Abstract
The Standard Penetration Test (SPT) is a widely used field test in geotechnical engineering to determine the consistency, strength, and other properties of soil. This paper presents an alternative approach to determine the Standard Penetration Resistance Value. A lot of times, due to budget limitations, time constraints, and other concerns, there is a tendency to discard this test. Often, N value is estimated from the adjacent site if the data is available, else it is discarded. Various studies have been carried out to determine factors such as shear velocity and angle of internal friction to estimate SPT-N value. This research is a novel approach to estimate N value of the soil with the help of various soil parameters. Here, N value of cohesionless soil is estimated using different techniques such as artificial neural networks (ANNs), XGBoost model, ElasticNet model, lasso regression model, extra trees, Bayesian linear regression, ridge regression, AdaBoost, and random forest models with the help of seven soil parameters, namely moisture content, soil composition (% of gravel, sand, silt, and clay content), bulk density, and dry density. The model is trained on a dataset of SPT-N values and corresponding soil properties. The performance of these models is evaluated using various statistical measures and compared with the existing empirical equations. The results show that the random forest and AdaBoost models have the most efficient performance with R2 values of 0.62 and 0.63, respectively, while their accuracies are 84.53% and 83.61%, respectively. The proposed approach can be a useful tool for geotechnical engineers to predict the SPT-N values of soils and thus facilitate more efficient and cost-effective site investigations.
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References
Rahman MM (2020) Foundation design using standard penetration test (SPT) N-value. https://doi.org/10.13140/RG.2.2.23159.73123
Reich Y (1997) Machine learning techniques for civil engineering problems. Computer-Aided Civil Infrastruct Eng 12:295–310. https://doi.org/10.1111/0885-9507.00065
Reddy YR (2022) Issue 2. www.jetir.org
Erzin Y, Tuskan Y (2017) Prediction of standard penetration test (SPT) value in Izmir, Turkey using radial basis neural network. Celal Bayar Ãœniversitesi Fen Bilimleri Dergisi. https://doi.org/10.18466/cbayarfbe.319912
Tarawneh B (2017) Predicting standard penetration test N-value from cone penetration test data using artificial neural networks. Geosci Front 8:199–204. https://doi.org/10.1016/j.gsf.2016.02.003
Mbarak WK, Cinicioglu EN, Cinicioglu O (2020) SPT based determination of undrained shear strength: Regression models and machine learning. Front Struct Civ Eng 14:185–198. https://doi.org/10.1007/s11709-019-0591-x
Galupino J, Dungca J (2022) Development of a k-Nearest Neighbor (kNN) machine learning model to estimate the SPT N-values of Valenzuela City, Philippines. In: IOP Conference Series: Earth and Environmental Science. Institute of Physics
Arshid MU (2021) Knowledge based prediction of standard penetration resistance of soil using geotechnical database. Civ Eng J (Iran) 7:1–12. https://doi.org/10.28991/CEJ-SP2021-07-01
Nugroho SA, Fernando H, Suryanita R (2021) Prediction of standard penetration test value on cohesive soil using artificial neural networks. J Inform 5:103. https://doi.org/10.26555/jifo.v15i2.a19822
Fernando H, Nugroho SA, Suryanita R, Kikumoto M (2021) Prediction of SPT value based on CPT data and soil properties using ANN with and without normalization. Int J Artif Intell Res. https://doi.org/10.29099/ijair.v5i2.208
Grossi E, Buscema M (2007) Introduction to artificial neural networks. Eur J Gastroenterol Hepatol 19:1046–1054
Freund Y, Schapire RE (1999) A short introduction to boosting
Chen T, Guestrin C (2016) XGBoost: a scalable tree boosting system. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Association for Computing Machinery, pp 785–794
Zou H, Hastie T (2005) Regularization and variable selection via the elastic net. J R Stat Soc Ser B Stat Methodol 67(2):301–320
Tibshirani R (1996) Regression shrinkage and selection via the lasso. J R Stat Soc Ser B (Methodological) 58(1):267–268
Freijeiro-González L, Febrero-Bande M, González-Manteiga W (2020) A critical review of LASSO and its derivatives for variable selection under dependence among covariates. Int Statistical Rev 90(1):118–145
Geurts P, Ernst D, Wehenkel L (2006) Extremely randomized trees. Mach Learn 63:3–42. https://doi.org/10.1007/s10994-006-6226-1
Wundervald B, Ali A, Inglis AN, Prado E (2019) Bayesian linear regression Bayesian trees view project R-music view project Bayesian linear regression. https://doi.org/10.13140/RG.2.2.28385.97121
Hastie T (2020) Ridge regularization: an essential concept in data science. Technometrics 62:426–433. https://doi.org/10.1080/00401706.2020.1791959
Tu C, Liu H, Xu B (2017) AdaBoost typical algorithm and its application research. In: MATEC Web of Conferences. EDP Sciences
Breiman L (2001) Random forests
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Shaik, M.R., Atyam, H., Dharavath, S., Nageshwar Rao, C. (2024). Comparative Study of Various Machine Learning Models for Estimating Standard Penetration Test-N Value. In: Menon, N.V.C., Kolathayar, S., Rodrigues, H., Sreekeshava, K.S. (eds) Recent Advances in Civil Engineering for Sustainable Communities. IACESD 2023. Lecture Notes in Civil Engineering, vol 459. Springer, Singapore. https://doi.org/10.1007/978-981-97-0072-1_33
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