Estimation of ultimate bearing capacity of bored piles using machine learning models

Authors

  • Binh Thai Pham University of Transport and Technology, Hanoi, Vietnam
  • Dam Duc Nguyen University of Transport and Technology, Hanoi, Vietnam
  • Quynh-Anh Thi Bui University of Transport and Technology, Hanoi, Vietnam
  • Manh Duc Nguyen Department of Geotechnical Engineering, University of Transport and Communications, Hanoi, Vietnam
  • Thanh Tien Vu Department of Technology, Smart Contruction Group, Hanoi, Vietnam
  • Indra Prakash DDG (R) Geological Survey of India, Gandhinagar 382010, India

DOI:

https://doi.org/10.15625/2615-9783/17177

Keywords:

Bearing capacity, bored pile, machine learning, ANN, ANFIS, SVM

Abstract

The ultimate bearing capacity of bored piles is an essential parameter in foundation design of structure. In the present study, three Machine Learning (ML) methods namely Adaptive Neuro-Fuzzy Inference System (ANFIS), Support Vector Machine (SVM) and Artificial Neural Network (ANN) were utilized to estimate bearing capacity of bored piles based on limited engineering parameters of pile and soil obtained from 75 test sites in Vietnam. These parameters include pile diameter, pile length, tensile strength of main longitudinal steel bar, compressive strength of concrete, average SPT index at the tip of the pile, average SPT index at the pile body. Validation of the methods was verified using standard statistical metrics namely Root Mean Square Error (RMSE) and Correlation coefficient (R). The results show that all the proposed models have good potential in predicting correctly bearing capacity of bored piles on training data (R>0.93) and on testing data (R>0.88) but performance of the SVM model is the best (R:0.985 (training) and R:0.958 (testing). Thus SVM model can be used for the accurate prediction of ultimate bearing capacity of bored piles for proper designing of the civil engineering structure foundation.

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References

Abiodun O.I., Jantan A., Omolara A.E., Dada K.V., Mohamed N.A., Arshad H., 2018. State-of-the-art in artificial neural network applications: A survey. Heliyon, 4, e00938. https://doi.org/10.1016/j.heliyon.2018.e00938

Al-Atroush M.E., Hefny A.M., Sorour T.M., 2021. A Parametric Numerical Study for Diagnosing the Failure of Large Diameter Bored Piles Using Supervised Machine Learning Approach. Processes, 9, 1411. https://doi.org/10.3390/pr9081411.

Albusoda B.S., Mohammed S.M., Abbas M.F., 2021. Comparison among Different Methods to Estimate Ultimate Capacity of Bored Pile, in: IOP Conference Series: Materials Science and Engineering. IOP Publishing, p.012008.

Alkroosh I.S., Bahadori M., Nikraz H., Bahadori A., 2015. Regressive approach for predicting bearing capacity of bored piles from cone penetration test data. Journal of Rock Mechanics and Geotechnical Engineering, 7, 584-592. https://doi.org/10.1016/j.jrmge.2015.06.011.

Armaghani D.J., Asteris P.G., 2021. A comparative study of ANN and ANFIS models for the prediction of cement-based mortar materials compressive strength. Neural Comput & Applic, 33, 4501-4532. https://doi.org/10.1007/s00521-020-05244-4.

Ata A., Badrawi E., Nabil M., 2015. Numerical analysis of unconnected piled raft with cushion. Ain Shams Engineering Journal, 6, 421-428.

Barnston A.G., 1992. Correspondence among the correlation, RMSE, and Heidke forecast verification measures; refinement of the Heidke score. Weather and Forecasting, 7, 699-709.

Bazaraa A.R., Kurkur M.M., 1986. N-values used to predict settlements of piles in Egypt, in: Use of In Situ Tests in Geotechnical Engineering. ASCE, 462-474.

Birid K.C., 2021. Evaluation of Ultimate Pile Compression Capacity from Static Pile Load Test Results, in: Abu-Farsakh, M., Alshibli, K., Puppala, A. (Eds.), Advances in Analysis and Design of Deep Foundations, Sustainable Civil Infrastructures. Springer International Publishing, Cham, 1-14. https://doi.org/10.1007/978-3-319-61642-1_1.

Bond A.J., Schuppener B., Scarpelli G., Orr T.L., Dimova S., Nikolova B., Pinto A.V., 2013. Eurocode 7: geotechnical design worked examples, in: Workshop “Eurocode.

Briaud J.-L., Tucker L.M., 1988. Measured and predicted axial response of 98 piles. Journal of Geotechnical Engineering, 114, 984-1001.

Budi G.S., Kosasi M., Wijaya D.H., 2015. Bearing Capacity of Pile Foundations Embedded in Clays and Sands Layer Predicted Using PDA Test and Static Load Test. Procedia Engineering, Civil Engineering Innovation for a Sustainable, 125, 406-410. https://doi.org/10.1016/j.proeng.2015.11.101.

