PREDICTING GEOTECHNICAL AXIAL CAPACITY OF REINFORCED CONCRETE DRIVEN PILE USING MACHINE LEARNING TECHNIQUE
DOI:
https://doi.org/10.11113/mjce.v35.20544Keywords:
Pile Geotechnical Axial Capacity, Machine Learning, Skin Friction Factor, End Bearing Factor, StatisticsAbstract
Modified Meyerhof method is a popular method to calculate pile geotechnical axial capacity in Malaysia currently. From past experience, pile design based on empirical and analytical method produce variability of predicted capacity, in which, there is a wide scatter of predicted capacities and tendency for the predictions to be conservative, i.e. to underestimate the load capacity. This study provides options of machine learning and statistical approach for prediction of pile capacity based on soil investigation and dynamic pile load test result. It serves as an additional checking for engineer during design of pile based on conventional empirical method. It also helps to provide deeper insights of non-linear variables related to pile capacity through machine learning and statistical approach. This study helps engineer to design pile foundation optimally, economically and safely. The prediction of pile geotechnical axial capacity with machine learning technique and statistical approach for local marine clay soil in Penang, Malaysia is proposed in this study. The information from soil investigation report and dynamic pile load test report are gathered from six projects at Batu Kawan and Nibong Tebal located in Penang state that contributed 439 numbers of data. The skin friction factor, end bearing factor and pile geotechnical axial capacity are computed and predicted using empirical method, machine learning model and statistical model. Support Vector Machine illustrate best fit model for predicting skin friction factor with R2 of 0.517 while Random Forest seems to be the best fit model for predicting end bearing factor with R2 of 0.264. Random Forest is found to be the best model in predicting the geotechnical pile axial capacity compare to other models as it explains 96.2% of the variability of pile capacity.
References
American Society for Testing and Materials. 2012. Standard Test Method for High-Strain Dynamic Testing of Deep Foundations. Retrieved from https://doi.org/10.1520/D4945-12 Retrieve on July 12, 2023
Independent Media Associates. 2023. ANCOVA: Analysis of Covariance. Retrieved May 13, 2023, from https://www.statisticshowto.com/ancova/
Anthony T. C. Goh. 1994. Seismic Liquefaction Potential Assessed by Neural Networks. Journal of Geotechnical Engineering, 120(9): 1467–1480.
Benali, A., Boukhatem, B., Hussien, M. N., Nechnech, A., and Karray, M. 2017. Prediction of axial capacity of piles driven in non-cohesive soils based on neural networks approach. Journal of Civil Engineering and Management, 23(3): 393–408.
Benbouras, M. A., Petrişor, A. I., Zedira, H., Ghelani, L., and Lefilef, L. 2021. Forecasting the bearing capacity of the driven piles using advanced machine-learning techniques. Applied Sciences, 11(22): 10908
Carvalho, S., Sales, M., and Cavalcante, A. 2023. Systematic literature review and mapping of the prediction of pile capacities. Soils and Rocks, 46(3): 1-12
C.T. Toh, T.A. Ooi, H.K. Chiu, S.K. Chee, and W.H. Ting. 1989. Design parameter for bored piles in a weathered sedimentary formation. International Society For Soil Mechanics And Geotechnical Engineering. International Society For Soil Mechanics And Geotechnical Engineering, 13/7: 1073-1078.
Dynamic Pile Testing Sdn. Bhd. 2012. High Strain Dynamic Pile Testing Report.
Geological Survey Department of Malaysia. 1985. Geological Map of Peninsular Malaysia, 8th Edition, 1:750,000.
Gomes, Y. F., Verri, F. A. N., and Ribeiro, D. B. 2021. Use of machine learning techniques for predicting the bearing capacity of piles. Soils and Rocks, 44(4): 1--14
Harnedi Maizir and Khairul Anuar Kassim. 2013. International Multi Conference of Engineers and Computer Scientists: IMECS 2013: 13-15 March, 2013, the Royal Garden Hotel, Kowloon, Hong Kong. Newswood Ltd.
Jaksa, M., and Liu, Z. 2021. Editorial for Special Issue “Applications of Artificial Intelligence and Machine Learning in Geotechnical Engineering”. Geosciences, 11(10): 399
James Schneider and Paul W. Mayne. 1999. Soil Liquefaction Response in Mid-America Evaluated by Seismic Piezocone Tests. Retrieved July 12 2023, from https://www.researchgate.net/publication/32962762_Soil_Liquefaction_Response_in_Mid America_Evaluated_by_Seismic_Piezocone_Tests
Kamaludin Bin Hassan. 1990. A Summary of the Quaternary Geology investigations in Seberang Prai, Pulau Pinang and Kuala Kurau. Geological Society of Malaysia Bulletin 26: 47-53.
