Hostname: page-component-78c5997874-fbnjt Total loading time: 0 Render date: 2024-11-10T13:33:29.916Z Has data issue: false hasContentIssue false
  • English
  • Français

Developing a new hybrid soft computing technique in predicting ultimate pile bearing capacity using cone penetration test data

Published online by Cambridge University Press:  30 January 2020

Hooman Harandizadeh Open the ORCID record for Hooman Harandizadeh [Opens in a new window]
Hooman Harandizadeh*
Affiliation:
Department of Civil Engineering, Faculty of Engineering, Shahid Bahonar University of Kerman, Pajoohesh Sq., Imam Khomeni Highway, Post Office Box: 76169133, Kerman7616913439, Iran
*
Author for correspondence: Hooman Harandizadeh, E-mail: hoomanharandizadeh@eng.uk.ac.ir
Get access Rights & Permissions [Opens in a new window]

Abstract

This research intends to investigate a new hybrid artificial intelligence (AI) technique compared to some common CPT methods in estimating axial ultimate pile bearing capacity (UPBC) using cone penetration test (CPT) data in geotechnical engineering applications. A data series of 108 samples was collected in order to develop a new hybrid structure of an adaptive neuro-fuzzy inference system (ANFIS) network, and the group method of the data handling (GMDH) type neural network was optimized by applying the particle swarm optimization (PSO) algorithm over the hybrid ANFIS-GMDH topology, which leads to a new hybrid AI model called as ANFIS-GMDH-PSO. The derived database provides information related to pile load tests, in situ field CPT data, and soil–pile information for introducing the proposed hybrid neural system. The cross-section of the pile toe, average cone tip resistance along embedded pile length, and sleeve frictional resistance along the shaft had been considered as input parameters for the proposed network. The results of this research indicated that the proposed ANFIS-GMDH-PSO model predicted the UPBC with an acceptable precision compared to various CPT methods, including Schmertmann, De Kuiter & Bringen, and LPC/LPCT methods. Moreover, ANFIS-GMDH-PSO network model performance was compared to CPT-based models in terms of statistical criteria in order to achieve a best fitted model. From the statistical results, it was found that the developed ANFIS-GMDH-PSO model has achieved a higher accuracy level in terms of statistical indices compared to CPT-based empirical methods, such as Schmertmann model, De Kuiter & Beringen model, and Bustamante & Gianeselli for predicting driven pile ultimate bearing capacity.

Keywords

ANFIS algorithmCPT-based modelsGMDH type neural networkpile bearing capacityPSO algorithm
Type
Research Article
Information
AI EDAM , Volume 34 , Issue 1: Thematic Collection on “Cognitive and Learning processes for transition to Design for Sustainability (DfS)” , February 2020 , pp. 114 - 126
Copyright
Copyright © Cambridge University Press 2020

