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Swarm Intelligence Based Optimum Design of Deep Excavation Systems

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Applied Optimization and Swarm Intelligence

Part of the book series: Springer Tracts in Nature-Inspired Computing ((STNIC))

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Abstract

In this chapter, the design of deep excavation system for single row anchor sheet piles, which is ensured the necessary stability conditions of geotechnical and structural with FHWA-IF-99-015, is investigated by utilizing a particle swarm optimization. Some parametric analyses considering various design cases have been conducted to examine alternation of the dimensions of the sheet wall design in the cohesionless soil. Different values of the depth excavation and the angle of internal friction have been taken into consideration to evaluate in terms of the parametric effect. As a result, safe and economical sheet wall designs with single row anchor has been acquired for the discrete design parameters which are the depth anchor; the horizontal distance between anchors, anchor inclination, anchor bond length and the number of strands in an anchor. Design examples with the proposals of the different design cases have been submitted, indicating that the algorithm developed is the most robust and efficient method in solving deep excavation systems optimizations problem.

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Authors and Affiliations

  1. Department of Civil Engineering, KTO Karatay University, Konya, Turkey

    E. Uray

  2. Department of Civil Engineering, Karamanoglu Mehmetbey University, Karaman, Turkey

    S. Çarbaş

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  1. E. Uray
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  2. S. Çarbaş
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Correspondence to E. Uray .

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Editors and Affiliations

  1. Tecnalia Research and Innovation, BRTA (Basque Research and Technology Alliance), Derio, Spain

    Eneko Osaba

  2. Department of Design Engineering and Mathematics, School of Science and Technology, Middlesex University, London, UK

    Xin-She Yang

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Uray, E., Çarbaş, S. (2021). Swarm Intelligence Based Optimum Design of Deep Excavation Systems. In: Osaba, E., Yang, XS. (eds) Applied Optimization and Swarm Intelligence. Springer Tracts in Nature-Inspired Computing. Springer, Singapore. https://doi.org/10.1007/978-981-16-0662-5_10

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