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Soft Computing Approaches to Constitutive Modeling in Geotechnical Engineering
Jamshid Ghaboussi and Youssef M. HashashDepartment of Civil and Environmental Engineering
University of Illinois at Urbana-Champaign
Contents
Introductory Remarks on Soft ComputingSelfSim: Constitutive Modeling from System ResponseSeveral Applications of SelfSimField Calibration of constitutive Models in Deep Excavations
Soft Computing
Soft computing methods are inspired by the computing and problem solving strategies in nature.
They Are fundamentally different than the mathematically based problem solving methods.
Soft computing tools are neural networks, genetic algorithm and fuzzy logic.
Problem Solving in NatureNature solves very difficult inverse problems.
Nature does not use mathematics.
Two main components of nature’s problem solving strategies are:
LearningReduction in disorder
Nature utilizes random search and gradual improvement.
Inverse Problem of Constitutive Modeling from Field Measurements
In many geotechnical problems it is too difficult to sample or perform realistic material tests.Often field measurements are available.
Autoprogressive Algorithm
Structure is modeled by FE and constitutive model is represented by a NN.
Pre-train NN material model to learn linearly elastic constitutive behavior
Apply boundary forces: apply ∆Pn, compute ∆Un
Enforce boundary displacements
11
1
( ,... : ...)
n n nj j
t n n nj j j
n n n
n n n
P P P
K U P I
U U U
δ
δσ σ ε
−
−
−
= + ∆
= −
∆ = ∆ += NN
( ) (( ),... : ...)nn n
n n n n
U U Uδσ δσ ε δε
= ++ = +ΝΝ
Train Neural Networks
(( ),... : ...)n n n nσ σ ε δε= +NN
Learning Material Behavior from Response of a Simple Structure
NN TrainingFE Analysis A,Force BC
xε yε xyγ
…
…
xσ yσ xyτ
FE Analysis B,Displacement BC
Simulated Experiment, Von-Mises Plasticity with hardening
Train a NN to learn the material behavior from the response of this structure
Evolution of Load-Displacement
pre-training
0
200
400
600
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1000
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1400
1600
1800
0 0.5 1 1.5 2 2.5 3edge-center point deflection (mm)
load
(MN
)pre-training
0
200
400
600
800
1000
1200
1400
1600
1800
0 0.5 1 1.5 2 2.5 3edge-center point deflection (mm)
load
(MN
)pass 04
0
200
400
600
800
1000
1200
1400
1600
1800
0 0.5 1 1.5 2 2.5 3edge-center point deflection (mm)
load
(MN
)pass 05
0
200
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800
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1400
1600
1800
0 0.5 1 1.5 2 2.5 3edge-center point deflection (mm)
load
(MN
)pass 08
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1400
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1800
0 0.5 1 1.5 2 2.5 3edge-center point deflection (mm)
load
(MN
)pass 10
0
200
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800
1000
1200
1400
1600
1800
0 0.5 1 1.5 2 2.5 3edge-center point deflection (mm)
load
(MN
)pass 11
0
200
400
600
800
1000
1200
1400
1600
1800
0 0.5 1 1.5 2 2.5 3edge-center point deflection (mm)
load
(MN
)pass 12
0
200
400
600
800
1000
1200
1400
1600
1800
0 0.5 1 1.5 2 2.5 3edge-center point deflection (mm)
load
(MN
)pass 13
0
200
400
600
800
1000
1200
1400
1600
1800
0 0.5 1 1.5 2 2.5 3edge-center point deflection (mm)
load
(MN
)
NN Learning of Material Behavior
s11-e11 (pre-train)
0.00E+00
5.00E+01
1.00E+02
1.50E+02
2.00E+02
2.50E+02
3.00E+02
-6.00E-03 -5.00E-03 -4.00E-03 -3.00E-03 -2.00E-03 -1.00E-03 0.00E+00
s22-e22 (pre-train)
0.00E+00
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s11-e11 (pre-train)
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1.00E+02
1.50E+02
