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Abstract

Precise pile settlement prediction (SP) in rock-socketed foundations is vital for designing robust bridge foundations and other civil engineering structures. In this work, the behaviors of three powerful algorithms are employed, Dynamic Differential Annealed Optimization (DDAO), Runge Kutta Optimization (RKO), and Ant Lion Optimization (ALO) to improve the performance of the Adaptive Neuro-Fuzzy Inference System (ANFIS) model. In the ANFIS model, some critical input parameters include the rock's unconfined compressive strength, pile length, and pile diameter, which predict SP with high accuracy. The primary contribution of this research is its comparative study with optimization techniques applied to the ANFIS model. Results show that the ANFIS model optimized by DDAO algorithm has the lowest Root Mean Square Error (RMSE) and highest coefficient of determination (R²). On the other side, even though the models optimized through RKO and ALO algorithms also have high predictive capabilities, ALO has extra power in generating a set of Pareto-optimal solutions. This will facilitate the engineers in selecting the most appropriate model given specific design requirements and site-specific constraints. The study provides essential development within the geotechnical engineering study by enhancing the SP prediction accuracy. All these can greatly improve the design and reliability of bridge foundations and other major civil engineering structures for performance and long-term stability.

Keywords

Pile settlement prediction Adaptive Neuro-Fuzzy Inference System ant lion optimizer Runge Kutta optimization dynamic differential annealed optimization

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Prediction of pile settlement using a hybrid Adaptive-Network-Based Fuzzy Inference System

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Keywords

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