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Abu-Farsakh, MY and Titi, HH (2004) Assessment of direct cone penetration test methods for predicting the ultimate capacity of friction driven piles. Journal of Geotechnical and Geoenvironmental Engineering 130, 935–944.CrossRefGoogle Scholar
Abu-Kiefa, M (1998) General regression neural networks for driven piles in cohesionless soils. Journal of Geotechnical and Geoenvironmental Engineering 124, 1177–1185.CrossRefGoogle Scholar
Adeniran, AA and El-Ferik, S (2017) A reinforced combinatorial particle swarm optimization based multimodel identification of nonlinear systems. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 31, 327–358.CrossRefGoogle Scholar
Alkroosh, IS and Nikraz, H (2011) Correlation of pile axial capacity and CPT data using gene expression programming. Geotechnical and Geological Engineering 29, 725–748.CrossRefGoogle Scholar
Alkroosh, IS and Nikraz, H (2012) Predicting axial capacity of driven piles in cohesive soils usingintelligent computing. Engineering Applications of Artificial Intelligence 25, 618–627.CrossRefGoogle Scholar
Al-Refaie, A, Judeh, M and Li, M-H (2018) Optimal fuzzy scheduling and sequencing of multiple-period operating room. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 32, 108–121.CrossRefGoogle Scholar
Armaghani, DJ, Raja, RSNSB, Faizi, K and Rashid, ASA (2017) Developing a hybrid PSO–ANN model for estimating the ultimate bearing capacity of rock-socketed piles. Neural Computing and Applications 28, 391–405.CrossRefGoogle Scholar
Armaghani, DJ, Faradonbeh, RS, Rezaei, H, Rashid, ASA and Amnieh, HB (2018) Settlement prediction of the rock-socketed piles through a new technique based on gene expression programming. Neural Computing and Applications 29, 1115–1125.CrossRefGoogle Scholar
Begemann, HP (1963) The use of the static soil penetrometer in Holland. New Zealand Engineering 18.Google Scholar
Bustamante, M and Gianeselli, L (1982) Pile bearing capacity prediction by means of static penetrometer CPT. In Proceedings of the 2-nd European symposium on penetration testing, pp. 493–500.Google Scholar
Coello, CAC and Aguirre, AH (2002) Design of combinational logic circuits through an evolutionary multiobjective optimization approach. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 16, 39–53.CrossRefGoogle Scholar
De Kuiter, J and Beringen, F (1979) Pile foundations for large North Sea structures. Marine Georesources & Geotechnology 3, 267–314.CrossRefGoogle Scholar
Eberhart, R and Kennedy, J (1995) A new optimizer using particle swarm theory. In MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science, 4–6 Oct, IEEE, Nagoya, Japan, pp. 39–43. DOI: 10.1109/MHS.1995.494215.CrossRefGoogle Scholar
Ebrahimian, B and Movahed, V (2016) Application of an evolutionary-based approach in evaluating pile bearing capacity using CPT results. Ships and Offshore Structures 12, 1–17.Google Scholar
Elloumi, M, Krid, M and Masmoudi, DS (2015) Neuro-fuzzy system based on particle swarm optimization algorithm for image denoising application. Paper presented at the Advances in Biomedical Engineering (ICABME), 2015 International Conference on.CrossRefGoogle Scholar
Eslami, A (1997) Bearing Capacity of Piles From Cone Penetration Test Data. Canada: University of Ottawa.Google Scholar
Faizi, K, Armaghani, DJ, Sohaei, H, Rashid, ASA and Nazir, R (2015) Deformation model of sand around short piles under pullout test. Measurement 63, 110–119.CrossRefGoogle Scholar
Hanna, S (2007) Inductive machine learning of optimal modular structures: estimating solutions using support vector machines. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 21, 351–366.CrossRefGoogle Scholar
Harandizadeh, H, Toufigh, MM and Toufigh, V (2018) Application of improved ANFIS approaches to estimate bearing capacity of piles. Soft Computing 23, 1–13.Google Scholar
Haykin, S, Nie, J and Currie, B (1999) Neural network-based receiver for wireless communications. Electronics Letters 35, 203–205.CrossRefGoogle Scholar
Hu, Y-J, Wang, Y, Wang, Z-L, Wang, Y-Q and Zhang, B-C (2014) Machining scheme selection based on a new discrete particle swarm optimization and analytic hierarchy process. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 28, 71–82.CrossRefGoogle Scholar
Kalantary, F, Ardalan, H and Nariman-Zadeh, N (2009) An investigation on the Su–NSPT correlation using GMDH type neural networks and genetic algorithms. Engineering Geology 104, 144–155.CrossRefGoogle Scholar