Kardani, N., Zhou, A., Nazem, M., and Shen, S. L. 2020. Estimation of Bearing Capacity of Piles in Cohesionless Soil Using Optimised Machine Learning Approaches. Geotechnical and Geological Engineering, 38(2): 2271–2291.
Wikipedia. 2023. K-nearest neighbors algorithm Retrieved July 14, 2023, from https://en.wikipedia.org/wiki/K-nearest_neighbors_algorithm
Kordjazi, A., Pooya Nejad, F., and Jaksa, M. B. 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.
Mahesh, P., and Surinder, D. 2008. Modeling Pile Capacity Using Support Vector Machines and Generalized Regression Neural Network. Journal of Geotechnical and Geoenvironmental Engineering, 134(7): 1021–1024.
Momeni, E., Nazir, R., Jahed Armaghani, D., and Maizir, H. 2014. Prediction of pile bearing capacity using a hybrid genetic algorithm-based ANN. Journal of the International Measurement Confederation, 57: 122–131.
Neoh Cheng Aik. 2018. Design & Construction of Driven RC Pile Foundations-Past & Present Experiences. IEM Sabah Pile Foundation Seminar, 7 August 2018.
Pedram Jahangiry. 2022. Part 5-Bias variance trade off and overfitting in Machine learning - YouTube. Retrieved July 12, 2023, from https://www.youtube.com/watch?v=EEHhGRq-r1c&list=PL2GWo47BFyUPWL5fBZSn6FFHRr1bSkX_J&index=6
Pham, T. A., Ly, H. B., Tran, V. Q., Giap, L. Van, Vu, H. L. T., and Duong, H. A. T. 2020. Prediction of pile axial bearing capacity using artificial neural network and random forest. Applied Sciences, 10(5): 1871:
Poulos, H. G. 1988. From theory to practice in pile design. International Journal of Rock Mechanics and Mining Sciences & Geomechanics Abstracts, 25(5): 10-40.
Qiuxia Liu, Yadong Cao, and Changhong Wang. 2019. Prediction of Ultimate Axial Load-carrying Capacity for Driven Piles using Machine Learning Methods. 2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC 2019).
Randolph, M. F. 2003. Science and empiricism in pile foundation design. Geotechnique, 53(10): 847-875.
Random forest - Wikipedia. 2023. Retrieved July 14, 2023, from https://en.wikipedia.org/wiki/Random_forest
Shahin, M.A. 2010. Intelligent Computing For Modeling Axial Capacity Of Pile Foundations. Canadian Geotechnical Journal, 47(2): 230-243.
Shahin, M. A. 2013. Artificial Intelligence in Geotechnical Engineering: Applications, Modeling Aspects, and Future Directions. Metaheuristics in Water, Geotechnical and Transport Engineering, 169–204. Elsevier Inc.
Shahin, M. A. 2016. State-of-the-art review of some artificial intelligence applications in pile foundations. Geoscience Frontiers, 7(1): 33–44.
Shooshpasha, I., Hasanzadeh, A., and Taghavi, A. 2013. Prediction of the axial bearing capacity of piles by SPT-based and numerical design methods. International Journal of GEOMATE, 4(2): 560–564.
Ubani Obinna. 2022. Standard Penetration Test (SPT) for Foundation Design - Structville. Retrieved July 12, 2023, from https://structville.com/standard-penetration-test-spt
Wikipedia. 2023. Support vector machine. Retrieved July 14, 2023, from https://en.wikipedia.org/wiki/Support_vector_machine
Shaw Shong, Liew. 2002. Pile Design with Negative Skin Friction. Tripartite Meeting and Technical Courses-Geotechnical Engineering, 27 June 2022, Puteri Pacific Hotel, Johore.
Truong Tien, Nguyen. 1981. Design of Piles in Cohesive Soil. Linkoping, Sweden.
XLSTAT Help Centre (n.d.). Which statistical model should you choose? Lumivero. Retrieved on May 13, 2023, from https://help.xlstat.com/6723-which-statistical-model-should-you-choose
Yean Chin, Tan and Chee Meng, Chow. 2003. Design & Construction of Bored Pile Foundation. Geotechnical Course for Pile Foundation Design & Construction, Ipoh (29 – 30 September 2003)
Zhang, W., Li, H., Li, Y., Liu, H., Chen, Y., and Ding, X. 2021. Application of deep learning algorithms in geotechnical engineering: a short critical review. Artificial Intelligence Review, 54(8): 5633–5673.
