DYNAMIC ANALYSIS OF SOIL STRUCTURE INTERACTION … · moment opposing building frames resisting on...

http://www.iaeme.com/IJCIET/index.asp 496 [email protected]

International Journal of Civil Engineering and Technology (IJCIET)

Volume 9, Issue 11, November 2018, pp. 496–512, Article ID: IJCIET_09_11_049

Available online at http://www.iaeme.com/ijciet/issues.asp?JType=IJCIET&VType=9&IType=11

ISSN Print: 0976-6308 and ISSN Online: 0976-6316

©IAEME Publication Scopus Indexed

DYNAMIC ANALYSIS OF SOIL STRUCTURE

INTERACTION (SSI) USING ANFIS MODEL

WITH OBA MACHINE LEARNING APPROACH

Ponnala Ramaiah

Department of Civil Engineering

Koneru Lakshmaiah Education, Deemed University

Green fields, Guntur, Vaddeshwaram. Andhra Pradesh, India

Dr. Sanjeet Kumar

Associate Professor, Department of Civil Engineering

Koneru Lakshmaiah Education, Deemed University

Green fields, Guntur, Vaddeshwaram, Andhra Pradesh, India

ABSTRACT

One of the real difficulties for structural engineers is design and construction of

structures with satisfactory performance under dynamic loading conditions actuated

by strong wind or seismic tremors. SSI is a major problem in the construction process,

which may alter the dynamic characteristics of the structural response altogether. The

SSI system has two characteristic differences from the general structural dynamic

system which are the unbounded nature as well as the non-direct characteristics of the

soil medium. This study considering the SSI impacts in dynamic impacts of concrete

moment opposing building frames resisting on Soil Pile Structure (SPS) is additionally

anticipated. In SSI modeling, for diminishing the complexity and enhance the

prediction accuracy, Adaptive Neuro Fuzzy Inference System (ANFIS) model with

Opposition Based BAT Algorithm (OBAT) is proposed. It is demonstrated that the

proposed model can foresee the dynamic response of the soil-structure system with

great accuracy in much less time contrasted and the current strategies.

Key words: Soil structure interaction, dynamic characteristics, dynamic response,

ANFIS and OBAT.

Cite this Article: Ponnala Ramaiah, Dr. Sanjeet Kumar, Dynamic Analysis of Soil

Structure Interaction (SSI) Using Anfis Model with OBA Machine Learning

Approach, International Journal of Civil Engineering and Technology (IJCIET) 9(11),

2018, pp. 496–512.

http://www.iaeme.com/IJCIET/issues.asp?JType=IJCIET&VType=9&IType=11

1. INTRODUCTION

The apartment building arises in numerous urban areas and based on limit locales, where the

structures influence each other through the soil under earthquake excitation [1]. The event of a

Ponnala Ramaiah, Dr. Sanjeet Kumar


vibrating structure impacting the response of the soil and the ground movement influencing

the response of the structure is alluded to as dynamic SSI [2]. The subsurface conditions at a

site can large affect the level of vibrations from a given source [3]. The seismic design of the

structure and the piled pontoon is performed by figuring base shear of the structure under

fixed base condition [4]. To investigate the impacts of SSI on the response of three-

dimensional steel with two distinctive sidelong opposing systems subjected to ground

movements [5]. The SSI examination is to adjust the response of the structures [6]. The

impact of SSI is taken into for structural response investigation and the damping system of

SSI has attained expanding consideration [7]. The vulnerability in soil properties along with

SSI and probabilistic appraisal is required with regards to current design worldview [8]. The

dynamic connection between melting soil and a structure under seismic excitation has been

dissected by this technique [9]. Soil practices under seismic tremor indicate evident nonlinear

properties and a geometric nonlinearity caused by huge strain twisting [10].

The viscous damping proportion is a straightforward scientific portrayal of the energy

scattered by all the damping systems in a structure [11]. The soil– structure system has a

higher damping proportion, because of radiation and material damping in the soil, which can

radically impact the response of the system [12]. The damping acquired from soil-structure

communication for versatile structures on inflexible establishments to high rigid system [13].

It is basic to anticipate a proportional damping model indicating the consolidated impacts of

flexible and hysteretic damping for the pile-bolstered wharf [14]. It was obtained that SSI

resulted in a period lengthening of structures and expansion of their damping proportions,

with these impacts being more featured in taller structures and on gentler soils [15]. The

extension that is based on the part damping proportions including the equal viscous damping

proportions of confinement orientation and are intended to stay flexible amid a noteworthy

tremor [16]. The interaction impacts will be measured by contrasting the most extreme

response of the gathering structures with the greatest response of a single structure on the

ground [17]. This clarifies a three-organize procedure of preparing, testing, and cross

validation to avoid over fitting, number of neurons have been utilized as a part of the vast

majority of the other research works inside a single hidden layer network.

