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The risk analysis of road tunnels faces a growing complexity in ﬁre scenarios, e.g. caused by new energy carriers. Essentially, such complex scenarios involve many interactions between the tunnel users, the ﬁre source and the safety measures. One example is the alarm of tunnel users either initiated by the perception of smoke or by the ﬁre alarm system. To consider these interactions for the quantiﬁcation of consequences, e.g. fatalities, risk analysis requires a complex model. However, the complex model can compute in practice only few discrete scenarios due to its high computational cost, whereas risk analysis generally needs the consequences of a high number of random scenarios. Metamodels can solve this contradiction. They are able to approximate the consequences of many random scenarios with low computational cost based on the consequences of few discrete scenarios computed with the complex model. The eﬃciency of metamodels depends on the required number of these discrete scenarios. In this sense, this dissertation proposes an eﬃcient metamodel within an innovative methodology for risk analysis of road tunnels to allow to consider an increased complexity of scenarios. This metamodel applies the following methods or models: the projection array-based design method speciﬁes the experimental design for the discrete scenarios; the combination of the ﬁre model FDS and the microscopic evacuation model FDS+Evac constitutes the complex model; and moving least squares produces the response surface model. The response surface model approximates the consequences of the random scenarios and therewith introduces an uncertainty, called metamodel uncertainty, which is quantiﬁed with the prediction interval method. Additionally, stochastic individual characteristics of tunnel users in discrete scenarios computed with FDS+Evac attribute evacuation uncertainties to the consequences. An original development in this dissertation, the ’direct approach’, directly transfers the evacuation uncertainties of the discrete scenarios to any random scenario. The evaluation of the metamodel in this dissertation shows following results. Firstly, the response surface model suﬃciently represents the consequences of the complex model. Secondly, the metamodel uncertainty is also essential for this representation, but the prediction interval method reveals a drawback in the risk analysis. Potential approaches to deal with this drawback are discussed. Finally, the direct approach reproduces the evacuation uncertainty of the complex model which then clearly aﬀects the consequences of random scenarios. Therefore, the consideration of the evacuation uncertainty plays an important role for the risk analysis. Furthermore, the projection array-based design method was adapted in this dissertation with two approaches, namely the combination of the experimental designs for FDS and FDS+Evac as well as their sequential reﬁnement. Both approaches contribute to the eﬃciency of the metamodel. These results lead to following conclusions. Firstly, the metamodel eﬃciently integrates the consequences of discrete scenarios into risk analysis and thus allows to consider an increased complexity. Secondly, the metamodel is an advancement for risk analysis not only for road tunnelsbutalsomoregeneralinﬁresafetyengineering. Forthesetworeasons,themetamodel might be interesting for other methodologies for risk analysis. In addition, the metamodel is generic and is therefore widely applicable on other issues beside from risk analysis, e.g. to assess the safety of structures related to time-consuming experiments depending on multiple variables.

The current practice of operating and maintaining deteriorating structural systems ensures acceptable levels of structural reliability, but it is not clear how efficient it is. Changing the current prescriptive approach to a risk-based approach has great potential to enable a more efficient management of such systems. Risk-based optimization of operation and maintenance strategies identifies the strategy that optimally balances the cost for controlling deterioration in a structural system with the achieved risk reduction. Inspections and monitoring are essential parts of operation and maintenance strategies. They are typically performed to reduce the uncertainty in the structural condition and inform decisions on future operation and maintenance actions. In risk-based optimization of operation and maintenance strategies, Bayesian updating is used to include information contained in inspection and monitoring data in the prediction of the structural reliability. All computations need to be repeated many times for different potential inspection and monitoring outcomes. This motivates the development of robust and efficient approaches to this computationally challenging task.
The reliability of deteriorating structural systems is time-variant because the loads on them and their capacities change with time. In most practical applications, the reliability analysis of deteriorating structural systems can be approached by dividing their lifetime into discrete time intervals. The time-variant reliability problem can then be represented by a series of time-invariant reliability problems. Using this methodology as a starting point, this thesis proposes a novel approach to compute the time-variant reliability of deteriorating structural systems for which inspection and monitoring data are available. The problem is formulated in a nested way in which the prediction of the structural condition is separated from the computation of the structural reliability conditional on the structural condition. Information on the structural condition provided by inspections and monitoring is included in the reliability assessment through Bayesian updating of the system deterioration model employed to predict the structural condition. The updated system reliability is obtained by coupling the updated deterioration model with a probabilistic structural model utilized to calculate the failure probability conditional on the structural condition. This approach is the first main outcome of this thesis and termed nested reliability analysis (NRA) approach. It is demonstrated in two numerical examples considering inspected and monitored steel structures subject to high-cycle fatigue.
An alternative – recently developed – approach, which also follows the strategy of discretizing time, describes deteriorating structural systems with hierarchical dynamic Bayesian networks (DBN). DBN combined with approximate or exact inference algorithms also enable the computation of the time-variant reliability of deteriorating structural systems conditional on information provided by inspection and monitoring data. In this thesis – as a proof of concept – a software prototype is developed based on the DBN approach, which can be used to assess the reliability of a corroding concrete box girder for which half-cell potential measurements are available. This is the second main outcome of this thesis.
Both approaches presented in this thesis enable an integral reliability analysis of inspected and monitored structures that accounts for system effects arising from (a) the correlation among deterioration states of different structural elements, (b) the interaction between element deterioration and system failure, and (c) the indirect information gained on the condition of all unobserved structural elements from inspecting or monitoring the condition of some structural elements. Thus, both approaches enable a systemwide risk-based optimization of operation and maintenance strategies for deteriorating structural systems.
The NRA approach can be implemented relatively easily with subset simulation, which is a sequential Monte Carlo method suitable for estimating rare event probabilities. Subset simulation is robust and considerably more efficient than crude Monte Carlo simulation. It is, however, still sampling-based and its efficiency is thus a function of the number of inspection and monitoring outcomes, as well as the value of the simulated event probabilities. The current implementation of the NRA approach performs separate subset simulation runs to estimate the reliability at different points in time. The efficiency of the NRA approach with subset simulation can be significantly improved by exploiting the fact that failure events in different years are nested. The lifetime reliability of deteriorating structural systems can thus be computed in reverse chronological order in a single subset simulation run.
The implementation of the DBN approach is much more demanding than the implementation of the NRA approach but it has two main advantages. Firstly, the graphical format of the DBN facilitates the presentation of the model and the underlying assumptions to stakeholders who are not experts in reliability analysis. Secondly, it can be combined with exact inference algorithms. In this case, its efficiency neither depends on the number of inspection and monitoring outcomes, nor on the value of the event probabilities to be calculated. However, in contrast to the NRA approach with subset simulation, the DBN approach with exact inference imposes restrictions on the number of random variables and the dependence structure that can be implemented in the model.