Keles, HY (2018) Embedding parts in shape grammars using a parallel particle swarm optimization method on graphics processing units. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 32, 256–268.CrossRefGoogle Scholar
Kennedy, J and Eberhart, R (1995) Perth, Australia. In Proc. IEEE International Conference on Neural Networks.Google Scholar
Kiefa, MA (1998) General regression neural networks for driven piles in cohesionless soils. Journal of Geotechnical and Geoenvironmental Engineering 124, 1177–1185.CrossRefGoogle Scholar
Kondner, RL (1963) Hyperbolic stress-strain response: cohesive soils. Journal of the Soil Mechanics and Foundations Division 89, 115–144.Google Scholar
Kontovourkis, O, Phocas, MC and Lamprou, I (2015) Adaptive kinetic structural behavior through machine learning: optimizing the process of kinematic transformation using artificial neural networks. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 29, 371–391.CrossRefGoogle Scholar
Koopialipoor, M, Nikouei, SS, Marto, A, Fahimifar, A, Jahed Armaghani, D and Mohamad, ET (2018) Predicting tunnel boring machine performance through a new model based on the group method of data handling. Bulletin of Engineering Geology and the Environment 78, 1–14.Google Scholar
Kordjazi, A, Nejad, FP and Jaksa, M (2014) Prediction of ultimate axial load-carrying capacity of piles using a support vector machine based on CPT data. Computers and Geotechnics 55, 91–102.CrossRefGoogle Scholar
Lin, C-J and Huang, M-L (2018) Efficient hybrid group search optimizer for assembling printed circuit boards. Artificial Intelligence for Engineering Design, Analysis and Manufacturing, 1–16. doi:10.1017/S0890060418000240Google Scholar
Mirabi, M and Seddighi, P (2018) Hybrid ant colony optimization for capacitated multiple-allocation cluster hub location problem. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 32, 44–58.CrossRefGoogle Scholar
Moayedi, H and Armaghani, DJ (2018) Optimizing an ANN model with ICA for estimating bearing capacity of driven pile in cohesionless soil. Engineering with Computers 34, 347–356.CrossRefGoogle Scholar
Mola-Abasi, H and Eslami, A (2018) Prediction of drained soil shear strength parameters of marine deposit from CPTu data using GMDH-type neural network. Marine Georesources and Geotechnology 37, 1–10.Google Scholar
Momeni, E, Nazir, R, Jahed Armaghani, D and Maizir, H (2014) Prediction of pile bearing capacity using a hybrid genetic algorithm-based ANN. Measurement 57, 122–131.CrossRefGoogle Scholar
Momeni, E, Nazir, R, Armaghani, DJ and Maizir, H (2015) Application of artificial neural network for predicting shaft and tip resistances of concrete piles. Earth Sciences Research Journal 19, 85–93.CrossRefGoogle Scholar
Momeni, E, Armaghani, DJ, Fatemi, SA and Nazir, R (2018) Prediction of bearing capacity of thin-walled foundation: a simulation approach. Engineering with Computers 34, 319–327.CrossRefGoogle Scholar
Naeinia, SA, Ziaie Moayeda, R, Kordnaeija, A and Mola-Abasib, H (2018) Elasticity modulus of clayey deposits estimation using Group Method of Data Handling type neural network. Measurement 121, 335–343.CrossRefGoogle Scholar
Najafzadeh, M (2015) Neuro-fuzzy GMDH based evolutionary algorithms to predict the scour pile under clear-water condition. Ocean Engineering 99, 85–94.CrossRefGoogle Scholar
Najafzadeh, M and Azamathulla, HM (2013) Neuro-fuzzy GMDH systems to predict the scour pile groups due to waves. Journal of Computing in Civil Engineering. doi:10.1061/(ASCE)CP.1943-5487.0000376.Google Scholar
Najafzadeh, M and Barani, GA (2011) Comparison of group method of data handling based genetic programming and back propagation systems to predict scour depth around bridge piers. Scientia Iranica Transactions A: Civil Engineering 18, 1207–1213.CrossRefGoogle Scholar
Najafzadeh, M and Lim, S-Y (2015) Application of improved neuro-fuzzy GMDH to predict scour downstream of sluice gates. Earth Science Informatics 8, 187–196.CrossRefGoogle Scholar
Najafzadeh, M and Saberi-Movahed, F (2019) GMDH-GEP to predict free span expansion rates below pipelines under waves. Marine Georesources & Geotechnology 37, 375–392.CrossRefGoogle Scholar
Najafzadeh, M and Tafarojnoruz, A (2016) Neuro-fuzzy GMDH based particle swarm optimization to predict longitudinal dispersion coefficients in rivers. Environmental Earth Sciences 75, 1–16.CrossRefGoogle Scholar
Najafzadeh, M, Barani, GA and Azamathulla, HM (2013a) GMDH to predict scour depth around a pier in cohesive soils. Applied Ocean Research.40, 35–41.CrossRefGoogle Scholar
