Abstract
Fully grouted ground anchors have been increasingly used as a part of foundation system to resist buoyant force in geotechnical practice. However, designs of fully grouted anchors are commonly based on the calculation of the ultimate pullout capacity along with safety factors, which results in unnecessary economic loss. This is partly due to the fact that it is impractical to predict the anchor performance without strong assumptions of how steel tendons, soils, rock, and grout can collectively resist pullout force or without detailed information of the ground parameters. As one of the promising fields within the framework of artificial intelligence, Machine Learning (ML) has been increasingly used to address geotechnical problems by giving computers the ability to learn without being explicitly programmed. Multivariate Adaptive Regression Splines (MARS) is an ML nonparametric algorithm that is based on a data-driven process. This paper presents the development of a MARS performance prediction model using data from 530 anti-floating anchor pullout tests in 8 different projects in weathered soils and rocks located in Shenzhen, China. In this study, MARS demonstrates the capabilities to capture the complex non-linear relationships in the anti-floating anchor pullout problem. In addition, it is shown that the displacement-based design procedure of the anti-floating anchor based on the MARS model is feasible if appropriate safety factors are adopted.
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GB50086: Technical code for engineering of ground anchorages and shotcrete support. China Architecture & Building Press (2015)
ASTM D4435-13e1: Standard Test Method for Rock Bolt Anchor Pull Test. ASTM International, West Conshohocken, PA (2013)
CSN EN 1537 European Standard: Execution of special geotechnical work – Ground Anchors (2013)
Briaud, J.-L., Weatherby, D.E.: Should grouted anchors have short tendon bond length? J. Geotech. Geoenvironmental Eng. 124, 110–119 (1998)
Shahin, M.A.: 8 – Artificial Intelligence in Geotechnical Engineering: Applications, Modeling Aspects, and Future Directions (2013)
Nejad, F.P., Jaksa, M.B.: Load-settlement behavior modeling of single piles using artificial neural networks and CPT data. Comput. Geotech. 89, 9–21 (2017)
Nejad, F.P., Jaksa, M.B., Kakhi, M., Mccabe, B.A.: Computers and Geotechnics Prediction of pile settlement using artificial neural networks based on standard penetration test data. Comput. Geotech. 36, 1125–1133 (2009)
Zhang, W.G., Goh, A.T.C.: Multivariate adaptive regression splines for analysis of geotechnical engineering systems. Comput. Geotech. 48, 82–95 (2013)
Friedman, J.H.: Multivariate adaptive regression splines. Ann. Stat. 19, 1–141 (1991)
Py-earth python package. https://github.com/scikit-learn-contrib/py-earth. Accessed 19 Mar 2019
Acknowledgements
The authors would like to acknowledge the support from National Key R&D Program of China (No. 2018YFC0704900).
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Shen, H., Li, J., Li, P., Wang, S. (2020). Use of Multivariate Adaptive Regression Splines (MARS) in the Performance Prediction of Anti-floating Anchors. In: Correia, A., Tinoco, J., Cortez, P., Lamas, L. (eds) Information Technology in Geo-Engineering. ICITG 2019. Springer Series in Geomechanics and Geoengineering. Springer, Cham. https://doi.org/10.1007/978-3-030-32029-4_27
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DOI: https://doi.org/10.1007/978-3-030-32029-4_27
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