AI-Enhanced Digital Twin for Weld’s Fatigue Cracks
AI-enhanced Digital Twin of an infrastructure is a digital living simulation that brings all the data and models together and updates itself from multiple sources to represent its physical counterpart. The Digital Twin, maintained throughout the life cycle of an asset and easily accessible at any time, provides the infrastructure owner with an early insight into a potential risk to mobility.
Welding is commonly used for connecting metal components during the fabrication and construction of civil and industrial structures such as bridges. Fatigue Cracks in steel bridges are amongst the most harmful welding defects which can be produced during the fabrication process or the in-service stage. These cracks may ultimately lead to the reduced overall durability of the entire structure. Thus, from a structural health monitoring perspective, it is crucial to detect the crack progression at early stages. The main scientific focus of this project is to study the feasibility of next-generation AI-inspired inspection methods for the detection of fatigue cracks in welds of steel bridges. The outcome in the long-run will lead to a safer, quicker, and more accurate method for condition assessment of infrastructure, ensuring resource-efficiency, accessibility, cost-effectiveness, safety, and societal benefits.
Mozhdeh Amani (contact)
Senior Lecturer at Chalmers, Architecture and Civil Engineering, Structural Engineering
Associate Professor at Chalmers, Architecture and Civil Engineering, Structural Engineering
GENIE, Chalmers Gender Initiative for Excellence
Funding Chalmers participation during 2020–2021
Related Areas of Advance and Infrastructure
Information and Communication Technology
Areas of Advance
C3SE (Chalmers Centre for Computational Science and Engineering)