Performance-based multi-objective optimization of strut structural design using constraint violation gene correction integrated NSGA-II algorithm

For the design of large-scale foundation pits, material usage could be a subjective matter, which usually leads to uncontrolled increase of cost. Meanwhile, the less considered carbon emission is also increased for such cases. To synchronously reduce carbon emission and cost in foundation pit strut structural system, this study first establishes a carbon emission quantification framework for foundation strut systems with clearly defined computational boundaries. A Constraint Violation Gene Correction (CVGC) mechanism was proposed to enhance the optimizing algorithm, reducing the overall iteration time and computational cost of the algorithm population search. Finally, three typical foundation pit strut case studies were analyzed to validate the effectiveness and applicability of the proposed methodology. Results demonstrate that compared with conventional designs, the proposed algorithm achieves significant reductions in both cost and carbon emissions. Meanwhile, the optimization efficiency was improved by 40.9%, 25.3% and 46.8% for cost, 30.1%, 20.9% and 56.3% for carbon emission in the three case studies, respectively. Also, the improved algorithm saved 40%-50% of the population search time. This research provides a scientifically robust methodology to effectively bridge the gap between economic viability and environmental responsibility for the foundation pit design problems.