2. LITERATURE REVIEW

In 2015 HojjatAbbasiFarfanet al [18] had proposed the scientific model for seismic

investigation of Soil-Pile-Structure (SPS) systems were worked in the neural systems based

on the current experimental data. A system comprising of two hidden layers was proved to be

the most effective among different decisions. Three sets of information are used for training,

testing, and approval of the ANN model to maintain a strategic distance from over fitting by

cross-approval. The accuracy of the neural systems to suspect the seismic behavior was

enhanced by the parallel vectorial examination strategy for the vector machines. This model

can foresee the dynamic characteristics. Thusly, more research ought to be composed toward

conveying refined data for the DBM examination of soil-pile-structure issues.

Had shown the dynamic interaction of pile establishments, implanted in an on a level

plane stratified soil profile, with superstructures under low to direct seismic tremor excitation

can be taken care of in various ways Mohammad MuazAldimashkiet al 2014 [19]. In this

article, the soil-pile-superstructure dynamic interaction issue has been researched utilizing the

coupled limited component boundary element strategy. A parametric investigation of the

proposed model has yielded critical outcomes basically concerning the intensification

variables of the pile establishment and the superstructure. In addition, the choice of the

damping ratio of soil when performing dynamic soil-pile-structure interaction investigation

ought to be done in this strategy.

Dynamic Analysis of Soil Structure Interaction (SSI) Using Anfis Model with OBA Machine

Learning Approach


Had analyzed the pile-head movement was commanded by two successive frequencies: a

lower frequency (SSI) where pile head movement was generously amplified and a higher one

(fps) where the response was minimized regarding free field surface movement Mahmoud N.

Hussienet al 2016 [20]. The results showed that strong mobilized kinematic collaboration

impact producing critical pile bending when the ground was energized at its resonant

frequency. Dissemination of pile bending minutes in the gathering was observed to be an

element of the pile position and the excitation frequency. The relative commitments of

kinematic and inertial connection to the seismic powers prompted in pile establishments

might be distinctive to those seen in the present rotator tests.

In 2016 PallaviBadryet al [21] had proposed the Seismic studies and investigations led on

methods of disappointment of structures during past quakes observed that the unbalanced

structures demonstrate the most powerless impact over the span of disappointments. Hence,

all unbalanced structures essentially failduring the shaking occasions and it was extremely

expected to concentrate on the precise investigation of the building, incorporating all

conceivable accuracy in the analysis. Aside from superstructure geometry, the soil behavior at

the time of seismic tremor shaking assumes a critical part in the building collapse. This can be

very much clarified in the SSI. The outcome demonstrated that increasingly the mind

boggling building indicates high hazard during a seismic tremor occasion and responses of the

building administers by the pinnacle ground speeding up of the specific quake instead of its

extent.

Had analyzed the computationally productive demonstrating methodology of including

the dynamic SSI into air flexible codes is given an emphasis on monopile establishments M.

Damgaardet al 2014 [22]. The Semi-logical frequency area solutions were connected to assess

the dynamic impedance elements of the soil– pile system at various discrete frequencies. The

air versatile response was assessed for three distinctive establishment conditions, i.e. obvious

fixity length, the steady lumped-parameter demonstrate and fixed support at the seabed. This,

thusly, makes the accessible methodology of soil– pile communication exceptionally

appealing and may profitably be utilized for other pile establishments.

In 2013 V. Jaya et al [23] had proposed the seismic SSI examination of a ventilation stack

situated in an atomic power plant site. In a seismic soil-structure association examination, it

was important to think about the infinite extent and layered nature of the soil also the

nonlinear behavior of soils. The nonlinear soil behavior was demonstrated by utilizing

webpage particular modulus lessening and damping proportion bends. It was discovered that

the seismic response at the different levels of the stack demonstrates a solid reliance on the

relative stiffness of site and the profundity of the soil layer to bedrock. As a result of bigger

implant proportion outcomes was the lesser response for the tall thin structures.

Had shown the dynamic SSI impacts in seismic investigation and design of structures

laying on soft soil stores was a standout amongst the most discussed and testing issues in the

field of seismic design and requalification of various structures Harry Far et al 2017 [24]. In

this investigation, a far reaching basic survey has been done on accessible and surely

understood modeling procedures and calculation strategies for dynamic SSI examination.

Contrasting the benefits and impediments of utilizing every strategy, in this investigation, the

most exact and solid demonstrating system, and additionally calculation technique, have been

distinguished and proposed to be utilized in concentrate dynamic SSI analysis of structures

laying on soft soil deposits.

In 2017 Ali RuziOzuygur et al 2017 [25] had investigated the emphasis based numerical

algorithm was proposed to deal with ideally controlled SSI systems under earthquakes. To

start with, the ideal control powers were gotten by utilizing a fixed base system. The parallel



uprooting and the shaking of the establishment were acquired from the conditions of the SSI

system containing the ideal control powers in the recurrence area. The horizontal dislodging

and shaking of the establishment were changed over to the time space by the backwards

Fourier change system. The result acquired in the time area was utilized as a part of the

conditions of the soil-structure system from which the behavior of foundation as well as

structure was attained.

