Rock Mechanics
Mohammad-Taghi Hamzaban; Alireza Chehreghan; Roozbeh Geraili Mikola
Abstract
In the companion Part I paper, an integrated Finite Difference Method–Genetic Algorithm (FDM–GA) framework was developed for automated tunnel back analysis, coupling numerical simulation with evolutionary optimization to estimate geotechnical parameters from field monitoring data. Part II ...
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In the companion Part I paper, an integrated Finite Difference Method–Genetic Algorithm (FDM–GA) framework was developed for automated tunnel back analysis, coupling numerical simulation with evolutionary optimization to estimate geotechnical parameters from field monitoring data. Part II demonstrates the application and validation of this framework through the back analysis of the Pardis Highway Tunnel, excavated within the Hezar Darreh conglomerate formation in northern Tehran, Iran. Three-point convergence measurements from 24 monitoring stations were processed to derive representative reference displacements for calibration. Thirty independent FDM–GA optimization runs were performed to estimate the ground parameters, yielding narrow calibrated ranges for cohesion (c), friction angle (φ), Young's modulus (E), and the coefficient of lateral earth pressure at rest (K0). A representative engineering parameter set was identified by integrating optimization results with inter-parameter correlations and geotechnical evidence from comparable tunnel projects. Unlike conventional tunnel back analysis studies that primarily emphasize parameter optimization, the proposed framework integrates optimization with geological interpretation to identify representative engineering parameter sets. It also systematically evaluates how stress relaxation assumptions influence parameter calibration and the engineering interpretation of calibrated solutions. Recalculated Ground Reaction Curve (GRC) and Longitudinal Displacement Profile (LDP) showed that relatively small variations in stress relaxation assumptions significantly affect predicted convergence and calibrated geotechnical parameters. The results demonstrate that reliable estimation of pre-support deformation and stress release is essential for realistic tunnel back analysis and confirm that the integrated FDM–GA framework provides a robust, repeatable tool for geotechnical parameter calibration in NATM tunnel engineering.
Rock Mechanics
Mohammad-Taghi Hamzaban; Alireza Chehreghan; Roozbeh Geraili Mikola
Abstract
Back analysis of tunnel excavation plays a fundamental role in calibrating geomechanical parameters using field monitoring data. However, conventional direct back analysis procedures remain computationally demanding and highly dependent on operator supervision. This study presents an integrated Finite ...
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Back analysis of tunnel excavation plays a fundamental role in calibrating geomechanical parameters using field monitoring data. However, conventional direct back analysis procedures remain computationally demanding and highly dependent on operator supervision. This study presents an integrated Finite Difference Method–Genetic Algorithm (FDM–GA) framework for automated tunnel back analysis, implemented entirely within the FLAC environment using the embedded FISH programming language. The proposed approach eliminates the need for external optimization software and data transfer between numerical and artificial intelligence platforms. A simplified genetic algorithm is coupled directly with finite difference simulations to iteratively minimize the discrepancy between measured and computed tunnel convergences. The framework incorporates constrained parameter optimization, automated handling of non-convergent models, and a robust convergence-based stopping criterion that avoids predefined error thresholds. Verification is performed using two synthetic plane-strain tunnel models representing stiff cohesive soil and dense granular material. Six unknown parameters (ρ, E, ν, c, φ, and K0) are back-calculated using only three convergence measurements. Results from multiple independent runs demonstrate stable convergence toward very small error values (on the order of 10-6–10-5) and consistent reproduction of synthetic monitoring data. The method successfully narrows broad initial parameter ranges and produces multiple acceptable parameter sets, explicitly acknowledging the non-uniqueness inherent in back analysis problems. The developed FDM–GA framework provides an efficient, self-contained, and adaptable tool for practical tunnel back analysis applications.
