OPTIMAL DESIGN OF SLOPE STABILIZING PILES USING METAHEURISTIC TECHNIQUES

Date

2024-1-26

Author

Öcal, Arda

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The economical and efficient design of slope stabilizing piles is a challenging task that requires overcoming complex and detailed analysis procedures. This thesis proposes an optimization-based program to find optimal design instances efficiently and intelligently. The developed approach combines the fundamental analysis and design phases of stabilizing piles through four modules. In the first module, a limit equilibrium-based slope stability assessment algorithm is developed to find the critical slip surface and the corresponding factor of safety values for both unreinforced and pile-reinforced slopes. The second module introduces an Artificial Neural Network (ANN) based structural analysis tool to compute pile responses under lateral loading. The third component, namely the reinforcement calculation module, designs the required reinforcement configuration and checks the capacity of the pile cross-section. In the last part, the optimization module formulates the whole design and analysis procedures as an optimization problem. It utilizes a metaheuristic algorithm to search for the optimal design variables for single and multi-plane analyses. Experimental studies reveal that the proposed approach can successfully investigate the safety of slopes under various conditions. The study also demonstrates the potential of ANNs in analyzing laterally loaded piles by establishing the complex relationship between soil and pile interaction. Analyzing multiple planes simultaneously also helps to achieve more comprehensive and robust solutions for larger terrains. Consequently, this study provides a unified tool for the analysis of slopes, and it is expected to assist in the preliminary design stages of slope stabilizing piles.

Subject Keywords

Landslide Stabilizing Piles, Optimization, Metaheuristic Algorithm, Limit Equilibrium Method, Artificial Neural Networks

URI

https://hdl.handle.net/11511/108714

Collections

Graduate School of Natural and Applied Sciences, Thesis