Estimation of pile settlement applying hybrid ALO-MLP and GOA-MLP approaches

Jin-Kou Hu

doi:10.6180/jase.202212_25(6).0019

Estimation of pile settlement applying hybrid ALO-MLP and GOA-MLP approaches

Computer Science and Information Engineering

Jin-kou Hu This email address is being protected from spambots. You need JavaScript enabled to view it.¹

¹Hebei Software Institute, Baoding 071000, China

Received: November 3, 2022
Accepted: January 1, 2022
Publication Date: March 1, 2022

Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

Download Citation: ||https://doi.org/10.6180/jase.202212_25(6).0019

ABSTRACT

Pile settlement (SP) socketed to rock has taken vital regard. Despite introducing some design methods to measure SP, applying the novel and efficient prediction model with satisfactory performance is pivotal. The main goal of this study is to find out the applicability of applying two hybrid multi-layer perceptron neural network (MLP) models in predicting the SP in the Klang Valley Mass Rapid Transit (KVMRT) project constructed operated in Kuala Lumpur, Malaysia. Various hidden layers of models were examined to have comprehensive, accurate and reliable outputs. Ant lion optimizer (ALO) and grasshopper optimization algorithm (GOA) was applied to identify each hidden layer’s optimal number of neurons. In this case, five parameters were considered as input variables and SP as output. Regarding ALO-MLP models, ALO-MLP1 has the lowest score (48), with R² stood at 0.9382 and 0.93, and PI at 0.0416 and 0.0494 for the training and testing phases, respectively. In the training phase, best values of R², RMSE and PI were belonged to MLP1, while MLP2 has the smallest value of MAE. However, in the testing phase, MLP model with two hidden layer has best values for all indices, which makes it the proposed MLP model with two hidden layers. The results show that ALO is more capable than GOA for determining the optimal neuron numbers of MLP. By summation of the ranking scores obtained from performance evaluation indices, although GOA-MLP models have acceptable performance, two layers of MLP optimized with ALO could be recognized as the proposed model.

Keywords: Pile Settlement; Prediction; Multi-layer perceptron neural network; Optimization algorithms

