AI-Enhanced Lean-EVM Framework for Railway Infrastructure Delivery: Integrating WBS, LPS, LBMS, and Deep Learning

Railway construction often feels like orchestrating dozens of moving pieces—earthworks, track-laying, Bridges, approach roads, signalling—scattered over many kilometres. Traditional tools such as Work Breakdown Structures and Earned Value Management give teams clear budgets and high-level timelines, but they struggle to reflect what’s happening daily. Lean approaches like the Last Planner System and Location-Based Management System bring in weekly commitments and spatial flow-line charts. Yet, they still rely on manual updates and can’t foresee emerging problems. In this paper, we weave these methods within a digital-twin environment and layer on artificial intelligence to bridge that gap. We first map the entire corridor into zones, assign each task a budget and schedule, and generate classic S-curves for cost and progress. Simulated lean checkins capture crew commitments and roadblocks weekly, while flow-line charts visualize how far each crew has advanced along the track. Behind the scenes, a language model reads meeting notes and field logs to flag new risks, a deep-learning network learns from past cost, schedule, and flow-line data to forecast future performance and a lightweight neural detector watches for early signs of bottlenecks. Our paradigm provides a theoretical path towards location-aware, real-time decision assistance by conceiving Schedule Performance Index (SPI), Cost Performance Index (CPI), and Percent Plan Complete (PPC) as elements of a single digital twin. Through the conversion of scattered data into coherent advice, this integration promises to balance contractual rigour with flow-centric reactivity, maintaining linear infrastructure delivery in line with both financial and geographical goals.