Digital Twin Anomaly Detection Decision-Making for Bridge Management

Bridge infrastructure in Europe has great economic, social and cultural value. Nevertheless, many of the assets which are part of this network are in poor condition, which has been recently evidenced by the collapse of several bridges. The available human and economic resources are simply not enough to repair, maintain or replace all bridges - part of the European network. Therefore, improvements on the current bridge management and operation system is urgently needed.During this fellowship an open-source digital twin anomaly detection decision-making (DTADD) tool will be developed to support the preventive conservation of existing bridges in Europe. This innovative tool will improve the bridge management by aiding the implementation of a reliability-based bridge management approach (RBBMA), thus contributing to bridge cultural heritage (CH) conservation and sustainability by extending their service life and reducing management costs.The DTADD fellowship has two specific objectives. The first objective is to build digital twin (DT) models of heritage/conventional bridges to assess and identify the highest performing anomaly detection algorithm (ADA) for damage and/or significant decay detection. Its second objective is to develop an ADA-informed open-source decision-making tool based on a RBBMA, to assess the need for bridge intervention while explicitly considering the bridge’s CH value.This fellowship will be carried out mainly at OsloMet, Norway, with a six-month secondment at TU Delft, Netherlands. The research experience, skills and knowledge obtained during the fellowship will help the experienced researcher to become a leading international expert on the conservation of bridges and will aid him to achieve his goal of becoming an independent researcher and obtaining a tenure track position. Furthermore, this fellowship is aligned with the Transport action of the European Green Deal as well as with the UN Sustainable Development Goals (SDGs).