IABSE Survey of Implemented Decision-making Models used by Public and Private Owners/Operators of Road- and Railway Infrastructures

• Published in: Structural engineering international : journal of the International Association for Bridge and Structural Engineering (IABSE) 2024-01, Vol.ahead-of-print (ahead-of-print), p.1-10
• Main Authors: Strauss, Alfred, Orcesi, André, Lampropoulos, Andreas, Briseghella, Bruno, Frangopol, Dan M., Sousa, Hélder S., Casas, Joan, Matos, José C., Schellenberg, Kristian, Valenzuela, Matias, Akiyama, Mitsuyoshi, Linneberg, Poul, Hajdin, Rade, Moser, Thomas
• Format: Article
• Language: English
• Subjects: Bridges; decision-making models; Engenharia Civil; Engenharia e Tecnologia; Indústria; Infrastructure systems; inovação e infraestruturas; Key performance indicators; Risk; Science & Technology
• ISSN: 1016-8664; 1683-0350
• DOI: 10.1080/10168664.2022.2154731

Infrastructure systems, such as bridges, are a driver for the economic growth and sustainable development of countries. Similarly, the development of operation and maintenance strategies for infrastructure systems may aim at optimal management using Key Performance Indicators (KPIs) such as reliability, redundancy, availability, safety, economy, environmental performance and resilience. Recent research and development projects, such as COST TU1406, highlight that infrastructure managers make decisions based on a mix of qualitative and quantitative data from various sources paired with models of various levels of complexity as well as expert judgement. Similarly, recent state-of-the-art academia reports on a variety of different decision-making models applicable to the optimal management of infrastructure systems may be used. Within IABSE Commission 5 on Existing Structures, Task Group 5.4 has performed a survey on implemented decision-making models among 23 infrastructure managers from 20 countries. It highlights some similarities in relation to KPIs, condition rating and limit state checks. This has stimulated the standardisation of decision making. The application of risk-based methods, performance prediction and intervention modelling are somewhat more scattered and may call for further research and development as well as training. The need to bridge the gap between implemented decision-making models and research is of paramount importance.