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Article

Estimating Shear Strength of Marine Soft Clay Sediment: Experimental Research and Hybrid Ensemble Artificial Intelligence Modeling

1
School of Merchant Marine and School of Ocean Science and Engineering, Shanghai Maritime University, Shanghai 200135, China
2
China Water transport Research Institute, Ministry of Transport, Beijing 100088, China
3
School of Architecture and Engineering, Tianjin University, Tianjin 300072, China
4
School of Physical Education and Health Management, Chongqing Second Normal University, Chongqing 400065, China
5
School of Engineering, Qufu Normal University, Qufu 273165, China
6
Pearl River Hydraulic Research Institute, Pearl River Water Resources Commission, Guangzhou 51061, China
7
School of Civil and Resources Engineering, University of Science and Technology Beijing, Beijing 100083, China
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Water 2024, 16(12), 1664; https://doi.org/10.3390/w16121664
Submission received: 24 May 2024 / Revised: 4 June 2024 / Accepted: 7 June 2024 / Published: 11 June 2024

Abstract

In the design of offshore engineering foundations, a critical consideration involves determining the peak shear strength of marine soft clay sediment. To enhance the accuracy of estimating this value, a database containing 729 direct shear tests on marine soft clay sediment was established. Employing a machine learning approach, the Particle Swarm Optimization algorithm (PSO) was integrated with the Adaptive Boosting Algorithm (ADA) and Back Propagation Artificial Neural Network (BPANN). This novel methodology represents the initial effort to employ such a model for predicting the peak shear strength of the soil. To validate the proposed approach, four conventional machine learning algorithms were also developed as references, including PSO-optimized BPANN, Support Vector Machine (SVM), BPANN, and ADA-BPANN. The study results show that the PSO-BPANN model, which has undergone optimization via Particle Swarm Optimization (PSO), has prediction accuracy and efficiency in determining the peak shear performance of marine soft clay sediments that surpass that offered by traditional machine learning models. Additionally, a sensitivity analysis conducted with this innovative model highlights the notable impact of factors such as normal stress, initial soil density, the number of drying–wetting cycles, and average soil particle size on the peak shear strength of this type of sediment, while the impact of initial soil moisture content and temperature is comparatively minor. Finally, an analytical formula derived from the novel algorithm allows for precise estimation of the peak shear strength of marine soft clay sediment, catering to individuals lacking a background in machine learning.
Keywords: marine soft clay sediment; shear tests; peak shear strength; machine learning marine soft clay sediment; shear tests; peak shear strength; machine learning

Share and Cite

MDPI and ACS Style

Hu, S.; Li, Z.; Wang, H.; Xue, Z.; Tan, P.; Tan, K.; Wu, Y.; Feng, X. Estimating Shear Strength of Marine Soft Clay Sediment: Experimental Research and Hybrid Ensemble Artificial Intelligence Modeling. Water 2024, 16, 1664. https://doi.org/10.3390/w16121664

AMA Style

Hu S, Li Z, Wang H, Xue Z, Tan P, Tan K, Wu Y, Feng X. Estimating Shear Strength of Marine Soft Clay Sediment: Experimental Research and Hybrid Ensemble Artificial Intelligence Modeling. Water. 2024; 16(12):1664. https://doi.org/10.3390/w16121664

Chicago/Turabian Style

Hu, Shuyu, Zhikang Li, Haoyu Wang, Zhibo Xue, Peng Tan, Kun Tan, Yao Wu, and Xianhui Feng. 2024. "Estimating Shear Strength of Marine Soft Clay Sediment: Experimental Research and Hybrid Ensemble Artificial Intelligence Modeling" Water 16, no. 12: 1664. https://doi.org/10.3390/w16121664

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