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3.00E+02
-6.00E-03 -5.00E-03 -4.00E-03 -3.00E-03 -2.00E-03 -1.00E-03 0.00E+00
s22-e22 (pre-train)
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s11-e11 (pass 04)
0.00E+00
5.00E+01
1.00E+02
1.50E+02
2.00E+02
2.50E+02
3.00E+02
-6.00E-03 -5.00E-03 -4.00E-03 -3.00E-03 -2.00E-03 -1.00E-03 0.00E+00
s22-e22 (pass 04)
0.00E+00
1.00E+02
2.00E+02
3.00E+02
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5.00E+02
6.00E+02
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8.00E+02
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0.00E+00 1.00E-02 2.00E-02 3.00E-02 4.00E-02 5.00E-02 6.00E-02 7.00E-02
s11-e11 (pass 05)
0.00E+00
5.00E+01
1.00E+02
1.50E+02
2.00E+02
2.50E+02
3.00E+02
-6.00E-03 -5.00E-03 -4.00E-03 -3.00E-03 -2.00E-03 -1.00E-03 0.00E+00
s22-e22 (pass 05)
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5.00E+02
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0.00E+00 1.00E-02 2.00E-02 3.00E-02 4.00E-02 5.00E-02 6.00E-02 7.00E-02
s11-e11 (pass 08)
0.00E+00
5.00E+01
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3.00E+02
-6.00E-03 -5.00E-03 -4.00E-03 -3.00E-03 -2.00E-03 -1.00E-03 0.00E+00
s22-e22 (pass 08)
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0.00E+00 1.00E-02 2.00E-02 3.00E-02 4.00E-02 5.00E-02 6.00E-02 7.00E-02
s11-e11 (pass 10)
0.00E+00
5.00E+01
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1.50E+02
2.00E+02
2.50E+02
3.00E+02
-6.00E-03 -5.00E-03 -4.00E-03 -3.00E-03 -2.00E-03 -1.00E-03 0.00E+00
s22-e22 (pass 10)
0.00E+00
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2.00E+02
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0.00E+00 1.00E-02 2.00E-02 3.00E-02 4.00E-02 5.00E-02 6.00E-02 7.00E-02
s11-e11 (pass 11)
0.00E+00
5.00E+01
1.00E+02
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-6.00E-03 -5.00E-03 -4.00E-03 -3.00E-03 -2.00E-03 -1.00E-03 0.00E+00
s22-e22 (pass 11)
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0.00E+00 1.00E-02 2.00E-02 3.00E-02 4.00E-02 5.00E-02 6.00E-02 7.00E-02
s11-e11 (pass 12)
0.00E+00
5.00E+01
1.00E+02
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-6.00E-03 -5.00E-03 -4.00E-03 -3.00E-03 -2.00E-03 -1.00E-03 0.00E+00
s22-e22 (pass 12)
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s11-e11 (pass 13)
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5.00E+01
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-6.00E-03 -5.00E-03 -4.00E-03 -3.00E-03 -2.00E-03 -1.00E-03 0.00E+00
s22-e22 (pass 13)
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9.00E+02
0.00E+00 1.00E-02 2.00E-02 3.00E-02 4.00E-02 5.00E-02 6.00E-02 7.00E-02
NN Learning of Material Behavior
s22-e22 (pre-train)
0.00E+00
1.00E+02
2.00E+02
3.00E+02
4.00E+02
5.00E+02
6.00E+02
7.00E+02
8.00E+02
9.00E+02
0.00E+00 5.00E-03 1.00E-02 1.50E-02 2.00E-02 2.50E-02 3.00E-02
s12-e12 (pre-train)
-2.00E+01
-1.00E+01
0.00E+00
1.00E+01
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5.00E+01
6.00E+01
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-1.00E-03 0.00E+00 1.00E-03 2.00E-03 3.00E-03 4.00E-03 5.00E-03 6.00E-03
s22-e22 (pre-train)
0.00E+00
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7.00E+02
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0.00E+00 5.00E-03 1.00E-02 1.50E-02 2.00E-02 2.50E-02 3.00E-02
s12-e12 (pre-train)
-2.00E+01
-1.00E+01
0.00E+00
1.00E+01
2.00E+01
3.00E+01
4.00E+01
5.00E+01
6.00E+01
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-1.00E-03 0.00E+00 1.00E-03 2.00E-03 3.00E-03 4.00E-03 5.00E-03 6.00E-03
s22-e22 (pass 04)
0.00E+00
1.00E+02
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5.00E+02
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0.00E+00 5.00E-03 1.00E-02 1.50E-02 2.00E-02 2.50E-02 3.00E-02