3. PROBLEM IDENTIFICATION

There are several issues that threaten the dynamic analysis of SSI. Some of them are as

follows:

Many endeavors have been made to demonstrate the SSI issue numerically, yet

have been discovered that the soil nonlinearity, as well as foundation interfaces,

application of boundary component makes more difficult and computationally

costlier.

The ANN for data based models is more unpredictable than what was figured for

the ANN models of the peak acceleration. This reality implies by one means or

another for the irregularity of the database of the period lengthening [18].

Although the FEM analysis does not represent the development of pore pressure

because of cyclic/dynamic stacking. The friction at the soil-pile interface is

dismissed. At each time step/cycle, just partition and debonding of pile along with

soil was considered.

In the current papers, some optimization algorithms (ANN, SVM, and so forth.)

are utilized to foresee the dynamic characterization and dynamic responses of SPS

system. The procedures won't function admirably or make it more complex to

overcome these issues paid the best approach to proposed technique [20-23].

4. METHODOLOGY

This methodology aims to develop the models for foreseeing both dynamic characteristics and

dynamic responses of SSI issues. The dataset is collected from the existing literature; the

dataset incorporates SSI consequences for 57 structures under various seismic tremors. This

investigation planned to recommend the approach for lessening the complexity nature in SSI

modeling and diminishing the analysis time by implementing the ANFIS for predicting the

dynamic responses of SPS system. To enhance the structure performance, biased weight is

optimized by sing inspired optimization algorithm i.e. OBAT. With the assistance of the

proposed model, dynamic characteristics of structures including their basic period and

damping proportion incorporating SSI are assessed in addition to the dynamic responses.

4.1. Influence of SSI in Soil Structure

In general, SSI will impact the soil-structure system in three ways

It will modify the dynamic characteristics of the soil-structure system, as

vibrational response and modular frequencies. In particular, the fundamental

period will stretch and the rigid body movement of the structure will be altered.

It will raise the modular damping as a component of the soil will add to the general

damping of the soil-structure system.

It will alter the free field ground movement.

A large measure of experimental estimations and data recordings of genuine occasions on

SSI systems is controlled. At that point, the dynamic characteristics of structures including


Learning Approach


their fundamental period and damping ratio incorporating SSI are evaluated along with the

dynamic responses. Degree of progress of dynamic properties demonstrates that how essential

SSI is in a particular building. The dynamic characteristics of the soil-structure system, as

vibrational response and modular frequencies are dissected by executing the ANFIS model

and furthermore it decreases the complexity in SSI modeling.

ANFIS is a sort of ANN that is based on Takagi– Sugeno fuzzy inference system. Since it

consolidates both neural systems and fuzzy logic standards, it can possibly catch the

advantages of both in a single structure. ANFIS incorporates: Neural Network (NN) with

Fuzzy Inference System (FIS).

4.1.1. Neural Network (NN)

NN are normally structured in three layers which are comprised of various interconnected

nodes contain an 'activation function'. Every neuron applies an activation function to its net

contribution to decide its output signal. The NN has three layers such as Input layer, Hidden

layer and Output layer and the structure is shown in figure 1.

Figure 1 Basic structure of Neural Network

Input Layer:This layer is in charge of getting data (information), signs, highlights, or

estimations from the external condition.

Hidden Layer: The Hidden layer of the neural system is the middle layer between Input and

Output layer. The weights in the hidden node need to test using training data.

Output Layer: The nodes in this layer are dynamic ones. This layer results from the

processing performed by the neurons in the past layers.

4.1.2. Fuzzy Inference System (FIS)

A fuzzy neural network or Neuro-fuzzy system is a learning machine that finds the parameters

of a fuzzy system (i.e., fuzzy sets, fuzzy rules) by exploiting approximation techniques from

neural networks. FIS is the process of formulating an input fuzzy set map to an output fuzzy

set using fuzzy logic. It is comprised of three stages that process the system inputs to the

appropriate system outputs. These steps are fuzzifier, inference engine and defuzzifier and it

is depicted in figure 2.



Figure 2 Structure of Fuzzy Inference System

Fuzzifier: It translates the crisp input into a linguistic variable using the membership function

stored in the fuzzy knowledge base.

Inference Engine: Using IF-THEN rules and membership functions, it converts the fuzzy

input into fuzzy output.

Knowledge base:It combines both the rule base and database.Rule base containing a number

of fuzzy IF–THEN rules; database which defines the membership functions of the fuzzy sets

used in the fuzzy rules.

Defuzzifier: It translates the fuzzy output into crisp value using the membership function

which is analogous to one used by the fuzzifier.

4.2. ANFIS

ANFIS is a class of adaptive NN and that are practically proportional to FIS. ANFIS control

is a hybrid strategy comprises of two sections which are gradient method connected to

evaluate input membership function parameters, and least square method is applied to

calculate the parameters of output function. The structure of ANFIS is appeared in figure 3.

