Modeling of Accumulated Energy Ratio (AER) for Estimating LiqueFaction Potential Using Artificial Neural Network (ANN) and Gene Expression Programming (GEP) (using data from Tabriz)
الموضوعات :
Armin Sahebkaram Alamdari
1
(Department of Civil Engineering, Tabriz Branch, Islamic Azad University, Tabriz, Iran)
Rouzbeh Dabiri
2
(Department of Civil Engineering, Tabriz Branch, Islamic Azad University, Tabriz, Iran)
Rasoul Jani
3
(Department of Civil Engineering, Tabriz Branch, Islamic Azad University, Tabriz, Iran)
Fariba Behrouz Sarand
4
(Department of Civil Engineering, Tabriz Branch, Islamic Azad University, Tabriz, Iran)
الکلمات المفتاحية: Artificial Neural Network, Tabriz City, Liquefaction potential, Accumulated energy ratio, Genetic expression programming,
ملخص المقالة :
Presenting a model specific to the city of Tabriz to estimate the liquefaction potential due to the region's seismicity and the high groundwater level can be effective in dealing with and predicting solutions to deal with this phenomenon. In recent years, the accumulation energy ratio (AER) as a parameter for estimating the liquefaction potential in the energy-based method proposed by Kokusho (2013) has been considered by many researchers. In this research, using perceptron multilayer (MLP) and radial base function (RBF) methods in artificial neural network (ANN) and genetic expression programming (GEP), the accumulation energy ratio using seismic and geotechnical data is modeled for the city of Tabriz. These modeling’s performed by all three methods are well consistent with the outputs. Still, the modeling performed using the Perceptron Multilayer (MLP) method is very compatible with the outputs and can estimate the results with an acceptable percentage. The relationship presented by genetic expression programming (GEP), which is trained with local data, can also yield satisfactory results from estimating the rate of accumulated energy in the study area and provided an independent and accessible relationship trained. With data specific to the study area, there is another advantage.
1.Kokusho, T. ―Liquefaction potential
evaluations: energy-based method versus
stress-based method‖, Canadian
Geotechnical Journal; 2013, 50: 1088–1099.
2.Kokusho, T., Mimori, Y. ―Liquefaction
potential evaluations by energy-based
method and stress-based method for various
ground motions‖, Soil Dynamic sand
Earthquake Engineering; 2015, 75: 130–146.
3.Kokusho, T., Kaneko, Y. ―Energy evaluation
for liquefaction-induced strain of loose sands
by harmonic and irregular loading tests‖,
Soil Dynamics and Earthquake Engineering;
2018, 114: 362-377.
4.Masulli, F., Studer, L. ―Time series
forecasting and neural networks‖, In: Proc.
Int. Joint Conf. on Neural Networks
(IJCNN), Washington, DC. New York,
1999.
5.Sahebkaram Alamdari, A., Dabiri, R., Jani,
R., and Behrouz Sarand, F. ―Seismic Zoning
of Tabriz City Area by Stochastic Finite
Fault Model with Acts of Site Impact‖, Soils
and Rocks; 2021, 44(1): 1-13.
6.Sahebkaram Alamdari, A., Dabiri, R., Jani,
R., and Behrouz Sarand, F. ―Evaluation of
Liquefaction Potential by Energy-based and
Stress-based Methods and Gene Expressing
Programming (Case study: Tabriz City)‖,
Indian Geotechnical Journal; 2021, (in
review).
7.Zhang, J., Juang, C. H., Martin, J. R.,
Huang, H. W. ―Inter-region variability of
Robertson and Wride method for
liquefaction hazard analysis‖, Engineering
Geology; 2016, 203: 191–203.
8.Muduli, P. K., Das, S. K. ―Model
uncertainty of SPT-based method for
evaluation of seismic soil liquefaction
potential using multi-gene genetic
programming‖, Soils and Foundations; 2015,
55(2): 258–275.
9.Goharzay, M., Noorzad, A., Ardakani, A.
M., Jalal, M. ―A worldwide SPT-based soil
liquefaction triggering analysis utilizing
gene expression programming and Bayesian
probabilistic method‖, Journal of Rock
Mechanics and Geotechnical Engineering,
2017, 9(4): 683-693
10.Pirhadi, N., Tang, X., Yang, Q., Kang, F. ―A
New Equation to Evaluate Liquefaction
Triggering Using the Response Surface
Method and Parametric Sensitivity
Analysis‖, Sustainability; 2018, 11(1): 1-14.
11.Hu, J., Liu, H. ―Identification of ground
motion intensity measure and its application
for predicting soil liquefaction potential
based on the Bayesian network method‖,
Engineering geology; 2019, 248: 34-49.
12.Rahbarzare, A., Azadi, M. ―Improving
prediction of soil liquefaction using hybrid
optimization algorithms and a fuzzy support
vector machine‖, Bulletin of Engineering
Geology and the Environment; 2019, 78:
4977–4987.
13.Zhang, J., Wang, Y. ―An ensemble method
to improve prediction of earthquake-induced
soil liquefaction: a multi-dataset study‖,
Neural Computing and Applications; 2020,
33: 1533–1546.
