Metaheuristic based soil parameter identification in deep excavations

Date

2019

Author

Akgül, Abdülsamed

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Attaining accurate ground parameters in the design of cost- efficient underground structures is essential due to the level of complexity and uncertainty in soil- structure interactions and ground conditions. Backcalculation methods have an increasing popularity in the field of geotechnical engineering due to the fact that these methods rely on laboratory and field tests in addition to field monitoring and field information which delivers genuine structure conditions. Therefore, the use of this method provides much more accurate geomechanical parameters of materials in deep excavations when compared to conventional methods. Moreover, acquiring these parameters in a faster method aids in the calibration of the parameters during fast track construction projects. In this study, a finite element based backcalculation is developed through the use of Particle Swarm Optimization algorithm (PSO). The PSO algorithm, which is embedded in the back-analysis platform, acts as an intelligent parameter selection process which provides data for the finite element method. The reaction of the deep excavation structure is attained through two-dimensional finite element analyses. This developed back analysis framework is then tested using the ground deformation data obtained from the deep excavation case study in Ankara/Turkey. The parameters of soil are backcalculated and these parameters are then used for future predictions of deep excavation response. The attainment of the successful results has been observed due to the use of the optimization algorithm and the sensitivity of the measured values. This backcalculation using the PSO algorithm can be used to create more realistic models for the construction of underground structures which share the same properties and ground conditions.

Subject Keywords

Excavation., Deep Excavations, Backcalculation, Particle Swarm Optimization, Metaheuristic, Soil Parameter Identification.

URI

http://etd.lib.metu.edu.tr/upload/12624552/index.pdf
https://hdl.handle.net/11511/44638

Collections

Graduate School of Natural and Applied Sciences, Thesis