Figure 3 Proposed ANFIS Structure

4.2.1. Structural Initialization

For determining the dynamic characteristics and dynamic responses of the soil structure

interaction, ANFIS structure initialize five input parameters as: peak base accelerator,

amplitude factor, mass, length and N-pile and analyze the responses like Pile Head

Inference

Engine

Knowledge Base

Rule Base

Database

Fuzzifier

Defuzzifier

Crisp

Input

Crisp

Output

B1Hi

B1Hi

B1Hi

Bn

B2

B1

L

M

H

π N

Π

L

M

H

π

Π

N

Π

L

M

H

π

Π

N

Π

∑

Inputs

I2

I5

Layer 1

Layer 2 Layer 3

Layer 4

Layer 5

B1

B2

Bn

I1

I1…..I

5

I1…..I5

I1…..I

5

∑

∑

Outputs


Learning Approach


Acceleration (PHA), Period Lengthening (PL) and Super Structure Acceleration (SSA) based

on the input parameters [26]. Initialize the input parameter as

54321 ,,, IandIIIII (1)

The initialized parameters description are: racceleratobasepeakI 1 ,

factorAmplitudeI 2 ,MassI 3 , LengthI 4 ,

pileNI 5 .

For a typical first-order Takagi-Sugeno fuzzy model, a common rule set, with fuzzy if–

then rules, is presented.

4.2.2. Layer Operation of ANFIS

ANFIS is a Multi-layer network. In ANFIS model, there are five layers used that are

described as follows. Out of five layers, the first as well as the fourth layers acquire adaptive

nodes while the second, third plus fifth layers acquire fixed nodes.

Layer 1-Input Nodes: Every node in this layer is adaptive one and the node produces

membership grades of the crisp inputs. The assigned crisp inputs are peak base accelerator,

amplitude factor, mass, length and N-pile. The fuzzy membership grade of the each crisp

input is evaluated by the following equation.

)( 1

1IMG ii

, Where ,...2,1i (2)

Similarly, fuzzy membership grade for other crisp inputs ( 5432 ,, IandIII) are evaluated.

For example, the created bell-shaped membership function is given by

i

i

i

i

ts

ux

IM

21

1

1)(

… (3)

Where I is the input to node ,...2,1i , iM is the linguistic variable connected with this

node function iM is the membership function of iM

, and is, it and iu

are the premise

parameter set.

Layer 2- Rule nodes: The second layer nodes are called as fixed nodes. This layer includes

fuzzy operators; it utilizes the AND administrator to fuzzify the sources of input. They are

marked with , showing that they execute as a simple multiplier. The output is called as

firing strengths of the rules.

Rule Generation

If peak base acceleration is high, amplitude factor is low, mass is medium, length

is high and number of N-pile is low then the PHA is low, PL is high and SSA is

low.

If peak base acceleration is low, amplitude factor is low, mass is low, length is

medium and number of N-pile is low then the PHA is low, PL is low and SSA is

low.

If peak base acceleration is high, amplitude factor is high, mass is low, length is

high and number of N-pile is low then the PHA is high, PL is low and SSA is

medium.

If peak base acceleration is low, amplitude factor is low, mass is high, length is

high and number of N-pile is low then the PHA is low, PL is high and SSA is high.



Similarly, number of rules is generated based on the input parameters. Foreach rule, its

weight is calculated as the product of the input membership values as:

)(*)(*)(*)(*)( 54321

2 IQIPIOINIMBG iiiiiii , i=1, 2... .n (4)

Where Bi indicates weight of the structure and i = 1, 2 ,... are the rule fulfillment weights.

Layer 3-Average nodes: Average nodes are called as fixed nodes. These nodes are labeled

by N, to indicate that they play a normalization role to the firing strengths from the previous

layer. The output of this layer is called as normalized firing strengths and it can be represented

as

,..2,1,3

iB

BBG

i

i

iii

(5)

The normalized firing strengths (weight) of the structure are trained through the Neural

Network, to adjust the input parameters and to minimize the errors.

4.3. ANFIS Structure Optimization

In order to find the optimum value, the ANFIS structure bias and weights are optimized by

the inspired Opposition based BAT Algorithm (OBAT).

4.3.1. Objective Function for the Proposed Work

The objective function can be calculated based on the fitness function. The fitness function is

evaluated as the least Mean Square Error (MSE) rate. It can be defined as

)(MSEoptimalFi (8)

4.3.2. Minimum Error Rate

The distinction of the MSE between observed and predicted values was processed for every

trial with various epoch numbers, and the best structure was dictated by the most minimal

estimation of the MSE. MSE is the capacity to minimize the errors and it is characterized as.

j

jj zzN

MSE 2)ˆ(1

(9)

Where N is the total number of prediction, jzand jz

are the original and predicted time

series respectively. The performances of the ANFIS models of both training as well as

checking data were calculated according to MSE. The most minimum error value is evaluated

by the proposed OBAT algorithm.