Najafzadeh, M, Barani, GA and Hessami-Kermani, MR (2013b) Group method of data handling to predict scour depth around vertical piles under regular waves. Scientia Iranica 20(3), 406–413.Google Scholar
Najafzadeh, M, Barani, GA and Azamathulla, HM (2014) Prediction of pipeline scour depth in clear-water and live-bed conditions using GMDH. Neural Computing and Applications 24, 629–635.CrossRefGoogle Scholar
Najafzadeh, M, Barani, GA and Hessami-Kermani, MR (2015) Evaluation of GMDH networks for prediction of local scour depth at bridge abutments in coarse sediments with thinly armored beds. Ocean Engineering 104, 387–396.CrossRefGoogle Scholar
Najafzadeh, M, Saberi-Movahed, F and Sarkamaryan, S (2018) NF-GMDH-Based self-organized systems to predict bridge pier scour depth under debris flow effects. Marine Georesources and Geotechnology 36, 589–602.CrossRefGoogle Scholar
Nariman-Zadeh, N, Darvizeh, A and Dadfarmai, MH (2003) Adaptive neurofuzzy inference systems networks design using hybrid genetic and singular value decomposition methods for modeling and prediction of the explosive cutting process. Artificial Intelligence for Engineering Design Analysis and Manufacturing 17, 313–324.CrossRefGoogle Scholar
Nejad, FP, Jaksa, MB, Kakhi, M and McCabe, BA (2009) Prediction of pile settlement using artificial neural networks based on standard penetration test data. Computers and Geotechnics 36, 1125–1133.CrossRefGoogle Scholar
Niazi, FS and Mayne, PW (2013) Cone penetration test based direct methods for evaluating static axial capacity of single piles. Geotechnical and Geological Engineering 31, 979–1009.CrossRefGoogle Scholar
Padmini, D, Ilamparuthi, K and Sudheer, K (2008) Ultimate bearing capacity prediction of shallow foundations on cohesionless soils using neurofuzzy models. Computers and Geotechnics 35, 33–46.CrossRefGoogle Scholar
Persson, JA and Ölvander, J (2017) How to compare performance of robust design optimization algorithms, including a novel method. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 31, 286–297.CrossRefGoogle Scholar
Poli, R, Kennedy, J and Blackwell, T (2007) Particle swarm optimization. Swarm intelligence 1, 33–57.CrossRefGoogle Scholar
Rossi, A and Lanzetta, M (2013) Non-permutation flow line scheduling by ant colony optimization. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 27, 349–357.CrossRefGoogle Scholar
Salido, MA, Escamilla, J, Barber, F, Giret, A, Tang, D and Dai, M (2015) Energy efficiency, robustness, and make span optimality in job-shop scheduling problems. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 30, 300–312.CrossRefGoogle Scholar
Schmertmann, JH (1978) Guidelines for cone penetration test: performance and design. United States. Federal Highway Administration. Office of Research and Development https://rosap.ntl.bts.gov/view/dot/958.Google Scholar
Shaghaghi, S, Bonakdari, H, Gholami, A, Ebtehaj, I and Zeinolabedini, M (2017) Comparative analysis of GMDH neural network based on genetic algorithm and particle swarm optimization in stable channel design. Applied Mathematics and Computation 313, 271–286.CrossRefGoogle Scholar
Shahin, MA (2010) Intelligent computing for modeling axial capacity of pile foundations. Canadian Geotechnical Journal 47, 230–243.CrossRefGoogle Scholar
Takagi, T and Sugeno, M (1993) A fuzzy logic approach to qualitative modelling. IEEE Trans. Fuzzy Systems 1, 7–31Google Scholar
Thimmisetty, C, Tsilifis, P and Ghanem, R (2017) Homogeneous chaos basis adaptation for design optimization under uncertainty: application to the oil well placement problem. Artificial Intelligence for Engineering, Design Analysis and Manufacturing 31, 265–276.CrossRefGoogle Scholar
Zadeh, LA (1996) In Fuzzy Sets, Fuzzy Logic, And Fuzzy Systems. Binghamton, New york, USA: Binghamton University-SUNY, pp. 394–432.CrossRefGoogle Scholar
Zeng, K, Tan, Z, Dong, M and Yang, P (2014) Probability increment based swarm optimization for combinatorial optimization with application to printed circuit board assembly. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 28, 429–437.CrossRefGoogle Scholar
Zhang, Z, Ding, D, Rao, L and Bi, Z (2006) An ANFIS based approach for predicting the ultimate bearing capacity of single piles. In Foundation Analysis and Design: Innovative Methods, pp. 159–166CrossRefGoogle Scholar
Zimmermann, L, Chen, T and Shea, K (2018) A 3D, performance-driven generative design framework: automating the link from a 3D spatial grammar interpreter to structural finite element analysis and stochastic optimization. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 32, 189–199.CrossRefGoogle Scholar