14.Batzel, T. D., Lee, K.Y. ―A diagonally
recurrent neural network approach to
sensorless operation of the permanent
magnet synchronous motor‖, IEEE Power
Engineering Society Summer Meeting;
2000, 4: 2441-2445.<br style=" font-style: normal; font-variant: normal; font-weight: normal; letter-spacing: normal; line-height: normal; orphans: 2; text-align: -webkit-auto; text-indent: 0px; text-transform: none; white-space: normal; widows: 2; word-spacing: 0px; -webkit-text-size-adjust: auto; -web
15.Bhushan, B., Singh, M., Hage, Y.
―Identification and control using MLP,
Elman, NARXSP and radial basis function
networks: a comparative analysis‖, Artificial
Intellengce Review; 2012, 37: 133–156.
16.Haykin, S. ―Neural Networks, a
Comprehensive Foundation‖, Macmillan
College Publishing Co., New York, USA,
1999.
17.Hsu, K., Gupta, H. V. ―Sorooshian S.
Artificial neural network modeling of the
rainfall-runoff process‖, Water Resource
Research; 1995, 31(10): 2517–2530.
18.Ghritlahre, H. K., Prasad, R. K. ―Application
of ANN technique to predict the
performance of solar collector systems – a
review‖, renew. Sustain Energy Revew;
2018, 84: 75–88.
19.Wilamowski, B. M., Chen, Y., Malinowski,
A. ―Efficient algorithm for training neural
networks with one hidden layer‖,
International Joint Conference on Neural
Networks, Washington, DC, USA City,
1999, 1725–1728.
20.Hagan, M. T., Menhaj, M. B. ―Training
feedforward networks with the Marquardt
algorithm‖, IEEE Transactions on Neural
Networks; 1994, 5: 989–993.
21.Zendehboudi, A., Tatar, A. ―Utilization of
the RBF network to model the nucleate pool
boiling heat transfer properties of
refrigerantoil mixtures with nanoparticles‖ J.
Mol. Liq.; 2017, 247: 304–312.
22.Howlett, R. J., Jain, L. D. ―Radial basis
function networks: 1. Recent developments
in theory and applications‖, Heidelberg
(Germany): Physica-Verlag, A SpringerVerlag Company, (2001).
23.Howlett, R. J., Jain, L. D. ―Radial basis
function networks: 2. Recent developments
in theory and applications‖, Heidelberg
(Germany): Physica-Verlag, A SpringerVerlag Company, (2001).
24.Bortman, M., Aladjem, M. ―A Growing and
Pruning Method for Radial Basis Function
Networks‖, IEEE Transactions on Neural
Networks; 2009, 20(6): 1039-1045.
25.Koza J. R. ―Genetic Programming: On the
Programming of Computers by Means of
Natural Selection‖ MIT Press, Cambridge,
MA. (1992).
26.Ferreira C. ―Gene expression programming:
a new adaptive algorithm for solving
problems‖, Complex Systems; 2001, 13(2):
87–129.
27.Ferreira, C. ―Gene Expression
Programming: Mathematical Modeling by
an Artificial Intelligence‖, Springer-Verlag,
Germany, 2th ed., 2006, 478.
28.Lopez, H. S., Weinert, W. R. ―An Enhanced
Gene Expression Programming Approach
for Symbolic Regression Problems‖,
International Journal of Applied
Mathematics in Computer Science; 2004,
14: 375-384.
29.Ferreira, C. ―A Quick Introduction to Gene
Expression Programming. From GEP
Tutorials: A Gepsoft Web Resource‖,
http://www.gene-expression-programming,
2010.
30.Sahebkaram Alamdari, A., Najafi. A. ―The
Study of the Liquefaction Probability and
Estimation of the Relative Importance of
Effective Parameters Using Fuzzy
Clustering and Genetic Programming‖,
Journal of Civil and Environmental
Engineering; 2018, 47(4): 37-46.
31.Cetin, K. O., Seed, R. B., Kayen, R. E.,
Moss, R. E. S., Bilge, H. T., Ilgac, M.,
Chowdhury, K. ―Summary of SPT based
field case history data of Cetin 2016
Database‖, Report No: METU / GTENG
08/16-01. (2016).
32.Ferreira, C. ―Gene expression programming
and the evolution of computer programs. In:
Castro, L.N.D., Zuben, F.J.V. (Eds.)‖,
Recent Developments in Biologically
Inspired Computing; 2004, 82–103.
33.Alkroosh, I., Nikraz, H. ―Predicting axial
capacity of driven piles in cohesive soils
using intelligent computing‖, Engineering
Applying Artificial Intelligence; 2011,
25(3): 618-627.
34.Ahangari, K., Moeinossadat, S. R., Behnia
D. ―Estimation of tunnelling-induced
settlement by modern intelligent methods‖,
Soils and Foundations; 2015, 55(4): 737–
748.<br style=" font-style: normal; font-variant: normal; font-weight: normal; letter-spacing: normal; line-height: normal; orphans: 2; text-align: -webkit-auto; text-indent: 0px; text-transform: none; white-space: normal; widows: 2; word-spacing: 0px; -web