4.4. BAT Algorithm (BAT)

In nature, bats are entrancing creatures. Microbats utilize a kind of sonar, called echolocation,

to distinguish prey, maintain a strategic distance from impediments, and find their perching

fissure oblivious. By admiring a portion of the echolocation characteristics of microbats, BAT

is proposed. Initialize the bat population with velocity jV at position jS

emitting a fixed

frequency minBf , changing wavelength λ, and loudness jL to search for prey [27].

Initialization: The populace is produced arbitrarily for n number of bats. Every person of

the populace comprises of genuine valued vectors with d measurements. The accompanying

condition is utilized to create the initial populace. Initialized the bat populace as

))(1,0( 000,0 kkk lbublbkj EErandEE (6)


Learning Approach


Where nj ,..2,1 , dk ,..2,1 , kubE0 and klbE0 are upper and lower boundaries for

dimension k respectively.

4.4.1. Opposition Process

Optimizations algorithm begins with some initial solutions also attempt to enhance them by

simultaneously checking the opposite solution. By contrasting both the solution, the fittest

solution can be selected as an initial solution. Let ),( yxa is a real number. By applying the

opposite point definition, it can be written as

jjjj ayxa ~ (7)

4.4.2. Proposed Opposition based BAT Algorithm (OBAT)

The ordinary BAT is picked as a parent algorithm and opposition based thoughts are

implanted in it. The proposed technique particularly due to opposition idea incorporated keeps

great balance between global search stage and fine tuning stage at the time of new generations

and in the meantime shows accelerated convergence profile. The graphical representation of

OBAT algorithm is shown in figure 4.

4.4.3. New Solution Updation Process

Movement of virtual bats: In simulation analysis, virtual bats are used. The new solutions t

jS

and velocities t

jVat time step t are given by

jj BfBfBfBf *)( maxmaxmin (10)

ji

t

jj BfsSVV *)( 0

1

(11) t

j

t

j

t

j VSS 1

(12)

Where j is a random vector. From the equation 0s

is the present global best solution.

Once a solution is chosen among the present best solutions, a new solution for each bat is

generated locally using random walk is given by t

oldnew LSS * (13)

Where ]1,1[ is a random number and the

t

j

t LLis the average loudness of all

the bats. newSRepresents new solution and oldS

represents old solution.



Figure 4 Flowchart of OBAT

Loudness and pulse rate: Moreover, the loudness jL and the pulse emission rate jP

have

to be updated consequently as the number of iterations proceed. As the loudness decreases

once a bat has found its prey, at the same time the rate of pulse emission increases. Now the

updation process of jL and jP

is given by, t

jj LL * (14)

]1[ *01 t

j

t

j ePP

(15)

, are the constants and the ranges are 10 and 0

Their loudness along with its emission rates will be updated only if the new solutions are

improved, which means that these bats are moving towards the optimal solution.

4.4.4. Pseudo Code of the Proposed OBAT

Corresponding pseudo code for the proposed OBAT approach is summarized as follows:

Yes

No

Yes

Random

number >Pi

Replace the temporary local bat

Update the solution, increase Pi and loudness Ri

Rank the best bat, Pi, Ri

Evaluate the fitness of new

temporary bat

Generate a local bat around the best bat

Fitness

better than

the old bat

End

Yes

No

Opposition process

Generate opposition

process

Calculate the fitness for

opposite solution

Best Solution

Initialize random bat

population

Calculate the fitness

and find the best bat

No

No Is condition

satisfied?


Learning Approach


Thus the optimized structure weight is attained with the help of BAT algorithm

Layer 4-Consequent nodes: The fourth layer nodes are called as adaptive nodes. The

output of every node is calculated as the product of the normalized firing strength as well as a

first-order polynomial. Here, the contribution of thi

rules towards the total output or the

modeled output is designed as takes after:

iii HBG 3

(16)

Where, iB is the output of Layer 3 and i5i4i3i2i1ii f)(Ie+)(Id+)(Ic+)(Ib+)(Ia=H

and iiiiii fedcba ,,,,, are referred to as consequent parameters,

thiindicates number of rules.

Layer 5-Output Nodes: This layer has single fixed node which evaluates the final output

as the summation of all incoming signals.

i

iii HBG5

(17)

From the output layer, the dynamic characteristics and dynamic responses of SSI is

predicted with the use of output parameters like PHA, PL and SSA. Also, its simulation

analysis results are discussed in the below section.

Step 1: Initialize the bat population kjE ,0

Step 2: Evaluate pulse frequency Bf at each population, pulse emission rate Pi and the loudness Li

Step 3: Opposition based initialization kjkkkj ElbubE ,0,0

~

Step 4: Compare the set }~

,{ ,0,0 kjkj EE and select fittest individual as initial bat population

Step 5: while (t < Max number of iterations)

Generate new solutions by adjusting frequency, and updating velocities and locations.

