Case Studies
May 26, 2022

Prediction of the Maximum Tensile Load in Reinforcement Layers of a MSE Wall Using ANN-Based Response Surface Method and Probabilistic Assessment of Internal Stability of the Wall

Publication: International Journal of Geomechanics
Volume 22, Issue 8

Abstract

The design of mechanically stabilized earth (MSE) walls depends greatly on the maximum tensile loads developed in the reinforcement layers. In practice, it has been found that the measured tensile loads significantly differ from the predicted values, and it is quantified by a bias value named load bias. It is defined as the ratio of the measured to the predicted maximum tensile load. Further, to evaluate the load bias, prediction of the maximum tensile load in reinforcements needs to be assessed properly for wall safety. Therefore, in this paper, a new artificial neural network (ANN)-based response surface method has been proposed to predict the maximum tensile load developed in reinforcements of MSE walls reinforced with steel strips. Both tensile strength and pullout limit states have been considered in this study. The sensitivity of the proposed load model on the design outcome (reliability index or probability of failure) has been assessed and compared with the existing response surface-based load model. One practical example problem has been considered, and the feasibility of the proposed model in predicting the reliability index (or probability of failure) is examined for different values of coefficient of variation of the nominal load and resistance. Design charts in terms of the failure probability of the wall over depth are presented throughout this study for both tensile strength and pullout limit states, and results reveal that the satisfactory performance of the proposed load model is achieved in predicting the reliability of the wall.

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Data Availability Statement

Some data (such as the data points of Figs. 3–5 and 7–12) that support the findings of this study are available from the corresponding author upon reasonable request.

Acknowledgments

The authors gratefully acknowledge the financial support provided to the first author by DST-SERB, India under the National Post-Doctoral Fellowship (NPDF) scheme of file number: PDF/2020/000685 at the Department of Civil Engineering, Indian Institute of Science, Bengaluru.

Notation

The following symbols are used in this paper:
Ac
cross-section area of a single reinforcement strip after corrosion;
b
width of reinforcement strip;
Ci, CKs
constants used in ANN response surface;
COVRn, COVQn, COVλR, COVλQ
coefficient of variation of nominal resistance, nominal load, resistance bias, and load bias, respectively;
Dtmax
distribution factor for Tmax;
dzo
initial zinc thickness;
Ec
corroded strip thickness at the end of design life;
En
initial nominal (before corrosion) thickness of steel;
Es
sacrificial (after corrosion) thickness of steel;
e
eccentricity due to the earth pressure;
Fn
nominal factor of safety;
F*
dimensionless interaction factor between soil and reinforcement layer;
Fy
initial or noncorroded yield strength of the steel;
f1(•)
logistic sigmoid transfer function;
H
total wall height;
h
number of hidden neurons;
Href
reference height equal to 6 m;
K
ratio of loss of steel yield strength to average loss of steel thickness;
Ka
coefficient of active earth pressure;
Ks
horizontal component of active lateral earth pressure coefficient;
K0
coefficient of earth pressure at-rest;
K~s
approximate value of Ks;
L
reinforcement length;
Le
anchorage length of the reinforcement layer in the passive zone;
m
number of levels for each factor in UDM;
n
number of random variables;
Pc
the maximum pullout capacity of the reinforcement layer;
Pf
probability of failure;
p
maximum number of columns of the uniform design table;
Qm, Qn
measured and nominal load terms, respectively;
q
surcharge pressure;
Rc
coverage ratio of the reinforcement layer equal to b/Sh;
Rm, Rn
measured and nominal resistance terms, respectively;
S
surcharge height;
Sh
horizontal spacing of reinforcement layer;
Sv
tributary vertical spacing of reinforcement layer;
Tal
nominal long-term design strength of reinforcement per unit width;
Tmax
maximum tensile load in a steel strip;
Tmax,m, Tmax,c
measured and computed values of Tmax, respectively;
tdep
time to deplete the zinc coating that protects the steel strip from corrosion;
tdes
design lifetime of the reinforcement from time of construction;
t1
duration of Stage 1 corrosion;
Vs
rate of loss of average thickness at the surface of steel strip;
Vz1
zinc depletion rate for Stage 1 corrosion;
Vz2
zinc depletion rate for Stage 2 corrosion;
w1, b1
weights and bias between input and hidden layer, respectively;
w2, b2
weights and bias between hidden and output layer, respectively;
xi
original value of input variable;
xi,max
maximum value of original input variable;
xi,min
minimum value of original input variable;
xi
normalized value of input variable;
xi,max
maximum value of normalized input variable equal to 1;
xi,min
minimum value of normalized input variable equal to −1;
z
depth below crest of wall;
β
reliability index;
β0, β1, and β2
regression coefficient used in LM5;
γ
unit weight of the soil;
λi, λKs
coefficients used in ANN response surface;
λQ
load bias;
λR
resistance bias;
μRn, μQn, μλR, μλQ
mean values of nominal resistance, nominal load, resistance bias, and load bias, respectively;
ϕ
friction angle of the soil;
Φc
soil cohesion factor;
Φfb
facing batter factor;
Φfs
facing stiffness factor;
Φg
global stiffness factor;
Φlocal
local stiffness factor;
ρn
nominal correlation coefficient between nominal resistance and load terms;
ρQ
Pearson’s correlation coefficient between nominal load and load bias value;
ρR
Pearson’s correlation coefficient between nominal resistance and resistance bias value; and
σv
vertical overburden pressure of the soil.

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Go to International Journal of Geomechanics
International Journal of Geomechanics
Volume 22Issue 8August 2022

History

Received: Aug 29, 2021
Accepted: Mar 14, 2022
Published online: May 26, 2022
Published in print: Aug 1, 2022
Discussion open until: Oct 26, 2022

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Authors

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Postdoctoral Fellow, Dept. of Civil Engineering, Indian Institute of Science, Bengaluru, Karnataka 560012, India (corresponding author). ORCID: https://orcid.org/0000-0002-0881-8136. Email: [email protected]
G. L. Sivakumar Babu, F.ASCE [email protected]
Professor, Dept. of Civil Engineering, Indian Institute of Science, Bengaluru, Karnataka 560012, India. Email: [email protected]

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