REFERENCES

[1] M. Esmaeili-Falak and M. Hajialilue-Bonab, (2012) “Numerical studying the effects of gradient degree on slope stability analysis using limit equilibrium and finite element methods" Int J Acad Res 4(4): 216–22.
[2] P. Carrubba, (1997) “Skin friction of large-diameter piles socketed into rock" Canadian Geotechnical Journal 34(2): 230–240. DOI: 10.1139/t96-104.
[3] M. Esmaeili Falak, R. Sarkhani Benemaran, and R. Seifi, (2020) “Improvement of the mechanical and durability parameters of construction concrete of the Qotursuyi Spa" Concrete Research 13(2): 119–134.
[4] R. Sarkhani Benemaran, (2017) “Experimental and analytical study of pile-stabilized layered slopes" Tabriz: University of Tabriz:
[5] M. Esmaeili-Falak, H. Katebi, and A. Javadi, (2018) “Experimental study of the mechanical behavior of frozen soils - A case study of Tabriz Subway" Periodica Polytechnica Civil Engineering 62(1): 117–125. DOI: 10.3311/PPci.10960.
[6] C. Ng, T. Yau, J. Li, and W. Tang, (2001) “Side resistance of large diameter bored piles socketed into decomposed rocks" Journal of Geotechnical and Geoenvironmental Engineering 127(8): 642–657. DOI: 10.1061/(ASCE)1090-0241(2001)127:8(642).
[7] M. Esmaeili-Falak. “Effect of system’s geometry on the stability of frozen wall in excavation of saturated granular soils". (phdthesis). Doctoral dissertation, University of Tabriz Tabriz, Iran, 2017.
[8] R. Sarkhani Benemaran, M. Esmaeili-Falak, and H. Katebi, (2021) “Physical and numerical modelling of pilestabilised saturated layered slopes" Proceedings of the Institution of Civil Engineers: Geotechnical Engineering: DOI: 10.1680/jgeen.20.00152.
[9] A. Poorjafar, M. Esmaeili-Falak, and H. Katebi, (2021) “Pile-soil interaction determined by laterally loaded fixed head pile group" Geomechanics and Engineering 26(1): 13–25. DOI: 10.12989/gae.2021.26.1.013.
[10] M. Esmaeili-Falak, H. Katebi, A. Javadi, and S. Rahimi, (2017) “Experimental investigation of stress and strain characteristics of frozen sandy soils-A case study of Tabriz subway" Modares Civil Engineering journal 17(5): 13–23.
[11] M. Esmaeili-Falak, H. Katebi, and A. Javadi, (2020) “Effect of freezing on stress-strain characteristics of granular and cohesive soils" Journal of Cold Regions Engineering 34(2): DOI: 10.1061/(ASCE)CR.1943-5495.0000205.
[12] P. Le Tirant, (1992) “Design guides for offshore structures: Offshore pile design":
[13] R. Rowe and H. Armitage. “A new design method for drilled piers in soft rock: Implications relating to three published case histories”. In: cited By 1. 1987, 497–502.
[14] F. Pooya Nejad, M. Jaksa, M. Kakhi, and B. McCabe, (2009) “Prediction of pile settlement using artificial neural networks based on standard penetration test data" Computers and Geotechnics 36(7): 1125–1133. DOI: 10.1016/j.compgeo.2009.04.003.
[15] A. Soleimanbeigi and N. Hataf, (2006) “Prediction of settlement of shallow foundations on reinforced soils using neural networks" Geosynthetics International 13(4): 161–170. DOI: 10.1680/gein.2006.13.4.161.
[16] M. Shahin, H. Maier, and M. Jaksa, (2002) “Predicting settlement of shallow foundations using neural networks" Journal of Geotechnical and Geoenvironmental Engineering 128(9): 785–793. DOI: 10.1061/(ASCE)1090-0241(2002)128:9(785).
[17] M. Najafzadeh, G.-A. Barani, and M.-R. Hessami-Kermani, (2013) “Group method of data handling to predict scour depth around vertical piles under regular waves" Scientia Iranica 20(3): 406–413. DOI: 10.1016/j.scient.2013.04.005.
[18] M. Najafzadeh and G.-A. Barani, (2011) “Comparison of group method of data handling based genetic programming and back propagation systems to predict scour depth around bridge piers" Scientia Iranica 18(6): 1207–1213. DOI: 10.1016/j.scient.2011.11.017.
[19] M. Najafzadeh, M. Balf, and E. Rashedi, (2016) “Prediction of maximum scour depth around piers with debris accumulation using EPR, MT, and GEP models" Journal of Hydroinformatics 18(5): 867–884. DOI: 10.2166/hydro.2016.212.
[20] M. Najafzadeh, G.-A. Barani, and M. Kermani, (2013) “Abutment scour in clear-water and live-bed conditions by GMDH network"Water Science and Technology 67(5): 1121–1128. DOI: 10.2166/wst.2013.670.
[21] M. Esmaeili-Falak, H. Katebi, M. Vadiati, and J. Adamowski, (2019) “Predicting Triaxial Compressive Strength and Young’s Modulus of Frozen Sand Using Artificial Intelligence Methods" Journal of Cold Regions Engineering 33(3): DOI: 10.1061/(ASCE)CR.1943-5495.0000188.