s12-e12 (pass 04)
-2.00E+01
-1.00E+01
0.00E+00
1.00E+01
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-1.00E-03 0.00E+00 1.00E-03 2.00E-03 3.00E-03 4.00E-03 5.00E-03 6.00E-03
s22-e22 (pass 05)
0.00E+00
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s12-e12 (pass 05)
-2.00E+01
-1.00E+01
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s22-e22 (pass 08)
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s12-e12 (pass 08)
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s22-e22 (pass 10)
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s12-e12 (pass 10)
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s22-e22 (pass 11)
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s12-e12 (pass 11)
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s22-e22 (pass 12)
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s12-e12 (pass 12)
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s22-e22 (pass 13)
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s12-e12 (pass 13)
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Creep of Concrete Beam
pass00
0.000 0.001 0.002
strain
CL
3504 mm
150 mm
100 mm
20 mm
20 mm
pass00
0
5
10
15
20
0 100 200 300 400time (days)
defle
ctio
n (m
m) pass05
0
5
10
15
20
0 100 200 300 400time (days)
defle
ctio
n (m
m)
pass05
0.000 0.001 0.002
strainpass10
0.000 0.001 0.002
strain
pass10
0
5
10
15
20
0 100 200 300 400time (days)
defle
ctio
n (m
m)
pass20
0.000 0.001 0.002
strain
pass20
0
5
10
15
20
0 100 200 300 400time (days)
defle
ctio
n (m
m)
top surface
bottom surface
insta
nt12
day
s60
day
s13
0 da
ys51
3 da
ys
Experiment by Bakoss et al. (1982)
20
40
60
80
100
120
140
160
180
0 10 20 30 40 50 60 70
Jan 10, 2002: 73 days (12 segments)Feb 14, 2002: 108 days (all 18 segments)Jul 11, 2002: 255 days (all 18 segments)design camber at the completion
cam
ber (
mm
)
distance from the pier P4 (m)
The Challenge: Deflection Control
Field Calibration of the Pipiral Bridge
-30
-25
-20
-15
-10
-5
0
5
0 10 20 30 40 50 60 70
pre-trainmeasurement
disp
lace
men
t (m
m)
distance from the pier (m)
step 5
-30
-25
-20
-15
-10
-5
0
5
0 10 20 30 40 50 60 70
pass 10measurement
disp
lace
men
t (m
m)
distance from the pier (m)
step 5
-30
-25
-20
-15
-10
-5
0
5
0 10 20 30 40 50 60 70
pass 20measurement
disp
lace
men
t (m
m)
distance from the pier (m)
step 5
-70
-60
-50
-40
-30
-20
-10
0
0 10 20 30 40 50 60 70
disp
lace
men
t (m
m)
distance from the pier (m)
step 6
-70
-60
-50
-40
-30
-20
-10
0
0 10 20 30 40 50 60 70
disp
lace
men
t (m
m)
distance from the pier (m)
step 6
-70
-60
-50
-40
-30
-20
-10
0
0 10 20 30 40 50 60 70
disp
lace
men
t (m
m)
distance from the pier (m)
step 6
Improvement of Camber by Using SelfSim
0
5
10
15
20
25
0 10 20 30 40 50 60 70
adju
stm
ent t
o ca
stin
g cu
rves
(mm
)
distance from the pier (m)
20
40
60
80
100
120
140
160
180
0 10 20 30 40 50 60 70
Jan 10, 2002: 73 days (12 segments)Feb 14, 2002: 108 days (all 18 segments)Jul 11, 2002: 255 days (all 18 segments)design camber at the completion
cam
ber (
mm
)
distance from the pier P4 (m)
Application of SelfSim to synthetically generated downhole array data- case1
-0.08
-0.06
-0.04
-0.02
0.00
0.02
0.04
0.06
0.08
0 0.2 0.4 0.6 0.8 1Time (sec)
Dis
plac
emen
t(ft)
Hyperbolic model + Masing rule(Target)
Initialized NN (Linear)
-0.08
-0.06
-0.04
-0.02
0.00
0.02
0.04
0.06
0.08
0 0.2 0.4 0.6 0.8 1Time (sec)
Dis
plac
emen
t(ft)
Hyperbolic model + Masing rule(Target)Initialized NN (Linear)SelfSim (1st pass)
-0.08
-0.06
-0.04
-0.02
0.00
0.02
0.04
0.06
0.08
0 0.2 0.4 0.6 0.8 1Time (sec)
Dis
plac
emen
t(ft)
Hyperbolic model + Masing rule(Target)Initialized NN (Linear)SelfSim (3rd pass)