If (random number (0, 1) < Pulse rate Ri)

Select a solution among the best solutions and generate a local solution around the selected best

solution

Step 6: end if

Step 7: Generate a new solution by flying randomly

Step 8: if (random number 2(0, 1) < loudness Li and 0)( FSF j )

Accept the new solutions and increase pulse rate Pi and reduce Loudness Li.

Step 9: end if

Step 10: Opposition-based generation jumping,

If (random number3 (0, 1) < Jr)

kj

Gn

k

Gn

kkj EOE ,. maxmin // where Gn

k

Gn

k max,min represents minimum and maximum value

of thk variable in the present generation (Gn)

Select the fittest bat from the set of },{ ,, kjkj OEE as current bat population.

End if

Step 11: Rank the bats and find the current best solution 0s

Step 12: end while



5. RESULT AND DISCUSSION

This segment discusses dynamic characteristics and dynamic responses of Soil Structure

Interaction. This simulation procedure is implemented by MATLAB 2015 a with 4GB RAM

and i5 processor. Validation of numerical results for the dynamic investigation of SSI was

performed with the experimental data. The data incorporates the SSI consequences for 57

structures, having different structural systems and being on various locales, under various

quakes [18]. The validation tests between the anticipated outcomes and the real outcomes for

the quantity of testing data are displayed.

Table 1 Experimental Data

Input Output

Peak Base

Accelerator

Amplitude

Factor Mass Length

N-

pile SSA PHA PL

0.01 0.2 192.8 7.7 4 0.04 0.04 1.12

0.04 0.5 45.1 2.1 1 0.12 0.06 1.1

0.08 0.9 192.8 7.7 4 0.14 0.12 1.19

0.15 2 45.1 2.1 1 0.84 0.34 1.04

0.26 3.5 90.2 0.5 1 0.93 0.76 1.1

0.32 4.5 90.2 0.5 1 1.02 0.88 1.05

0.47 5.75 192.8 7.7 4 0.67 0.47 1.18

Table 1 describes the experimental data which is observed from [18]. Based on the peak

base accelerator, amplitude factor, mass, length and number of pile structure, the dynamic

characteristics are analyzed and it is depicted in the table.

Table 2 Simulation Results (Predicted values)

PHA SSA PL

Experime

ntal

ANFIS+

OBAT ANFIS BAT Experi

mental

ANFIS

+OBA

T

ANFI

S BAT

Experi

menta

l data

ANFI

S+OB

AT

ANFI

S BAT

0.76 0.79 1.31 0.87 0.93 0.94 1.06 1.35 1.1 1.08 1.41 1.33

0.88 0.85 1.06 1.02 1.02 0.98 1.25 1.80 1.05 1.08 1.69 1.58

0.18 0.20 0.62 0.87 0.09 0.17 0.74 0.79 1.27 0.78 1.89 1.33

0.13 0.18 0.58 0.86 0.26 0.38 0.58 0.89 0.26 0.66 0.92 1.02

0.47 0.68 0.89 0.96 0.67 0.78 0.89 0.96 0.67 0.88 1.12 1.08

Table 3 Error Rate Analysis

PHA SSA PL

ANFIS+OB

AT ANFIS BAT

ANFIS+OB

AT ANFIS BAT

ANFIS+O

BAT ANFIS BAT

0.03 0.55 0.11 0.01 0.16 0.42 0.02 0.3 0.23

0.03 0.18 0.14 0.04 0.23 0.78 0.03 0.64 0.53

0.02 0.44 0.69 0.08 0.65 0.70 0.49 0.62 0.56

0.05 0.45 0.73 0.12 0.32 0.63 0.4 0.74 0.1

0.24 0.42 0.51 0.11 0.22 0.29 0.21 0.45 0.04

Table 2 and 3 clearly depicts the dynamic characteristics (PHA, SSA and PL) and its error

rate is described and compared with BAT, ANFIS and ANFIS+OBAT techniques. For

different number of testing data the experimental and predicted values are determined and


Learning Approach


compare with existing methods. From the analysis, the proposed ANFIS+OBAT attain the

finest solution.

Dynamic Characteristic Analysis of SSI

Figure 5 Pile Head Acceleration and its Error Rate

Figure 5 (a) and (b) shows the analysis of pile head acceleration in the soil structure

interaction and its error rate. The graph clearly depicts the comparative analysis of actual and

the predicted values like BAT, ANFIS and ANFIS+OBAT. For the testing data 1, the PHA

results are 0.79 in the actual, and then it decreases gradually, and reaches 0.2 in the proposed

ANFIS and OBAT method. In the error rate analysis the proposed ANFIS+OBAT achieves

minimum error value compared to anfis and bat.

Figure 6 describes the period lengthening and its error rate analysis for different number

of testing data. The graph concludes that the minimum error rate is achieved for the testing

data 1 and 2 in the proposed method.

