch10

-3

(m2/day)

Upperclay

layer

Lowerclay

layer

Upperclay

layer

Lowerclay

layer

SP-1 30.0 25.0 18.6 0.38 0.77 9.20 74.63

SP-2 30.0 20.0 18.2 0.38 0.94 8.83 43.23

SP-3 22.2 20.0 18.1 0.31 0.89 6.48 19.25

SP-4 22.5 20.0 19.9 0.31 1.05 9.13 14.72

SP-5 22.7 12.0 19.2 0.45 1.09 8.29 18.75

SP-6 22.0 10.0 16.3 0.45 0.96 37.57 6.79

SP-7 22.0 10.0 16.7 0.40 1.10 9.38 45.00

SP-8 18.5 - 14.9 0.45 - 18.15 -

SP-9 11.5 - 13.5 0.36 - 1.50 -

SP-10 8.5 - 10.9 0.18 - 16.81 -

SP-11 8.5 - 11.0 0.39 - 11.44 -

SP-12 23.0 7.0 18.0 0.58 0.71 10.52 98.36

SP-13 16.7 - 10.3 0.26 - 18.26 -

* Note: Cc (design)= 0.9 (upper clay layer), 0.6 (lower clay layer)

ch (design)= 8.510-3

m2

/day (upper and lower clay layers)


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Table 3.Predicted final settlements by settlement estimation methods.

SettlementPlate

Measuredfinal

settlement(m)

Predicted final settlement (m)

Design

stage

Hyperbolic

method

Asaokas

method

Genetic

Algorithm

SP-1 5.19 6.84 5.56 5.14 5.35

SP-2 5.44 5.87 5.88 4.94 5.57

SP-3 5.96 6.51 6.50 5.62 6.11

SP-4 5.55 5.37 6.04 5.62 5.58

SP-5 5.65 5.37 6.14 5.72 5.73

SP-6 4.21 5.17 4.57 4.19 4.33

SP-7 4.08 4.98 4.46 4.08 4.09

SP-8 2.74 3.77 2.94 2.70 2.69

SP-9 1.79 2.85 1.90 1.86 1.74

SP-10 0.79 2.21 0.85 0.94 0.71

SP-11 1.33 2.27 1.51 1.31 1.28

SP-12 4.80 6.02 5.18 4.85 4.92

SP-13 1.21 3.00 1.19 1.06 1.19


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Fig. 1.Location of the studied site and sectional view of the deltaic deposits of the Nakdong

River (Modified after Kim (2008)): (a) Korean peninsula, (b) location of the studied site, and (c)

sectional view of soil layers of the studied site.

Fig. 2.Variation of soil properties on physical characteristics and compressibility with elevation.

Fig. 3.Graphical settlement prediction methods: (a) hyperbolic method and (b) Asaoka method.

Fig. 4. Flowchart of GA procedure.

Fig. 5.Variation of back-analyzed consolidation parameter through the optimization process at

SP-6: (a) compression index and (b) horizontal coefficient of consolidation.

Fig. 6.Variation of back-analyzed consolidation parameter by elapsed time at SP-6: (a)

compression index and (b) horizontal coefficient of consolidation.

Fig. 7.Settlement curves by consolidation theory, measurement, and back analysis at SP-3.

Fig. 8.Settlement prediction by the thickness of consolidation layer.

Fig. 9.Settlement curve by the GA back-analysis method in the case of modifying a surcharging

plan at SP-12: (a) original plan and measurement and (b) modified plan and measurement.


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Fig. 1


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Fig. 2


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Fig. 3


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.

Fig. 4

Generate initial population

Calculate the objective function

Calculate the fitness value

Selection

Reproduction & Mutation

Calculate the objective function

Calculate the fitness value

GEN = 0

GEN < GENmax

Best individual

GEN=GEN+1

Yes

No


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Fig. 5


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Fig. 6


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Fig. 7


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Fig. 8


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Fig. 9

Documents

Field Performance of a Genetic Algorithm in the Settlement Prediction of a Thick Soft Clay Deposit in the Southern Part of the Korean Peninsula 2015 E