-0.08
-0.06
-0.04
-0.02
0.00
0.02
0.04
0.06
0.08
0 0.2 0.4 0.6 0.8 1Time (sec)
Dis
plac
emen
t(ft)
Hyperbolic model + Masing rule(Target)Initialized NN (Linear)SelfSim (7th pass)
Layer1
•Sinusoidal motion •single layer
•Sinusoidal motion •single layer
Application of SelfSim to synthetically generated downhole array data- case1
-0.8
-0.6
-0.4
-0.2
0.0
0.2
0.4
0.6
0.8
-0.20 -0.15 -0.10 -0.05 0.00 0.05 0.10 0.15 0.20
Strain
Stre
ss
Hyperbolic model + Masing rule (Target)
Initialized NN (Linear)
-0.8
-0.6
-0.4
-0.2
0.0
0.2
0.4
0.6
0.8
-0.20 -0.15 -0.10 -0.05 0.00 0.05 0.10 0.15 0.20
Strain
Stre
ss
Hyperbolic model + Masing rule (Target)Initialized NN (Linear)SelfSim (1st pass)
-0.8
-0.6
-0.4
-0.2
0.0
0.2
0.4
0.6
0.8
-0.20 -0.15 -0.10 -0.05 0.00 0.05 0.10 0.15 0.20
Strain
Stre
ss
Hyperbolic model + Masing rule (Target)Initialized NN (Linear)SelfSim (3rd pass)
-0.8
-0.6
-0.4
-0.2
0.0
0.2
0.4
0.6
0.8
-0.20 -0.15 -0.10 -0.05 0.00 0.05 0.10 0.15 0.20
Strain
Stre
ss
Hyperbolic model + Masing rule (Target)Initialized NN (Linear)SelfSim (7th pass)
Layer1
•cyclic motion •single layer
•cyclic motion •single layer
Application of SelfSim to synthetically generated downhole array data- case2
0
1
2
3
4
5
6
7
8
9
10
0.01 0.1 1 10
Period (sec)
Sa
(g)
DEESPSOIL (NN)DEEPSOIL (hyperbolic)DEEPSOIL+LinearNN
Pass1
0
1
2
3
4
5
6
7
8
9
10
0.01 0.1 1 10
Period (sec)
Sa
(g)
DEESPSOIL (NN)DEEPSOIL (hyperbolic)DEEPSOIL+LinearNN
Pass2
0
1
2
3
4
5
6
7
8
9
10
0.01 0.1 1 10
Period (sec)
Sa
(g)
DEESPSOIL (NN)DEEPSOIL (hyperbolic)DEEPSOIL+LinearNN
Pass3
0
1
2
3
4
5
6
7
8
9
10
0.01 0.1 1 10
Period (sec)
Sa
(g)
DEESPSOIL (NN)DEEPSOIL (hyperbolic)DEEPSOIL+LinearNN
Pass4
Layer1
Layer2
Layer3
Layer4
•seismic motion •multiple layers•same soil properties
•seismic motion •multiple layers•same soil properties
Application of SelfSim to synthetically generated downhole array data- case2
-4 .0 0 E-0 2
-3 .0 0 E-0 2
-2 .0 0 E-0 2
-1 .0 0 E-0 2
0 .0 0 E+0 0
1 .0 0 E-0 2
2 .0 0 E-0 2
3 .0 0 E-0 2
4 .0 0 E-0 2
0 2 4 6 8 1 0
T im e (s e c )
Dis
plac
emen
t(ft
)
S e lfS imD E E P S O IL + hy p e rb o lic (Ta rg e t )D E E P S O IL + line a rN N
-5.0E+03
-4.0E+03
-3.0E+03
-2.0E+03
-1.0E+03
0.0E+00
1.0E+03
2.0E+03
3.0E+03
-3.0E-01 -2.0E-01 -1.0E-01 0.0E+00 1.0E-01 2.0E-01 3.0E-01
Strain(%)
Stre
ssTargetLayer4Layer3Layer2Layer1
Layer1
Layer2
Layer3
Layer4
•seismic motion •multiple layers•same soil properties
•seismic motion •multiple layers•same soil properties
SelfSim in Bio-Engineering
J. Ghaboussi, D. A. Pecknold, Y. M. Hashash
Applications in Ophthalmology Characterizing the material properties of human cornea for simulating Individualized laser surgeryMeasurement of intra-ocular pressure (IOP)Developing a new instrument
Applications in bio-medical imagingCharacterizing the material properties of soft tissueImaging of the material properties of soft tissue for diagnostics and disease progression
Simulation of Goldman Applanation Tonometer
0.02mm
15mmHg
First step
Applanation
Second stepBefore loading steps
Integration of numerical modeling Integration of numerical modeling and field observations for deep and field observations for deep excavationsexcavations
1. Field Measurements
2. SelfSim Training FEM, iterated
Neural Network Constitutive Soil Model
Stress-Strain PairsTraining of NANN
σ,ε
Initializing or Pre-training stress strain data from:1. Linear elastic2. Laboratory tests3. Case histories4. Approximate constitutive models
a) Simulate Construction Sequence=> extract stresses
b) Apply Measurements=> extract strains
Similar excavationsNext excavation stage
3.Forward FEM analysis with trained NN material model
and/or
εσ
Synthetic Synthetic ““FieldField”” MeasurementsMeasurements
