AI Simplifies Bridge Maintenance Planning With Interpretable Decision Trees

Researchers developed an interpretable reinforcement learning system that converts complex bridge condition data into human-readable decision trees for maintenance scheduling. The approach addresses new federal bridge inventory standards requiring element-level condition tracking, making AI-generated policies directly implementable in existing management systems.