The Water-Energy Nexus in Deep Excavation Dewatering: A MODFLOW–Improved Genetic Algorithm Coupled Model for Energy Efficiency Optimization and Engineering Safety Control

Deep excavation dewatering is an energy-intensive groundwater control process in underground engineering, especially under strong recharge and heterogeneous hydrogeological conditions. Conventional dewatering designs often rely on conservative pumping schemes to ensure the required drawdown, which may generate redundant groundwater extraction, unnecessary electricity consumption, additional carbon emissions, and excessive drawdown-induced settlement. To address this problem, this study develops a coupled improved genetic algorithm and MODFLOW optimization model, termed IGA-M, for dewatering well-group operation under engineering safety constraints. The purpose of the proposed model is not to reduce pumping arbitrarily, but to identify and eliminate redundant pumping while satisfying prescribed requirements for target water levels, settlement control, and hydraulic-gradient safety. Through the FloPy interface, the Improved Genetic Algorithm is dynamically linked with MODFLOW to establish a closed-loop simulation-optimization framework. In each optimization iteration, candidate well operation schemes are automatically transferred to MODFLOW, and the simulated hydraulic heads and settlement responses are returned to evaluate the objective function and safety constraints. In this framework, groundwater extraction, electricity consumption, carbon emissions, and land subsidence are treated as physically linked performance indicators of the optimized dewatering scheme. Validation using an idealized case shows that, under the same safety requirements, the IGA-M model reduces redundant hydraulic loading compared with the traditional uniformly distributed pumping method. By removing redundant pumping beyond the safety requirement, the optimized scheme reduced groundwater extraction by 62.7%, which was accompanied by a 44.9% decrease in both carbon emissions and comprehensive costs, as well as a 57.7% reduction in settlement at observation points. In a practical high-permeability deep excavation adjacent to the Yellow River, the model achieved well-group flow regulation under strong recharge conditions. Compared with the traditional scheme, it eliminated approximately 661,000 m3 of redundant groundwater extraction, corresponding to a 17.7% decrease, and consequently saved 26,800 kWh of electricity and reduced CO2 emissions by nearly 16,000 kg during the dewatering period. These results demonstrate that the proposed IGA-M framework can transform MODFLOW from a post-design verification tool into an active optimization engine for dewatering design. It provides a physically based decision-support method for reducing redundant pumping and improving energy efficiency while maintaining engineering safety.