02468100
5
10
15
20
25
30
35
40
Dep
th (m
)
2.5
5.0
10
15
MIT-E3 "Field" Measurements
Lateral Wall Deflection (cm)
7.5
12.5
-5
-4
-3
-2
-1
00 50 100 150 200
Surf
ace
Settl
emen
t (cm
)MIT-E3 "Field" Measurements
Distance behind the wall (m)
B/2=20m
H=15m
Support Spacing =2.5m
Diaphragm WallThickness =0.9mE=2.3*104 MPaυ=0.2
No SelfSim training
02468100
5
10
15
20
25
30
35
40
Dep
th (m
)
2.5
5.0
10
15
Elastic Pre-trained
Lateral Wall Deflection (cm)
7.5
12.5
-5
-4
-3
-2
-1
00 50 100 150 200
Surf
ace
Settl
emen
t (cm
)Elastic Pre-trained
Distance behind the wall (m)
SelfSim training SelfSim training
02468100
5
10
15
20
25
30
35
40
Dep
th (m
)
2.5
5.0
10
15
Training: Pass #1using Exc. depth =2.5m data
Lateral Wall Deflection (cm)
7.5
12.5
-5
-4
-3
-2
-1
00 50 100 150 200
Surf
ace
Settl
emen
t (cm
)Training: Pass #1
using all Exc. depth =2.5m data
Distance behind the wall (m)
SelfSim training SelfSim training
02468100
5
10
15
20
25
30
35
40
Dep
th (m
)
2.5
5.0
10
15
Training: Pass #1using Exc. depth=5m data
Lateral Wall Deflection (cm)
7.5
12.5
-5
-4
-3
-2
-1
00 50 100 150 200
Surf
ace
Settl
emen
t (cm
)Training: Pass #1
using Exc. depth=5m data
Distance behind the wall (m)
SelfSim training SelfSim training
02468100
5
10
15
20
25
30
35
40
Dep
th (m
)
2.5
5.0
10
15
Training: Pass #1using Exc. depth=7.5m data
Lateral Wall Deflection (cm)
7.5
12.5
-5
-4
-3
-2
-1
00 50 100 150 200
Surf
ace
Settl
emen
t (cm
)Training: Pass #1
using Exc. depth=7.5m data
Distance behind the wall (m)
SelfSim training SelfSim training
02468100
5
10
15
20
25
30
35
40
Dep
th (m
)
2.5
5.0
10
15
Training: Pass #1using Exc. depth=10m data
Lateral Wall Deflection (cm)
7.5
12.5
-5
-4
-3
-2
-1
00 50 100 150 200
Surf
ace
Settl
emen
t (cm
)Training: Pass #1
using Exc. depth=10m data
Distance behind the wall (m)
SelfSim training SelfSim training
02468100
5
10
15
20
25
30
35
40
Dep
th (m
)
2.5
5.0
10
15
Training: Pass #1using Exc.depth=12.5m data
Lateral Wall Deflection (cm)
7.5
12.5
-5
-4
-3
-2
-1
00 50 100 150 200
Surf
ace
Settl
emen
t (cm
)Training: Pass #1
using Exc.depth=12.5m data
Distance behind the wall (m)
SelfSim training SelfSim training
02468100
5
10
15
20
25
30
35
40
Dep
th (m
)
2.5
5.0
10
15
Training: Pass #1using all Exc. data
Lateral Wall Deflection (cm)
7.5
12.5
-5
-4
-3
-2
-1
00 50 100 150 200
Surf
ace
Settl
emen
t (cm
)Training: Pass #1
using all Exc. data
Distance behind the wall (m)
SelfSim training SelfSim training
02468100
5
10
15
20
25
30
35
40
Dep
th (m
)
2.5
5.0
10
15
Training: Pass #2using all Exc. data
Lateral Wall Deflection (cm)
7.5
12.5
-5
-4
-3
-2
-1
00 50 100 150 200
Surf
ace
Settl
emen
t (cm
)Training: Pass #2
using all Exc. data
Distance behind the wall (m)
SelfSim training SelfSim training
02468100
5
10
15
20
25
30
35
40
Dep
th (m
)
2.5
5.0
10
15
Training: Pass #4using all Exc. data
Lateral Wall Deflection (cm)
7.5
12.5
-5
-4
-3
-2
-1
00 50 100 150 200
Surf
ace
Settl
emen
t (cm
)Training: Pass #4
using all Exc. data
Distance behind the wall (m)
SelfSim training SelfSim training
02468100
5
10
15
20
25
30
35
40
Dep
th (m
)
2.5
5.0
10
15
Training: Pass #6using all Exc. data
Lateral Wall Deflection (cm)
7.5
12.5
-5
-4
-3
-2
-1
00 50 100 150 200
Surf
ace
Settl
emen
t (cm
)Training: Pass #6
using all Exc. data
Distance behind the wall (m)
SelfSim training SelfSim training
02468100
5
10
15
20
25
30
35
40
Dep
th (m
)
2.5
5.0
10
15
Training: Pass #8using all Exc. data
Lateral Wall Deflection (cm)
7.5
12.5
-5
-4
-3
-2
-1
00 50 100 150 200
Surf
ace
Settl
emen
t (cm
)Training: Pass #8
using all Exc. data
Distance behind the wall (m)
Evaluate a Trained NN modelEvaluate a Trained NN model
-20 0 20 40
SupportSpacingh=2.5m
H
CL
