Enhanced digital twin for on-site inspections using distributed optical fiber sensors and augmented reality

Abstract

Infrastructure inspections are still largely manual, episodic, and subjective, which delays damage detection and limits data-informed decision making. The paper introduces a Digital Twin framework designed to enhance infrastructure inspections using Distributed Optical Fiber Sensors (DOFS) and Augmented Reality (AR). The framework integrates advanced sensing technologies, edge computing, and web-based applications to provide real-time and historical data visualization during inspections. DOFS technology, known for its high spatial resolution and sensitivity to strain and temperature variations, is utilized to capture high-resolution strain data for continuous structural health monitoring. The framework combines DOFS data with Building Information Modelling (BIM) and AR to create a virtual representation of the assets, enabling precise and efficient on-site inspections. Two case studies demonstrate the practical application of this system: one focusing on historical data visualization and the other on real-time sensor data visualization. The results highlight the framework's ability to provide valuable insights into infrastructure health, improve inspection accuracy, and enhance decision-making processes.