PSI - Issue 84

Sebastian Thöns et al. / Procedia Structural Integrity 84 (2026) 1310–1317

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potentials of approximately 10% through joint expected cost–emission assessments. Liu, Lu and Faber (2024) show that in developing and managing traffic infrastructure, strategies that deliver roughly the same economic value can still differ in carbon footprint by as much as a factor of three. Despite these recent research advances, the environmental implications of service life extension - especially with respect to emissions - have received limited attention. This paper thus focusses on a basic decision-analytical approach to infrastructure service life extension with an integrated model basis for the joint assessment of structural reliability, risk, life cycle costs, and environmental impacts. 2. Decision analytical approach for an integrated infrastructure performance with variable service life The decision analytical approach for the quantification of the service life extension value involves a system performance analysis (SP-A) and predicted action decision analysis (PA-DA) - see Thöns, Caprani et al. (2025): ( ) ( ) 0 l SL, SPA X U l t U E E U X t     =      (1) ( ) ( ) ( ) l SL SL PA X U l SL t t U max E E U X t c t       = −            PA acc s.t. U U  (2) The SP-A evaluates the expected utility U over the original service life 0 SL, t for the system states l X . The service life is typically defined by compliance of the structural reliability with a prescribed target reliability, as specified, for example, in ISO 2394 (2015); see also Celati, Natali et al. (2025). The PA-DA determines an optimised service life SL t that maximises the expected utility, reduced by the service life extension costs ( ) SL c t . This optimisation is subject to acceptability constraints, denoted here in general form as acc U . These constraints may include, for instance, life-safety requirements as well as economic and regulatory limits. Equ. (2) can be further extended to incorporate obtained information (OI), represented by a posterior measurement outcome Z and their associated costs ( ) c Z : ( ) ( ) ( ) ( ) l SL SL OIPA X U l SL t t U max E EUXt|Z ct c Z       = − −            OIPA acc s.t. U U  (3) The value of the prediction service life extension action ( PA V ) and of the obtained information together with the service life extension ( OIPA V ) is then quantified with: (5) The aggregated expected utility (see above Equ. (2) and (3)) to be maximised is quantified through an integrated analysis of risk, life-cycle costs, and environmental impacts. This integrated framework links system state analyses with life-cycle cost and consequence modelling, as well as environmental impact assessment models. To enable this integration, a baseline infrastructure operation scenario model is developed, as summarised in Table 1. PA PA SPA V U U = − OIPA OIPA SPA V U U = − (4)

Table 1: Allocation of costs, consequences and benefits to system states System state

Utility model: Benefits, costs, consequences and environmental impact

▪ Operational benefits ▪ Operational expenses ▪ Operational emissions Direct consequences ▪ Loss of failed parts (value) ▪ Loss of embedded carbon emission intensity

Intact system state: I

Indirect consequences ▪ Human casualties ▪ GDP drop (loss of operation) ▪ Rebuilding (investigation costs, demolition and clean-up costs) ▪ Emissions for decommissioning and rebuilding

Failure state: F