Distance from Diaphragm Wall, x (m)
Diaphragm WallLength, L=40m
Thickness, t =0.9m
E = 2.3x104MPa
ν =0.2
L
Dth
()
Trained NN model
-20 0 20 40
SupportSpacingh=2.5m
H
CL
Distance from Diaphragm Wall, x (m)
Diaphragm WallLength, L=40m
Thickness, t =0.9m
E = 2.3x104MPa
ν =0.2
L
Dth
()
SelfSim
AnalysisStress and
Strain
jσ
jε 1jε − 1jσ −
-120 10-3 -80 10-3 -40 10-3 0 1
05
10152025303540
Dep
th (m
)
12.5
22.5
5.0
0 20 40 60 80 100
Surfa
ce D
ispl
acem
ent (
mm
) Distance behind wall (m)
0
-20
-40
-60
12.522.5
5.0
NN
MIT E-3
mm
Simulate Laboratory Tests
σ
ε
Plane Strain TestsPlane Strain Tests (PST)(PST)
-0.4
-0.2
0
0.2
0.4
0.6
0.8
-0.5 -0.25 0 0.25 0.5
(σ' v-σ
' h)/2*σ
' v0
εaxial
(%)
SelfSim MITE3 OCR1.0
SelfSim Elastic Pre-train
-0.4
-0.2
0
0.2
0.4
0.6
0.8
-0.5 -0.25 0 0.25 0.5(σ
' v-σ' h)/2
*σ' v0
εaxial
(%)
SelfSim MITE3 OCR1.0
SelfSim Elastic Pre-train
-0.4
-0.2
0
0.2
0.4
0.6
0.8
-0.5 -0.25 0 0.25 0.5
(σ' v-σ
' h)/2*σ
' v0
εaxial
(%)
SelfSim MITE3 OCR1.0
SelfSim Elastic Pre-train
1
10
100
1000
0.01 0.1 1
MITE3SelfSim
Seca
nt M
odul
us,
(δσ 1-δ
σ 3)/2γσ
' 0
Maximum Shear Strain, γ (%)
PST compression
PST Extension
Learning from PrecedenceLearning from Precedence
Soft Clay Layer MIT-E3
OCR=1.3Crust Clay Layer MIT-E3 OCR=4
Stiff Layer MIT-E3 OCR=5
Rock Linear Elastic
5 m
30 m
45 m
40 m
he=2.5mhs=5.0m
B/2=40m
Case3
40 m Flexible Wall
5 m
35 m
40 m
40 m
30 m Stiff Wall
he=2.5m
hs=2.5&5.0 m
B/2=30m
Case25 m
45 m
30 m
40 m
he=2.5mhs=2.5m
40 m Flexible Wall
B/2=20m
Case1
New Excavation Site
35 m
25 m
60 m
40 m Stiff Wall
he=2.5mhs=2.5m
B/2=20m
Case1Case1 Case2Case2 Case3Case3SelfSim TrainingSelfSim Training
02460
5
10
15
20
25
30
35
40
Dep
th (m
)
2.5
5.0
10
15
40m
Fle
xibl
e W
all
Lateral Wall Deflection (cm)
12.5
7.5
Soil
beha
vior
to b
e le
arne
d
hs=2.5m
B/2=20m
-3
-2
-1
00 50 100 150 200 Surface Settlement (cm
)
Case 1 after trainingwith all three cases
Distance behind the wall (m)
02468100
5
10
15
20
25
30
Dep
th (m
)
2.5
5.0
10
15
30m
Stif
f Wal
l
Lateral Wall Deflection (cm)
12.5
7.5
Soil
beha
vior
to b
e le
arne
d
hs=5.0 & 2.5m
B/2=30m
-4
-3
-2
-1
00 50 100 150 200 Surface Settlem
ent (cm)
Case2 after trainingwith all three cases
Distance behind the wall (m)
0246810120
5
10
15
20
25
30
35
40
Dep
th (m
)
2.55.0
10
15
Exc. Depth (m)
Lateral Wall Deflection (cm)
12.5
7.5
Soil
beha
vior
to b
e le
arne
d
40m
Fle
xibl
e W
all
ht=2.5 & 5.0m
B/2=40m
-6-5-4-3-2-10
0 50 100 150 200 Surface Settlement (cm
)
Case 3 after trainingwith all three cases
Distance behind the wall (m)
Case1Case1 Case2Case2 Case3Case3SelfSim TrainingSelfSim Training
02460
5
10
15
20
25
30
35
40
Dep
th (m
)
2.5
5.0
10
15
40m
Fle
xibl
e W
all
Lateral Wall Deflection (cm)
12.5
7.5
Soil
beha
vior
to b
e le
arne
d
hs=2.5m
B/2=20m
-3
-2
-1
00 50 100 150 200 Surface Settlement (cm
)
Case 1 after trainingwith all three cases
Distance behind the wall (m)
02468100
5
10
15
20
25
30
Dep
th (m
)
2.5
5.0
10
15
30m
Stif
f Wal
l
Lateral Wall Deflection (cm)
12.5
7.5
Soil
beha
vior
to b
e le
arne
d
hs=5.0 & 2.5m
B/2=30m
-4
-3
-2
-1
00 50 100 150 200 Surface Settlem
ent (cm)
Case2 after trainingwith all three cases
Distance behind the wall (m)
0246810120
5
10
15
20
25
30
35
40
Dep
th (m
)
2.55.0
10
15
Exc. Depth (m)
Lateral Wall Deflection (cm)
12.5
7.5
Soil
beha
vior
to b
e le
arne
d
40m
Fle
xibl
e W
all
ht=2.5 & 5.0m
B/2=40m
-6-5-4-3-2-10
0 50 100 150 200 Surface Settlement (cm
)
Case 3 after trainingwith all three cases
Distance behind the wall (m)
Case1Case1 Case2Case2 Case3Case3SelfSim TrainingSelfSim Training
02460
5
10
15
20
25
30
35
40
Dep
th (m
)
2.5
5.0
10
15
40m
Fle
xibl
e W
all
Lateral Wall Deflection (cm)
12.5
7.5
Soil
beha
vior
to b
e le
arne
d
hs=2.5m
B/2=20m
-3
-2
-1
