Risks Related to Advanced Bridge Monitoring Technologies

Area of Science:

• Civil Engineering
• Computer Science
• Risk Management

Background:

• Bridge monitoring is evolving with advanced technologies like structural health monitoring (SHM), Internet of Things (IoT) sensors, unmanned aerial vehicles (UAVs), artificial intelligence (AI), and cloud computing.
• These technologies enable continuous real-time data acquisition, processing, and early detection of structural degradation, but also introduce emerging risks.

Purpose of the Study:

• To present descriptive risk listings for advanced bridge monitoring.
• To propose a comprehensive risk-governance framework for advanced bridge monitoring using SWOT analysis.
• To couple reliability, security, and governance into a single operationalizable process.

Main Methods:

• SWOT analysis to develop a risk-governance framework.
• Integration of a unified risk taxonomy and assessment.
• Linking sensor and AI performance with cyber threat modeling and data governance requirements.
• Application to real-world deployments: Jindo Bridge SHM program and Stava Bridge digital-twin.

Main Results:

• The proposed framework effectively integrates heterogeneous measurements for improved bridge lifecycle management.
• Demonstrated conversion of diverse data for enhanced monitoring.
• The framework addresses risks associated with advanced monitoring technologies.

Conclusions:

• The interdisciplinary method ensures advanced technologies enhance, rather than erode, bridge infrastructure safety and resilience.
• The framework provides an operationalizable approach to managing risks in advanced bridge monitoring.
• This study contributes a novel method for coupling reliability, security, and governance in infrastructure management.