Figure 6 Period Lengthening and its Error Rate



Figure 7 Super Structure Acceleration and its Error Rate

One of the characteristics of SSI is Super Structure Acceleration (SSA) which is analyzed

with the number of testing data and it is depicted in the figure 7. The line graph shows the

actual and predicted values of the SSA and (b) represents the error rate analysis. From the

graph, the proposed ANFIS+OBAT accomplish better results compared to ANFIS and BAT.

Comparative Analysis

Figure 8 Error Rate Analysis

Figure 8 shows the error rate analysis of different algorithm in the dynamic analysis of

SSI. The minimum error rate is achieved by optimizing the structure weight of ANFIS by the

inspired OBAT algorithm. The least error rate is attained in the ANFIS+OBAT method.

Figure 9 Accuracy Analysis


Learning Approach


Figure 9 represents the accuracy analysis of different techniques like ANFIS+OBAT,

ANFIS, BAT, ANN and SVM in the dynamic analysis of SSI. The comparison graph

concludes that the proposed ANFIS+OBAT results in better performance.

6. CONCLUSIONS

This paper evaluates the effects of SSI on damping ratios of buildings subjected to earthquake

ground motions and to reduce the vibrations of structure due to seismic waves. With the help

of larger amount of SSI experimental measurements and data recordings of real events,

predict the dynamic characteristics and dynamic responses of SPS system. For reducing the

complexity in SSI modeling and reducing the analysis time, the ANFIS model was

implemented. Furthermore, weight of the ANFIS structure was optimized by the Opposition

based BAT algorithm. Finally, the results of the presented study showed that, the ANFIS-

based OBAT optimization techniques could solve the complex problem of seismic response

of structures. Also, it achieved much less computational time with more accuracy compared to

FEM, SVM and ANN [18].

FUTURE SCOPE

In future investigations, complex structural models with a detailed SSI might be considered,

however the computation time is an imperative for the application. All things considered,

hybrid strategies consolidating a few algorithms or new variations of the algorithms might be

produced.

Applications

The investigation of SSI has an essential part in the deep-seated constructions,

structures supported over soft soil, tall or slim structures.

Design of flexible retaining walls is pertinent based on the activated earth pressure

and soil protection.

It is utilized as a part of Heavy structures like water powered structures and atomic

structures.

REFERENCES

[1] Qi Ge, FengXiong, Jing Zhang, and Jiang Chen, "Shaking table test of dynamic

interaction of soil–high-rise buildings ", European Journal of Environmental and Civil

Engineering, Vol. 21, no. 3, pp.249-271, 2017.

[2] A.Amorosi, D. Boldini, and A. DiLernia, "Dynamic soil-structure interaction: A three-

dimensional numerical approach and its application to the Lotung case study ", Journal of

Computers and Geotechnics, Vol. 90, pp.34-54, 2017.

[3] J. C.Cleary, and E. J. Steward, "Analysis of ground vibrations induced by pile driving and

a comparison of vibration prediction methods ", DFI Journal-The Journal of the Deep

Foundations Institute, Vol. 10, no. 3, pp.125-134, 2016.

[4] RajibSaha, Sekhar Chandra Dutta, and SumantaHaldar, "Seismic Response of soil-pile

raft-structure system", Journal of civil engineering and management, Vol. 21, no.

2,pp.144-164, 2015.

[5] Cristian Cruz, and Eduardo Miranda, "Evaluation of soil-structure interaction effects on

the damping ratios of buildings subjected to earthquakes", Journal of Soil Dynamics and

Earthquake Engineering, Vol. 100, pp. 183-195, 2017.



[6] Zhiying Zhang, Hongyang Wei, and Xin Qin, "Experimental study on damping

characteristics of soil-structure interaction system based on shaking table test", Journal of

Soil Dynamics and Earthquake Engineering, Vol. 98, pp.183-190, 2017.

[7] PouriaAyough, and Seyyed Ali Haj SeyyedTaghia, "Response of steel moment and braced

frames subjected to near-source pulse-like ground motions by including soil-structure

interaction effects", Journal of Soil Dynamics and Earthquake Engineering, Vol. 101, pp.

53-66, 2017.

[8] Biplab Das, RajibSaha, and SumantaHaldar, "Effect of in-situ variability of soil on

seismic design of piled raft supported structure incorporating dynamic soil-structure-

interaction", Journalof Soil Dynamics and Earthquake Engineering, Vol. 84, pp.251-268,

2016.

[9] Denghong Chen, and Shangqiu Dai, "Dynamic fracture analysis of the soil-structure

interaction system using the scaled boundary finite element method", Journal of

Engineering Analysis with Boundary Elements, Vol. 77, pp. 26-35, 2017.

[10] ChuanLuo, Xun Yang, Changbao Zhan, Xianlong Jin, and Zhenkun Ding, "Nonlinear 3D

finite element analysis of soil–pile–structure interaction system subjected to horizontal

earthquake excitation", Journal of Soil Dynamics and Earthquake Engineering, Vol. 84,

pp.145-156, 2016.

