PSI - Issue 62

Sebastian Thöns et al. / Procedia Structural Integrity 62 (2024) 259–267 Author name / Structural Integrity Procedia 00 (2019) 000 – 000

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The annual structural system reliability is analysed (1) for a series system (SS) leading to a major repair and (2) for a brittle Daniels system (DS) model leading to bridge collapse and potentially replacement. The estimated residual service life is projected at 9 years for the SS and 12 years for the DS models (see dashed lines in Fig. 1). Incorporation of survival information significantly improves the reliability assessment for the current year and the ensuing years. The revised remaining service life extends to 18 years for the SS and 21 years for the DS models. The here presented service life analysis is based on structural reliability and human safety requirements, which are accounted for with the target reliability. A repair (SS) facilitates in principle a further serviceability given that with this scenario an economic service life end is not reached (which cannot be identified with this approach). The failure and replacement scenario (DS) leads to a service life end; however, only when no further information is used, which may potentially lead to more precise condition assessments and a sufficient structural reliability. 3 Service life management The service life of an infrastructure can have technical and economical limitations (see previous Sections). The scenario modelling and structural reliability analysis serve only for the qualitative determination of the potential service life end and for the quantification of a technical service life end (Section 2). The purpose of the service life management model is to provide decision support for service life extension of an infrastructure. This is accomplished by the joint modelling, analysis, and optimisation of the technical and economical service life end while accounting for human safety constraints. The service life management model builds upon structural reliability, risk and decision analysis (JCSS (2001-2015), Faber (2008), Thöns (2018)), and takes basis in the service life scenario and quantification models (see Section 2). The latter, however, is expanded with consideration of strategies for service life extension (repairing, replacing, strengthening in combination with monitoring and inspections, etc). This approach facilitates to quantify the effect of the individual strategies on the service life, the structural reliability, risks and expected benefits and costs aggregated to an expected utility, see Fig 2. By utilisation of a Bayesian decision analysis (Raiffa and Schlaifer (1961) and e.g., Thöns (2018)) and maximising the expected utility, optimal strategies are identified. In this way, the service life management model can be used to optimise the budget and timing for service life extension measures. Optimal strategies should be identified with boundaries according to the safety requirements specified in codes and standards.

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Service life distributions

Start of service life

Time

Fig. 2: Depiction of service life management model with expected utilities and ranges of the service life extensions

The assessment of an infrastructure's economic service life necessitates a comprehensive approach involving potential alternatives or extensions of the existing infrastructure's functionality. These models should also incorporate projections of environmental conditions and societal demands, accounting for various factors such as technological advancements, population growth, regulatory changes, and evolving social norms. Additionally, the environmental impact, including resource utilization, energy consumption, and ecological footprint, should be integrated into the decision analysis. With such an approach, the following can be predicted: (1) when the infrastructure will no longer be economically viable or sustainable in meeting the evolving needs and standards of society and the environment, and; (2) when to implement upgrades, replacements, or decommissioning of infrastructure assets, ensuring their optimal use and minimizing adverse economic and environmental impacts.