Genetic Algorithm-based Optimization of Tunnel Excavation

This work aims to improve the efficiency, safety, and cost-effectiveness of subterranean construction projects by presenting an optimization strategy for tunnel excavation based on genetic algorithms (GA). Due to the non-linear, multi-variable character of the problem, standard approaches frequently fail to discover optimal solutions for the complex geotechnical and operational challenges involved in tunnel excavation. Through the use of selection, crossover, and mutation—three evolutionary concepts found in genetic algorithms—this study creates a strong optimization framework that can manage a variety of excavation situations. The suggested GA model minimizes ground deformation and complies with safety regulations while optimizing important aspects including equipment allocation, support installation schedule, and excavation sequence. When contrasted with traditional techniques, simulation results show notable gains in excavation performance, underscoring GA's potential as an effective tool for tunnelling project decision-making. This method provides a flexible and scalable way to optimise tunnel excavation techniques under different project and geological circumstances.

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