[22] A. Nassr, M. Esmaeili-Falak, H. Katebi, and A. Javadi, (2018) “A new approach to modeling the behavior of frozen soils" Engineering Geology 246: 82–90. DOI: 10.1016/j.enggeo.2018.09.018.
[23] H. Rezaei, R. Nazir, and E. Momeni, (2016) “Bearing capacity of thin-walled shallow foundations: An experimental and artificial intelligence-based study" Journal of Zhejiang University: Science A 17(4): 273–285. DOI: 10.1631/jzus.A1500033.
[24] S. Yagiz, E. Sezer, and C. Gokceoglu, (2012) “Artificial neural networks and nonlinear regression techniques to assess the influence of slake durability cycles on the prediction of uniaxial compressive strength and modulus of elasticity for carbonate rocks" International Journal for Numerical and Analytical Methods in Geomechanics 36(14): 1636–1650. DOI: 10.1002/nag.1066.
[25] E. Momeni, R. Nazir, D. Armaghani, and H. Maizir, (2015) “Application of artificial neural network for predicting shaft and tip resistances of concrete piles" Earth Sciences Research Journal 19(1): 85–93. DOI: 10.15446/esrj.v19n1.38712.
[26] M. Khandelwal and T. Singh, (2007) “Evaluation of blast-induced ground vibration predictors" Soil Dynamics and Earthquake Engineering 27(2): 116–125. DOI: 10.1016/j.soildyn.2006.06.004.
[27] D. Jahed Armaghani, M. Mohd Amin, S. Yagiz, R. Faradonbeh, and R. Abdullah, (2016) “Prediction of the uniaxial compressive strength of sandstone using various modeling techniques" International Journal of Rock Mechanics and Mining Sciences 85: 174–186. DOI: 10.1016/j.ijrmms.2016.03.018.
[28] M. Pal and S. Deswal, (2010) “Modelling pile capacity using Gaussian process regression" Computers and Geotechnics 37(7-8): 942–947. DOI: 10.1016/j.compgeo.2010.07.012.
[29] P. Samui, (2019) “Determination of Friction Capacity of Driven Pile in Clay Using Gaussian Process Regression (GPR), and Minimax Probability Machine Regression (MPMR)" Geotechnical and Geological Engineering 37(5): 4643–4647. DOI: 10.1007/s10706-019-00928-8.
[30] E. Momeni, M. Dowlatshahi, F. Omidinasab, H. Maizir, and D. Armaghani, (2020) “Gaussian Process Regression Technique to Estimate the Pile Bearing Capacity" Arabian Journal for Science and Engineering 45(10): 8255–8267. DOI: 10.1007/s13369-020-04683-4.
[31] W. Zhang and A. Goh, (2013) “Multivariate adaptive regression splines for analysis of geotechnical engineering systems" Computers and Geotechnics 48: 82–95. DOI: 10.1016/j.compgeo.2012.09.016.
[32] R. Benemaran and M. Esmaeili-Falak, (2020) “Optimization of cost and mechanical properties of concrete with admixtures using MARS and PSO" Computers and Concrete 26(4): 309–316. DOI: 10.12989/cac.2020.26.4.309.
[33] L. Teodorescu and D. Sherwood, (2008) “High Energy Physics event selection with Gene Expression Programming" Computer Physics Communications 178(6): 409–419. DOI: 10.1016/j.cpc.2007.10.003.
[34] T.-T. Le and M. Le, (2021) “Development of user-friendly kernel-based Gaussian process regression model for prediction of load-bearing capacity of square concretefilled steel tubular members" Materials and Structures/Materiaux et Constructions 54(2): DOI: 10.1617/s11527-021-01646-5.
[35] I. Alkroosh and H. Nikraz, (2011) “Correlation of Pile Axial Capacity and CPT Data Using Gene Expression Programming" Geotechnical and Geological Engineering 29(5): 725–748. DOI: 10.1007/s10706- 011-9413-1.
[36] A. Mollahasani, A. Alavi, and A. Gandomi, (2011) “Empirical modeling of plate load test moduli of soil via gene expression programming" Computers and Geotechnics 38(2): 281–286. DOI: 10.1016/j.compgeo.2010.11.008.
[37] A. Ozbek, M. Unsal, and A. Dikec, (2013) “Estimating uniaxial compressive strength of rocks using genetic expression programming" Journal of Rock Mechanics and Geotechnical Engineering 5(4): 325–329. DOI: 10.1016/j.jrmge.2013.05.006.
[38] S. Dindarloo, (2015) “Prediction of blast-induced ground vibrations via genetic programming" International Journal of Mining Science and Technology 25(6): 1011–1015. DOI: 10.1016/j.ijmst.2015.09.020.
[39] S. Alemdag, Z. Gurocak, A. Cevik, A. Cabalar, and C. Gokceoglu, (2016) “Modeling deformation modulus of a stratified sedimentary rock mass using neural network, fuzzy inference and genetic programming" Engineering Geology 203: 70–82. DOI: 10.1016/j.enggeo.2015.12.002.
