AIoT-informed digital twin communication for bridge maintenance

Digital twin (DT) has been moving progressively from concept to practice for bridge operation and maintenance (O&M), but its issues of data synchronization fault tolerance remain problematic. This paper investigates the time delay DT services according communication computation complexity, revealing distinct impact their sequence, proposes an AIoT-informed framework solve above issues. The information hierarchy two-way can be leveraged minimize complexity in framework. Meanwhile, flow resilience proposed are demonstrated using a Petri net. Moreover, is developed into prototypical through cross-platform integration validated with different cases. results demonstrate that compared other existing DTs, high efficiency, low-latency, excellent tolerance, which contribute efficiency safety O&M, especially under communication-constraint circumstances. also promising federated learning protect AI-model privacy stakeholders potential support agent-based intelligent management future little human intervention.