00 50 100 150 200 Surface Settlement (cm
)
Case 1 after trainingwith all three cases
Distance behind the wall (m)
02468100
5
10
15
20
25
30
Dep
th (m
)
2.5
5.0
10
15
30m
Stif
f Wal
l
Lateral Wall Deflection (cm)
12.5
7.5
Soil
beha
vior
to b
e le
arne
d
hs=5.0 & 2.5m
B/2=30m
-4
-3
-2
-1
00 50 100 150 200 Surface Settlem
ent (cm)
Case2 after trainingwith all three cases
Distance behind the wall (m)
0246810120
5
10
15
20
25
30
35
40
Dep
th (m
)
2.55.0
10
15
Exc. Depth (m)
Lateral Wall Deflection (cm)
12.5
7.5
Soil
beha
vior
to b
e le
arne
d
40m
Fle
xibl
e W
all
ht=2.5 & 5.0m
B/2=40m
-6-5-4-3-2-10
0 50 100 150 200 Surface Settlement (cm
)
Case 3 after trainingwith all three cases
Distance behind the wall (m)
Case1Case1 Case2Case2 Case3Case3SelfSim TrainingSelfSim Training
02460
5
10
15
20
25
30
35
40
Dep
th (m
)
2.5
5.0
10
15
40m
Fle
xibl
e W
all
Lateral Wall Deflection (cm)
12.5
7.5
Soil
beha
vior
to b
e le
arne
d
hs=2.5m
B/2=20m
-3
-2
-1
00 50 100 150 200 Surface Settlement (cm
)
Case 1 after trainingwith all three cases
Distance behind the wall (m)
02468100
5
10
15
20
25
30
Dep
th (m
)
2.5
5.0
10
15
30m
Stif
f Wal
l
Lateral Wall Deflection (cm)
12.5
7.5
Soil
beha
vior
to b
e le
arne
d
hs=5.0 & 2.5m
B/2=30m
-4
-3
-2
-1
00 50 100 150 200 Surface Settlem
ent (cm)
Case2 after trainingwith all three cases
Distance behind the wall (m)
0246810120
5
10
15
20
25
30
35
40
Dep
th (m
)
2.55.0
10
15
Exc. Depth (m)
Lateral Wall Deflection (cm)
12.5
7.5
Soil
beha
vior
to b
e le
arne
d
40m
Fle
xibl
e W
all
ht=2.5 & 5.0m
B/2=40m
-6-5-4-3-2-10
0 50 100 150 200 Surface Settlement (cm
)
Case 3 after trainingwith all three cases
Distance behind the wall (m)
0
5
10
15
20
25
30
35
40
012345678
Dep
th (m
)
Lateral Wall Deflection (cm)
2.5
5.0
10
1512.5
7.5
hs=2.5m
B/2=20m
Soil
beha
vior
to b
e le
arne
d20
-3
-2
-1
00 50 100 150 200 Surface Settlem
ent (cm)
Distance behind the wall (m)
Prediction of New CasePrediction of New Case
0
5
10
15
20
25
30
35
40
012345678
012345678
Dep
th (m
)
Lateral Wall Deflection (cm)
2.5
5.0
10
1512.5
7.5
hs=2.5m
B/2=20m
Prediction of "New" Case
Soil
beha
vior
to b
e le
arne
d20
-3
-2
-1
00 50 100 150 200
-3
-2
-1
0
Surface Settlement (cm
)
Distance behind the wall (m)
Prediction of "New" Case after training with Case 1
Prediction of New Case after Case1Prediction of New Case after Case1
0
5
10
15
20
25
30
35
40
012345678
012345678
Dep
th (m
)
Lateral Wall Deflection (cm)
2.5
5.0
10
1512.5
7.5
hs=2.5m
B/2=20m
Prediction of "New" Case
Soil
beha
vior
to b
e le
arne
d20
-3
-2
-1
00 50 100 150 200
-3
-2
-1
0
Surface Settlement (cm
)
Distance behind the wall (m)
Prediction of "New" Case after training with Case 1 and 2
Prediction of New Case after Case1&2Prediction of New Case after Case1&2
Prediction of New Case after Prediction of New Case after Case1,2&3Case1,2&3
0
5
10
15
20
25
30
35
40
012345678
012345678
Dep
th (m
)
Lateral Wall Deflection (cm)
2.5
5.0
10
1512.5
7.5
hs=2.5m
B/2=20m
Prediction of "New" Case
Soil
beha
vior
to b
e le
arne
d20
-3
-2
-1
00 50 100 150 200
-3
-2
-1
0
Surface Settlement (cm
)
Distance behind the wall (m)
Prediction of "New" Case after training with all three cases
LurieLurie Research Center Research Center –– Plan and Plan and Cross Section ViewCross Section View
LR5
LR7
LR6
LR8
Embedded Soil Settlement Point
PK Nail
Utility Monitoring Point Inclinometer
LR3 LR2
LR4
Lurie Research Center
LR1
200 feet
60 meters
20 100
20 40
Soil Boring
E. Superior Street
E. Huron Street
Pedestrian tunnel
Pren
tice
Pavi
llion
(1
leve
l bas
emen
t)
N. F
airb
anks
Cou
rt
N
El. +2.1 mFill
Stiff/V. Stiff Silty Clay
Hard Silty Clay
208
308
PZ27 Sheeting
El. -1.2 m
El. -5.2 m
El. -14.9 m
Soft/Med Silty Clay
El. +3.4 m
