Visualizing Structural Health Monitoring with Digital Twins

Abstract

As infrastructure ages and more bridges become structurally deficient, there is a need for effective and efficient systems to monitor the health of structures and prevent structural failures. Structural Health Monitoring (SHM), which utilizes sensors to collect real-time data about a structure's behavior, is a proven solution for asset management, but its data is challenging to understand and visualize. One solution for SHM visualization is a Digital Twin (DT), a 3D digital replica of a physical asset that reflects the health of the real structure at any given time. Unfortunately, current solutions fail to properly integrate SHM data into DTs. The objective of this thesis is to develop a workflow for creating an integrated, synchronous, and user friendly SHM-DT and evaluate the methods through two case studies: an initial case study of a cantilever beam and a real world case study of Streicker Bridge at Princeton University. For each case study, we will create, combine, and compare a Geometric Model that encompasses the structure's physical form, a Numerical Model that analytically simulates the structures theoretical behavior, and an SHM Data Model that reflects the physical structure's actual behavior. The resulting DTs are evaluated based on their ability to accurately determine different load cases and update accordingly. The Cantilever Beam DT correctly identifies 19/21 load locations and 16/21 load magnitudes, and the Streicker Bridge DT correctly identifies 4/5 load locations and 5/5 load magnitudes. The implications and limitations of the SHM-DT workflow are discussed and future work is outlined to achieve a fully integrated, synchronous, and user-friendly SHM-integrated DT.