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Estimation of the undrained shear strength of sensitive clays using optimized inference intelligence system

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Abstract

The undrained shear strength of the sensitive clays is an important parameter for the design of the foundation of the civil engineering structures. In this study, novel hybrid machine learning approaches, namely ANFIS-CA and ANFIS-PSO, are developed to predict the undrained shear strength of the sensitive clays. These approaches are based on adaptive neuro-fuzzy inference system (ANFIS) and two metaheuristic optimizations techniques including cultural algorithm (CA) and particle swarm optimization (PSO). Unlike other empirical methods that relied on accurate determination of the pre-consolidation pressure, the proposed approaches are based on five reliable input parameters: depth, effective vertical stress, natural water content, liquid limit, and plastic limit. For this purpose, data of 216 sensitive clay samples obtained from different parts of Southern Finland were used for validating and training models. Standard statistical measures were used to evaluate performance of the models. The results show that the proposed hybrid ANFIS-PSO model obtained reasonably good accuracy (correlation coefficient: R = 0.715), in comparison with ANFIS-CA model (R = 0.6) in predicting the undrained shear strength of the sensitive clays. Therefore, the ANFIS-PSO model is very promising to predict the undrained shear strength of the sensitive clays with limited input parameters.

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Data availability statement

All data, models, or code that support the findings of this study are available from the open source platform GitHub by the following link: https://github.com/QuocAnh90/ANFIS.

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Acknowledgements

The first author acknowledges the funding from the European Union’s Horizon 2020 research and innovation program under the grant agreement 101022007. The authors also thank Dr. Marco D’Ignazio for sharing this data for carrying out this research.

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Authors and Affiliations

  1. Department of Civil and Environmental Engineering, Norwegian University of Science and Technology, Trondheim, Norway

    Quoc Anh Tran

  2. University of Transport Technology, 54 Trieu Khuc, Thanh Xuan, Hanoi, 100000, Vietnam

    Lanh Si Ho, Hiep Van Le & Binh Thai Pham

  3. Civil and Environmental Engineering Program, Graduate School of Advanced Science and Engineering, Hiroshima University, 1-4-1 Kagamiyama, Higashi-Hiroshima, Hiroshima, 739-8527, Japan

    Lanh Si Ho

  4. DDG(R), Geological Survey of India, Gandhinagar, Gujarat, 382010, India

    Indra Prakash

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  1. Quoc Anh Tran
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Correspondence to Binh Thai Pham.

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Tran, Q.A., Ho, L.S., Le, H.V. et al. Estimation of the undrained shear strength of sensitive clays using optimized inference intelligence system. Neural Comput & Applic 34, 7835–7849 (2022). https://doi.org/10.1007/s00521-022-06891-5

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