Lake Sand
El. -20.7 m
El. -4.6 m
El. +0.6 m
El. -11.6 m
El. +4.3 m
108
-20
-15
-10
-5
0
5 0246810Lat. Wall Deflection (mm)
Elev
atio
n (m
)
(a) Stage 1:Exc. +1.5 m
-20
-15
-10
-5
0
5 0246810Lat. Wall Deflection (mm)
Elev
atio
n (m
)
(a) Stage 1:Exc. +1.5 m
-20
-15
-10
-5
0
50204060
Elevation (m)(g) Stage 7:
Exc. -7.3 m-20
-15
-10
-5
0
50204060
Elevation (m)(g) Stage 7:
Exc. -7.3 m
Simplified Construction SequenceSimplified Construction Sequence
CL CL
CL CL
CL CL
CL CL
+4.2+4.2+1.5
+1.5+4.2
-2.4
+4.2
-2.4
+4.2
-5.8
+4.2
-5.8
+4.2
-7.3
+4.2
Stage 0
ReferenceConfiguration
Stage 1Undrained Excavation16 days dewatering
Stage 2 Stage 3InstallationTieback 1
Undrained Excavation86 days dewatering
Stage 4InstallationTieback 2 Stage 5
Undrained Excavation38 days dewatering
Stage 6InstallationTieback 3 Stage 7
Undrained Excavation25 days dewatering
-20
-15
-10
-5
0
5010203040506070
Lateral Wall Deflection (mm)El
evat
ion
(m)
Pre-trainedMeasuredExc StageExc. 1.5 mTieback 1
Exc. -2.4 mTieback 2
Exc. -5.8 mTieback 3
Exc. -7.3 m
(a) No Training
0
10
20
30
40
50
0 10 20 30 40 50 60 70 80Distance behind the wall (m)
Surface Settlement (m
m)
(b) No Training
No TrainingNo Training
-20
-15
-10
-5
0
5010203040506070
Lateral Wall Deflection (mm)El
evat
ion
(m)
SelfSimMeasuredExc StageExc. 1.5 mTieback 1
Exc. -2.4 mTieback 2
Exc. -5.8 mTieback 3
Exc. -7.3 m
(a) 10 Passes of Training
0
10
20
30
40
50
0 10 20 30 40 50 60 70 80Distance behind the wall (m)
Surface Settlement (m
m)
(b) 10 Passes of Training
SelfSim TrainingSelfSim Training
SelfSim TrainingSelfSim Training
-20
-15
-10
-5
0
5-505101520Lat. Wall Deflection (mm)
Elevation (m)
(c) Stage 3:Exc. -2.4 m
-20
-15
-10
-5
0
5 -505101520
Elev
atio
n (m
)
(d) Stage 4:Tieback 2 -20
-15
-10
-5
0
50204060
Elevation (m)(e) Stage 5:
Exc. -5.8 m
0246810Lat. Wall Deflection (mm)
-20
-15
-10
-5
0
5
(b) Stage 2:Tieback 1
-20
-15
-10
-5
0
50204060
Elevation (m)(g) Stage 7:
Exc. -7.3 m-20
-15
-10
-5
0
5 0204060LR3LR5
LR6LR7LR8
RepresentativeSelfSim(f) Stage 6:
Tieback 3
Elev
atio
n (m
)
-20
-15
-10
-5
0
5 0246810Lat. Wall Deflection (mm)
Elev
atio
n (m
)
(a) Stage 1:Exc. +1.5 m
Lurie Center
SelfSim TrainingSelfSim Training
0
10
20
0 10 20 30 40 50 60 70 80Distance behind the wall (m)
Surf
ace
Settl
. (m
m)
(a) Stage 1:Exc. +1.5 m
0
10
20
0 10 20 30 40 50 60 70 80Distance behind the wall (m)
Surface Settl. (mm
)
(b) Stage 2:Tieback 1
0
10
20
0 10 20 30 40 50 60 70 80
Surf
ace
Settl
. (m
m)
(c) Stage 3:Exc. -2.4 m
0
10
20
0 10 20 30 40 50 60 70 80 Surface Settl. (mm
)
(d) Stage 4:Tieback 2
01020304050
0 10 20 30 40 50 60 70 80
Surf
ace
Settl
. (m
m)
(e) Stage 5:Exc. -5.8 m
01020304050
0 10 20 30 40 50 60 70 80 Surface Settl. (mm
)
(f) Stage 6:Tieback 3
01020304050
0 10 20 30 40 50 60 70 80
Surf
ace
Settl
. (m
m)
(g) Stage 7:Exc. -7.3 m
Assessment of extracted soil modelAssessment of extracted soil model
-0.4
-0.2
0
0.2
0.4
0.6
-0.4 -0.2 0 0.2 0.4
(σ' v-σ
' h)/2σ'
v0
εaxial
(%)
Extracted Clay Model (SelfSim)Triaxial tests (Holman, 2005)
Elastic Pre-training
Assessment of extracted soil modelAssessment of extracted soil model
-0.4
-0.2
0
0.2
0.4
0.6
-0.4 -0.2 0 0.2 0.4
(σ' v-σ
' h)/2σ'
v0
εaxial
(%)
Extracted Clay Model (SelfSim)Triaxial tests (Holman, 2005)
Elastic Pre-training
Concluding RemarksMost of the difficult engineering problems are inverse problems.
Very effective methods can be developed for solving these inverse problems by learning from nature’s problem solving strategies.
Neural networks, genetic algorithm and fuzzy logic enable development of these methods.
Soft computing methods use nature’s strategies of reduction in disorder and learning.
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