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Efficiency of the evolutionary methods on the optimal design of secant pile retaining systems in a deep excavation

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Abstract

Deep large excavations in urban areas are an important engineering challenge, whereas secant piling techniques are among the best solutions to have a safe workplace environment. Optimal design of these structures will increase efficiency as well as reduce costs. In this paper, the optimum design of secant pile walls as a retaining system of a deep excavation pit is evaluated. For this purpose, an on-going Tabriz metro station project is investigated as the case study. The structural piles are made of steel material with a hollow pipe section. A layer of struts is also considered for the horizontal bracing of the excavation pit. A detailed finite element model is developed in the OpenSees platform in order to perform static analyses. The optimization of the retaining system is conducted by the mean of four different metaheuristic algorithms including genetic, particle swarm optimization, bee, and biogeography-based optimization algorithms. The total cost of retaining structures is considered as an objective function, which should be minimized in the design space of the variables. The results highlight the excellence of the bees algorithm in achieving a minimum cost, lower dispersion, and rapid convergence rate. The optimum placement of the bracing system and its effect on the soil shear stress are also investigated based on the obtained optimal results.

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Authors and Affiliations

  1. Department of Civil Engineering, Technical and Vocational University (TVU), Tehran, 1435761137, Iran

    F. Taiyari

  2. Department of Mining Engineering, Faculty of Engineering, Urmia University, Urmia, Iran

    M. Hajihassani

  3. Faculty of Engineering, Islamic Azad University, Science and Research Branch of Tehran, Tehran, Iran

    M. Kharghani

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  1. F. Taiyari
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  2. M. Hajihassani
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  3. M. Kharghani
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Correspondence to M. Hajihassani.

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Taiyari, F., Hajihassani, M. & Kharghani, M. Efficiency of the evolutionary methods on the optimal design of secant pile retaining systems in a deep excavation. Neural Comput & Applic 34, 20313–20325 (2022). https://doi.org/10.1007/s00521-022-07591-w

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