Burland J.B., Broms B.B., De Mello V.F., 1978. Behaviour of foundations and structures.

Chauhan V.K., Dahiya K., Sharma A., 2019. Problem formulations and solvers in linear SVM: a review. Artif. Intell. Rev., 52, 803-855. https://doi.org/10.1007/s10462-018-9614-6.

Chen W., Sarir P., Bui X.-N., Nguyen H., Tahir M.M., Jahed Armaghani D., 2020. Neuro-genetic, neuro-imperialism and genetic programing models in predicting ultimate bearing capacity of pile. Engineering with Computers, 36, 1101-1115.

Chow H., Small J.C., 2005. Finite layer analysis of combined pile-raft foundations with piles of different lengths, in: Proc. 11th Int. Conf. IACMAG. Turin, Italy, 429-436.

De Kuiter J., Beringen F.L., 1979. Pile foundations for large North Sea structures. Marine Georesources & Geotechnology, 3, 267-314.

Decourt L., 1995. Prediction of load settlement relationships for foundations on the basis of the SPT-T. Ciclo de Conferencias Inter.“Leonardo Zeevaert”, UNAM. Mexico, 85-104.

Elsherbiny Z.H., El Naggar M.H., 2013. Axial compressive capacity of helical piles from field tests and numerical study. Canadian Geotechnical Journal, 50, 1191-1203.

Ghasemian A., Hosseinmardi H., Clauset A., 2019. Evaluating overfit and underfit in models of network community structure. IEEE Transactions on Knowledge and Data Engineering, 32, 1722-1735.

Ghorbani B., Sadrossadat E., Bolouri Bazaz J., Rahimzadeh Oskooei P., 2018. Numerical ANFIS-based formulation for prediction of the ultimate axial load bearing capacity of piles through CPT data. Geotechnical and Geological Engineering, 36, 2057-2076.

Hipni A., El-shafie A., Najah A., Karim O.A., Hussain A., Mukhlisin M., 2013. Daily Forecasting of Dam Water Levels: Comparing a Support Vector Machine (SVM) Model With Adaptive Neuro Fuzzy Inference System (ANFIS). Water Resour Manage, 27, 3803-3823. https://doi.org/10.1007/s11269-013-0382-4.

Józefiak K., Zbiciak A., Maślakowski M., Piotrowski T., 2015. Numerical modelling and bearing capacity analysis of pile foundation. Procedia Engineering, 111, 356-363.

Kardani N., Zhou A., Nazem M., Shen S.-L., 2020. Estimation of bearing capacity of piles in cohesionless soil using optimised machine learning approaches. Geotechnical and Geological Engineering, 38, 2271-2291.

Koizumi Y., ITo K., 1967. Field tests with regard to pile driving and bearing capacity of piled foundations. Soils and Foundations, 7, 30-53.

Le H.-A., Nguyen T.-A., Nguyen D.-D., Prakash I., 2020. Prediction of soil unconfined compressive strength using Artificial Neural Network Model. Vietnam Journal of Earth Sciences, 42, 255-264.

Lee I.-M., Lee J.-H., 1996. Prediction of pile bearing capacity using artificial neural networks. Computers and geotechnics, 18, 189-200.

Lopes F.R., Laprovitera H., 1988. On the prediction of the bearing capacity of bored piles from dynamic penetration tests, in: International Geotechnical Seminar on Deep Foundations on Bored and Auger Piles, 1, 537-540.

Ly H.-B., Pham B.T., Dao D.V., Le V.M., Le L.M., Le T.-T., 2019. Improvement of ANFIS Model for Prediction of Compressive Strength of Manufactured Sand Concrete. Applied Sciences, 9, 3841. https://doi.org/10.3390/app9183841.

Meyerhof G.G., 1976. Bearing capacity and settlement of pile foundations. Journal of the Geotechnical Engineering Division, 102, 197-228.

Moayedi H., Hayati S., 2019. Artificial intelligence design charts for predicting friction capacity of driven pile in clay. Neural Computing and Applications, 31, 7429-7445.

Momeni E., Maizir H., Gofar N., Nazir R., 2013. Comparative Study on Prediction of Axial Bearing Capacity of Driven Piles in Granular Materials. Jurnal Teknologi, 61. https://doi.org/10.11113/jt.v61.1777.

Momeni E., Nazir R., Armaghani D.J., Maizir H., 2014. Prediction of pile bearing capacity using a hybrid genetic algorithm-based ANN. Measurement, 57, 122-131.

Ng C.W., Zhang L., Nip D.C., 2021. Response of laterally loaded large-diameter bored pile groups. Journal of Geotechnical and Geoenvironmental Engineering, 127, 658-669.

Nogueira C.G., Boni H.S., Giacheti H.L., 2022. Probabilistic Analysis of Bored Pile Foundations in the Design Phase: An Application Example. Geotechnical and Geological Engineering, 40, 335-353.