[11] Cristian Cruz, and Eduardo Miranda, "Evaluation of soil-structure interaction effects on

the damping ratios of buildings subjected to earthquakes", Journal of Soil Dynamics and

Earthquake Engineering, Vol.100, pp.183-195, 2017.

[12] FaramarzKhoshnoudian, EhsanAhmadi, and Ali Imani Azad, "Damping coefficients for

soil–structure systems and evaluation of FEMA 440 subjected to pulse-like near-fault

earthquakes", Soil Dynamics and Earthquake Engineering, Vol. 61, pp.24-134, 2014.

[13] M. Novak, and L. El Hifnawy, "Effect of soil‐structure interaction on damping of

structures ", Journal of Earthquake engineering & structural dynamics, Vol. 11, no. 5, pp.

595-621, 2012.

[14] ShufeiGao, Jinxin Gong, and YunfenFeng, "Equivalent damping for the displacement-

based seismic design of pile-supported wharves with soil–pile interaction", Journal of

Ocean Engineering, Vol. 125, pp.12-25, 2016.

[15] B. Madani, F. Behnamfar, and H. TajmirRiahi, "Dynamic response of structures subjected

to pounding and structure–soil–structure interaction ", Journal of Soil Dynamics and

Earthquake Engineering, Vol. 78, pp. 46-60, 2015.

[16] C. C.Spyrakos, and A. G. Vlassis, "Effect of soil-structure interaction on seismically

isolated bridges", Journal of Earthquake Engineering 6, no. 03, pp.391-429, 2002.

[17] Guillermo M.Álamo, Luis A. Padrón, Juan J. Aznárez, and Orlando Maeso, "Structure-

soil-structure interaction effects on the dynamic response of piled structures under

obliquely incident seismic shear waves ", Journal of Soil Dynamics and Earthquake

Engineering, Vol. 78, pp.142-153, 2015.

[18] HojjatAbbasiFarfan, FarhadBehnamfar, and Ali Fathollahi, "Dynamic analysis of soil-

structure interaction using the neural networks and the support vector machines ", Journal

of Expert Systems with Applications, Vol. 42, no. 22, pp. 8971-8981, 2015.

[19] Mohammad MuazAldimashki, James Mark William Brownjohn, and Subhamoy

Bhattacharya, "Experimental and analytical study of seismic soil-pile-structure interaction

in layered soil half-space ", Journal of Earthquake Engineering, Vol. 18, no. 5, pp.655-

673, 2015.


Learning Approach


[20] Mahmoud N.Hussien, Tetsuo Tobita, Susumu Iai, and MouradKarray, "Soil-pile-structure

kinematic and inertial interaction observed in geotechnical centrifuge experiments",

Journal of Soil Dynamics and Earthquake Engineering, Vol. 89, pp. 75-84, 2016.

[21] PallaviBadry, and Neelima Satyam, "Seismic soil structure interaction analysis for

asymmetrical buildings supported on a piled raft for the 2015 Nepal earthquake", Journal

of Asian Earth Sciences, Vol. 133, pp. 102-113, 2016.

[22] M. Damgaard, VarvaraZania, Lars Vabbersgaard Andersen, and Lars Bo Ibsen, "Effects

of soil–structure interaction on real time dynamic response of offshore wind turbines on

monopiles", Journal of Engineering Structures, Vol. 75, pp. 388-401, 2014.

[23] V. Jaya, G. Dodagoudar, and A. Boominathan, "Seismic soil-structure interaction analysis

of tall slender structures", International Journal of Geotechnical Engineering, Vol. 2, no.

4, pp.381-393, 2008.

[24] Harry Far, "Advanced computation methods for soil-structure interaction analysis of

structures resting on soft soils", International Journal of Geotechnical Engineering, pp. 1-

8, 2017.

[25] Anna Karatzetzou, and DimitrisPitilakis, "Modification of Dynamic Foundation Response

Due to Soil-Structure Interaction", Journal of Earthquake Engineering, pp.1-20, 2017.

[26] Ahmed Al-Hmouz, Jun Shen, Rami Al-Hmouz, and Jun Yan, "Modeling and simulation

of an adaptive neuro-fuzzy inference system (ANFIS) for mobile learning", IEEE

Transactions on Learning Technologies, Vol. 5, no. 3, pp. 226-237, 2012.

[27] Gopi Ram, DurbadalMandal, RajibKar, and Sakti Prasad Ghoshal, "Opposition‐based

BAT algorithm for optimal design of circular and concentric circular arrays with improved

far‐field radiation characteristics", International Journal of Numerical Modelling:

Electronic Networks, Devices and Fields, Vol. 30, pp. 3-4, 2017.

DYNAMIC ANALYSIS OF SOIL STRUCTURE INTERACTION … · moment opposing building frames resisting on...

Documents

Transcript of DYNAMIC ANALYSIS OF SOIL STRUCTURE INTERACTION … · moment opposing building frames resisting on...