Digital Twin I – Comprehensive Condition Assessment for Resilient Transport Infrastructure under Normal Service Conditions and Extreme Climatic Events
Transport infrastructures are the backbone of our society due to the heavy rely on the uninterrupted availability of the road network. Reliable condition assessment and maintenance of our aging transport infrastructures are essential to maintain their integrity and serviceability. Moreover, extreme climatic events and climate change are important threats to the reliability and safety of the road network. This has led to a growing demand for more accurate and reliable condition assessment – data collection, data interpretation and performance prediction – processes to ensure ever more resilient road transportation. This repository of data and models is integral to the framework of Digital Twin for transport infrastructures.
A Digital Twin of an infrastructure is a living digital 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/users with an early insight into potential risk to mobility induced by climatic events, heavy vehicle load and even aging of a transport infrastructure.
The goal of this project is to provide the needed knowledge and tools to predict the structural performance and safety of the aging transport infrastructure under both normal service conditions and extreme climatic events. The successful implementation of the project will lead to a safer, quicker and more accurate condition assessment of transport infrastructure ensuring resource-efficiency, accessibility, cost-effectiveness and safety. More specifically, the aim is to develop an enhanced modelling methods that incorporate condition assessment data of cracking and spalling to result in reliable prediction of the remaining load-carrying capacity of concrete structures with corroded reinforcement. The goal will be realized through the following specific objectives:
Objective 1: To develop an enhanced modelling method incorporating condition assessment data
Objective 2: To validate the enhanced modelling method with naturally corroded specimens
Objective 3: To demonstrate the proposed comprehensive condition assessment process in full scale scenarios on Johanneshov Bridge in Stockholm (see the figure)
Kamyab Zandi (kontakt)
Docent vid Chalmers, Arkitektur och samhällsbyggnadsteknik, Konstruktionsteknik
Doktorand vid Chalmers, Arkitektur och samhällsbyggnadsteknik, Konstruktionsteknik
Professor vid Chalmers, Arkitektur och samhällsbyggnadsteknik, Konstruktionsteknik
Finansierar Chalmers deltagande under 2018–2020
Relaterade styrkeområden och infrastruktur
Informations- och kommunikationsteknik
Building Futures (2010-2018)
C3SE (Chalmers Centre for Computational Science and Engineering)