[40] D. Armaghani, R. Faradonbeh, H. Rezaei, A. Rashid, and H. Amnieh, (2018) “Settlement prediction of the rock-socketed piles through a new technique based on gene expression programming" Neural Computing and Applications 29(11): 1115–1125. DOI: 10.1007/s00521-016-2618-8.
[41] D. Armaghani, P. Asteris, S. Fatemi, M. Hasanipanah, R. Tarinejad, A. Rashid, and V. Huynh, (2020) “On the use of neuro-swarm system to forecast the pile settlement" Applied Sciences (Switzerland) 10(6): DOI: 10.3390/app10061904.
[42] A. Saffari, S. Zahiri, and M. Khishe, (2022) “Fuzzy Grasshopper Optimization Algorithm: A Hybrid Technique for Tuning the Control Parameters of GOA Using Fuzzy System for Big Data Sonar Classification" Iranian Journal of Electrical and Electronic Engineering 18(1): 2131–2131.
[43] W. Liu, H. Moayedi, H. Nguyen, Z. Lyu, and D. Bui, (2021) “Proposing two new metaheuristic algorithms of ALO-MLP and SHO-MLP in predicting bearing capacity of circular footing located on horizontal multilayer soil" Engineering with Computers 37(2): 1537–1547. DOI: 10.1007/s00366-019-00897-9.
[44] M. Colak, M. Yesilbudak, and R. Bayindir, (2020) “Daily photovoltaic power prediction enhanced by hybrid GWO-MLP, ALO-MLP and WOA-MLP models using meteorological information" Energies 13(4): DOI: 10.3390/en13040901.
[45] X. Ma, L. Foong, A. Morasaei, A. Ghabussi, and Z. Lyu, (2020) “Swarm-based hybridizations of neural network for predicting the concrete strength" Smart Structures and Systems 26(2): 241–251. DOI: 10.12989/sss.2020.26.2.241.
[46] H. Moayedi, M. Gör, Z. Lyu, and D. Bui, (2020) “Herding Behaviors of grasshopper and Harris hawk for hybridizing the neural network in predicting the soil compression coefficient" Measurement: Journal of the International Measurement Confederation 152: DOI: 10.1016/j.measurement.2019.107389.
[47] A.W. Hatheway. The complete ISRM suggested methods for rock characterization, testing and monitoring; 1974–2006. 2009.
[48] S. Mirjalili, (2015) “The ant lion optimizer" Advances in Engineering Software 83: 80–98. DOI: 10.1016/j.advengsoft.2015.01.010.
[49] S. Simpson, A. McCaffery, and B. Hägele, (1999) “A behavioural analysis of phase change in the desert locust" Biological Reviews of the Cambridge Philosophical Society 74(4): 461–480. DOI: 10.1017/S000632319900540X.
[50] S. Saremi, S. Mirjalili, and A. Lewis, (2017) “Grasshopper Optimisation Algorithm: Theory and application" Advances in Engineering Software 105: 30–47. DOI: 10.1016/j.advengsoft.2017.01.004.
[51] M. Mafarja, I. Aljarah, H. Faris, A. Hammouri, A. Al-Zoubi, and S. Mirjalili, (2019) “Binary grasshopper optimisation algorithm approaches for feature selection problems" Expert Systems with Applications 117: 267–286. DOI: 10.1016/j.eswa.2018.09.015.
[52] M. Mafarja, I. Aljarah, A. Heidari, A. Hammouri, H. Faris, A. Al-Zoubi, and S. Mirjalili, (2018) “Evolutionary Population Dynamics and Grasshopper Optimization approaches for feature selection problems" Knowledge-Based Systems 145: 25–45. DOI: 10.1016/j.knosys.2017.12.037.
[53] Y. Gad, H. Diab, M. Abdelsalam, and Y. Galal, (2020) “Smart energy management system of environmentally friendly microgrid based on grasshopper optimization technique" Energies 13(18): DOI: 10.3390/en13195000.
[54] I. Basheer and M. Hajmeer, (2000) “Artificial neural networks: Fundamentals, computing, design, and application" Journal of Microbiological Methods 43(1): 3–31. DOI: 10.1016/S0167-7012(00)00201-3.
[55] M. Koopialipoor, D. Jahed Armaghani, M. Haghighi, and E. Ghaleini, (2019) “A neuro-genetic predictive model to approximate overbreak induced by drilling and blasting operation in tunnels" Bulletin of Engineering Geology and the Environment 78(2): 981–990. DOI: 10.1007/s10064-017-1116-2.
[56] B. Gordan, M. Koopialipoor, A. Clementking, H. Tootoonchi, and E. Tonnizam Mohamad, (2019) “Estimating and optimizing safety factors of retaining wall through neural network and bee colony techniques" Engineering with Computers 35(3): 945–954. DOI: 10.1007/s00366-018-0642-2.
[57] M. Najafzadeh, (2019) “Evaluation of conjugate depths of hydraulic jump in circular pipes using evolutionary computing" Soft Computing 23(24): 13375–13391. DOI: 10.1007/s00500-019-03877-9.
[58] F. Saberi-Movahed, M. Najafzadeh, and A. Mehrpooya, (2020) “Receiving More Accurate Predictions for Longitudinal Dispersion Coefficients in Water Pipelines: Training Group Method of Data Handling Using Extreme Learning Machine Conceptions" Water Resources Management 34(2): 529–561. DOI: 10.1007/s11269-019-02463-w.