Noori R., Hoshyaripour G., Ashrafi K., Araabi B.N., 2010. Uncertainty analysis of developed ANN and ANFIS models in prediction of carbon monoxide daily concentration. Atmospheric Environment, 44, 476-482. https://doi.org/10.1016/j.atmosenv.2009.11.005.

Pham B.T., Amiri M., Nguyen M.D., Ngo T.Q., Nguyen K.T., Tran H.T., Vu H., Anh B.T.Q., Van Le H., Prakash I., 2021a. Estimation of shear strength parameters of soil using Optimized Inference Intelligence System. Vietnam Journal of Earth Sciences, 43(2), 189-198.

Pham B.T., Hoang T.-A., Nguyen D.-M., Bui D.T., 2018. Prediction of shear strength of soft soil using machine learning methods. Catena, 166, 181-191.

Pham B.T., Nguyen M.D., Van Dao D., Prakash I., Ly H.-B., Le T.-T., Ho L.S., Nguyen K.T., Ngo T.Q., Hoang V., 2021. Development of artificial intelligence models for the prediction of Compression Coefficient of soil: An application of Monte Carlo sensitivity analysis. Science of the Total Environment, 679, 172-184.

Pham T.A., Ly H.-B., Tran V.Q., Giap L.V., Vu H.-L.T., Duong H.-A.T., 2020. Prediction of pile axial bearing capacity using artificial neural network and random forest. Applied Sciences, 10, 1871.

Poulos H.G., 1989. Pile behavior theory and application. Geotechnique, 39, 365-415.

Putra R.R., 2021. Relationship between obtained ultimate bearing capacity results based on n-spt results and static load tests. Geomate Journal, 19, 153-160.

Schmertmann J.H., 1978. Guidelines for cone penetration test: performance and design. United States. Federal Highway Administration.

Seifi A., Ehteram M., Singh V.P., Mosavi A., 2020. Modeling and Uncertainty Analysis of Groundwater Level Using Six Evolutionary Optimization Algorithms Hybridized with ANFIS, SVM, and ANN. Sustainability, 12, 4023. https://doi.org/10.3390/su12104023.

Seo D.-N., Choi S.-H., Kim J.-S., Kim S.-C., Lee D.-H., Cho S.-J., 2021. Study on the Evaluation of End Bearing Capacity of Pre-Bored Piles for the SPT-N value, in: Proceedings of the Korean Institute of Building Construction Conference. The Korean Institute of Building Construction, 133-134.

Shahin M.A., 2010. Intelligent computing for modeling axial capacity of pile foundations. Canadian Geotechnical Journal, 47, 230-243.

Shariatmadari, N., ESLAMI, A.A., KARIM, P.F.M., 2008. Bearing capacity of driven piles in sands from SPT applied to 60 case histories.

Shin K.-S., Lee T.S., Kim H., 2005. An application of support vector machines in bankruptcy prediction model. Expert Systems with Applications, 28, 127-135. https://doi.org/10.1016/j.eswa.2004.08.009.

Shioi Y., Fukui J., 2021. Application of N-value to design of foundations in Japan, in: Penetration Testing. Routledge, 159-164.

Shooshpasha I., Hasanzadeh A., Taghavi A., 2020. Prediction of the axial bearing capacity of piles by SPT-based and numerical design methods. Geomate Journal, 4, 560-564.

Van Dao D., Bui Q.-A.T., Nguyen D.D., Prakash I., Trinh S.H., Pham B.T., 2022. Prediction of interlayer shear strength of double-layer asphalt using novel hybrid artificial intelligence models of ANFIS and metaheuristic optimizations. Construction and Building Materials, 323, 126595.

Van Phong T., Ly H.-B., Trinh P.T., Prakash I., Btjvjoes P., 2020. Landslide susceptibility mapping using Forest by Penalizing Attributes (FPA) algorithm based machine learning approach. Vietnam Journal of Earth Sciences, 42(3), 237-246.

Wang W., Men C., Lu W., 2008. Online prediction model based on support vector machine. Neurocomputing, Neural Networks: Algorithms and Applications, 71, 550-558. https://doi.org/10.1016/j.neucom.2007.07.020.

Zhang W., Wu C., Li Y., Wang L., Samui P., 2021. Assessment of pile drivability using random forest regression and multivariate adaptive regression splines. Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards, 15, 27-40.

Zhao C.Y., Zhang H.X., Zhang X.Y., Liu M.C., Hu Z.D., Fan B.T., 2006. Application of support vector machine (SVM) for prediction toxic activity of different data sets. Toxicology, 217, 105-119. https://doi.org/10.1016/j.tox.2005.08.019.

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Published

2022-05-28

How to Cite

Binh Thai, P. ., Duc Nguyen, D. ., Bui Thi, Q.-A., Duc Nguyen, M., Tien Vu, T. ., & Prakash, I. . (2022). Estimation of ultimate bearing capacity of bored piles using machine learning models. Vietnam Journal of Earth Sciences. https://doi.org/10.15625/2615-9783/17177

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