The implementation of a least square support vector regression model utilizing meta-heuristic algorithms for predicting undrained shear strength

Author:

Qiang Shao,Chenyue M. A.,Dezhi Kong

Publisher

Springer Science and Business Media LLC

Subject

Applied Mathematics,Mechanics of Materials,General Materials Science

Reference44 articles.

1. Abu-Farsakh MY, Mojumder MAH (2020) Exploring artificial neural network to evaluate the undrained shear strength of soil from cone penetration test data. Transp Res Rec 2674(4):11–22

2. Akbarzadeh MR, Ghafourian H, Anvari A, Pourhanasa R, Nehdi ML (2023) Estimating compressive strength of concrete using neural electromagnetic field optimization. Materials (Basel) 16(11):4200

3. Ayubi Rad M, Ayubirad MS (2017) Comparison of artificial neural network and coupled simulated annealing based least square support vector regression models for prediction of compressive strength of high-performance concrete. Sci Iran 24(2):487–496

4. Benemaran RS, Esmaeili-Falak M (2020) Optimization of cost and mechanical properties of concrete with admixtures using MARS and PSO. Comput Concr 26(4):309–316. https://doi.org/10.12989/cac.2020.26.4.309

5. Chandler RJ, (1988) The in-situ measurement of the undrained shear strength of clays using the field vane. ASTM International West Conshohocken, PA, USA.

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