Artificial Intelligence in Railroad Track Base Management
The foundational paper, Artificial Intelligence in Railroad Track Base Management, established the current state of AI-enabled substructure assessment, organized the field into three functional layers (perception, condition assessment, prediction and planning), and surfaced five findings on capability maturity and adoption barriers. It also deliberately deferred several threads that exceeded its scope: sensing methods in technical depth, condition standards development, deployment economics, risk and hazard analysis, and a governance framework for responsible use. This prospectus defines the five papers that develop those threads. The series follows a deliberate arc: it moves from the physical layer upward, first establishing what can be measured and how (Paper 1), then what those measurements mean and how they earn engineering and regulatory standing (Paper 2), then what decisions they enable and what they are worth (Paper 3). With the capability case established, the series turns to what can go wrong (Paper 4) and closes with an integrated framework for implementing the technology under realistic institutional, workforce, and regulatory constraints (Paper 5). Each paper is self-contained with its own full bibliography, opens with a note situating it in the series, and closes by naming the threads it passes forward.
6 papers · 2 Aug 2026