Integrated Optimization of Construction Equipment Economics and Fleet Scheduling for Cost Control in Large-Scale Infrastructure Projects

Against the backdrop of China's 2025 carbon neutrality goals and the State Administration of Affairs' (2024) full-cycle cost control mandate. By deploying LSTM-based residual value prediction (MAE 8.2%), carbon-sensitive genetic algorithms (20.3% fuel savings), and 5G-enabled digital twins, this study framework achieves an 18.7% reduction in equipment-related costs for the Shanghai-Nanjing High-Speed Railway Expansion. Empirical results show that the equipment utilization is 29.5% higher, the carbon intensity is 20.6% lower, and the resilience to fuel - price volatility is 68% greater. This research develops a novel cost-carbon-schedule optimization paradigma three-dimensional framework integrating lifecycle economics, real-time carbon accounting, and dynamic schedulingto advance intelligent infrastructure governance in Chinas smart construction era. Significant potential for broader industialapplications is possessed by the framework looking ahead.With digital technologies advancing continuously,AI and blockchain integration into the framework might be considereddata security,transparency,and pblueictive accuracy thus it can be seen would see improvements. Exploration of emerging energy sources as well as storage technologies by future research should also be undertakenthe frameworks adaptability to shifting energy demands examples could be further expanded.