The changing climate with resulting more extreme weather events will likely impact infrastructure assets and services. This phenomenon can present direct threats to the assets as well as significant indirect effects for those relying on the services those assets deliver. Such threats are path-dependent and place-specific, as they strongly depend on current and future climate variability, location, asset design life, function and condition. One key question is how climate change is likely to increase both the probability and magnitude of extreme weather events under different scenarios of climate change. To address this issue, this paper investigates selected effects of climate change and their consequences on structural performance, in the context of evolving loading scenarios in three different continental regions: Europe, North America, and Asia. The aim is to investigate some main place-specific changes of the exposure in terms of intensity/frequency of extreme events as well as the associated challenges, considering some recent activities of members of the IABSE TG6.1. Climate change can significantly affect built infrastructure and the society by increasing the occurrence and magnitude of extreme events and increasing potential losses. Therefore, specific relationships relating hazard levels and structural vulnerability to climate change effects should be determined.

Different emerging threats highlighted the relevance of recovery and adaptation modelling in the functioning of societal systems. However, as modelling of systems becomes more complex, its effort increases challenging the practicality of the engineering analyses required for efficient recovery and adaptation. In the present work, metamodels are researched as a tool to enable these analyses in traffic networks. One of the main advantages of metamodeling is their synergy with the short decision times required in recovery and adaptation. A sequential global metamodeling technique is proposed and applied to three macroscopic day-to-day user-equilibrium models. Two reference contexts of application are researched: optimal recovery to a perturbation (with response times reduced by 98% with loss of accuracy lower than 1%) and adaptation under uncertainty with perturbation-dependent optimality. Results show that metamodeling-based metaheuristics enable fast resource-intensive engineering analyses of traffic recovery and adaptation, which may change the paradigm of decision-making in this field.

In traffic networks, some elements are more prone to suffer or to create disruptive situations, and the identification of these elements becomes a challenge due to the large number of possible threats. The following paper presents two new methodologies to identify and rank vulnerable and critical links of traffic networks. These methodologies use the Fisher Information Matrix, and the analysis of eigenvalues and eigenvectors, to systematically rank the links of a network. The identification is done by using traffic variables, such as the demand, the travel time, and the network's flow. For the ranking of the links, disruptions are considered in all the possible locations of the network, and the effects are systematically evaluated. In addition, the evaluation of traffic resilience is included in the process to validate the results. Finally, both methodologies are tested in a real network to infer on the validity of the results.

Significant research has been conducted in identifying optimal recovery and adaptation decisions in disruptive scenarios using engineering models. In this context, an aspect that has been target of limited research is that of response times. Modelling is expected to grow progressively more complex as it becomes more accurate. Such complexity increases modelling efforts, and the promise of optimal adaptation and recovery may become hindered. The present work discusses the role of modelling fidelities in adaptation and recovery of systems, and in particular that of using a lower fidelity model that enables zero-time analyses of a system. A framework is proposed for using different fidelities in adaptation and recovery, considering system's decision time requirements. The relevance of this analysis is researched in two traffic networks and results show that multi-fidelity models should be expected to play a key role in increasing the efficiency of optimal adaptation and recovery decisions.

The present work discusses an innovative approach to metamodeling in reliability that uses a field-transversal rationale. Adaptive metamodeling in reliability is characterized by its large spectra of models and techniques with different assumptions. As a result, the reliability engineer is frequently faced with the highly challenging task of selecting an appropriate model or technique with limited a priori knowledge about the performance function that defines the problem of reliability. To tackle this challenge, a complement-basis is proposed for adaptive metamodeling. It consists in using a batch of multiple metamodels or techniques that, accordingly to an activation criterion, are selected to solve the reliability analysis. This activation is set to depend on the model synergy with the problem in-hand. In the present work the leave-one-out loss is applied as evaluator of compatibility, and results show that the absolute loss successfully performs as an activator. A metamodel-independent learning approach and stopping criterion are implemented to study the proposed approach in five representative examples. Results show that the complement-basis allows to increase the efficiency of the reliability analysis through the selection of adequate metamodels, which is indicative of the untapped potential that further transversal research may add to metamodeling in reliability analysis.

One major issue when considering the effects of climate change is to understand, qualify and quantify how natural hazards and the changing climate will likely impact infrastructure assets and services as it strongly depends on current and future climate variability, location, asset design life, function and condition. So far, there is no well-defined and agreed performance indicator that isolates the effects of climate change for structures. Rather, one can mention some key considerations on how climate change may produce changes of vulnerability due to physical and chemical actions affecting structural durability or changes of the exposure in terms of intensity/frequency of extreme events. This paper considers these two aspects and associated challenges, considering some recent activities of members of the IABSE TG6.1.

The present work reviews the implementation of adaptive metamodeling for reliability analysis with emphasis in four main types of metamodels: response surfaces, polynomial chaos expansions, support vector machines, and Kriging models. The discussion presented is motivated by the identified spread and little interaction between metamodeling techniques in reliability, which makes it challenging for practitioners to decide which one to consider in a context of implementation. The conceptual problem of reliability analysis and the theoretical description of the four models is presented, and complemented by a comparative discussion of applications with identification of new areas of interest. The different considerations that influence the efficiency of adaptive metamodeling are reviewed, with extension to applicability discussions for the four models researched. Despite all adaptive techniques contributing to achieve significant gains in the amount of effort required for reliability analysis, and with minimal trade-off in accuracy, they should not be expected to perform equally in regard to the dependence on the reliability problem being addressed. Cross application of methodologies, bridging the gap between methodology and application, and ensembles are some of new areas of research interest identified. One of the major critical considerations for adaptive metamodeling, and that has been target of limited research, is the need for comprehensive techniques that allow a blind selection of the most adequate model with relation to the problem in–hand. To conclude, the extensive and comprehensive discussion presented aims to be a first step for the unification of the field of adaptive metamodeling in reliability; so that future implementations do not exclusively follow individual lines of research that progressively become more narrow in scope, but also seek transversal developments in the field of adaptive metamodeling for reliability analysis.

Reliability assessment with adaptive Kriging has gained notoriety due to the Kriging capability of accurately replacing the performance function while performing as a self-improving function for learning procedures. Recent works on adaptive Kriging pursued to improve the efficiency of the active learning through the application of distinct learning functions, sampling methods, or frameworks to assess the learning space. Within this context, the present work exploits three innovative applications of density scanning to improve the efficiency of the adaptive Kriging. Density scanning has significant synergies with adaptive Kriging implementation. For most learning criteria, candidate points occur in dense clusters. This is due to the fact that the most efficient learning strategies pursue to improve predictions near the failure region, or when the prediction uncertainty is large. Identifying dense clusters of points, and fomenting exploitation of these, parallelizing computations, and limiting the generation of dense clusters in the design of experiments are examples of learning frameworks that can be achieved with density scanning. Three reference examples are researched in the present work, a complex function, a series system, and a relatively high dimension engineering problem. For all the cases, the application of density scanning is identified to improve the active learning efficiency.

Estimating reliable projections of precipitation considering climate change scenarios is important for hydrological studies. General circulation models provide future climate simulations at large scale in terms of large-scale atmospheric variables (LSAVs). Those LSAVs can be downscaled to finer special resolution using several downscaling approaches. This paper presents a support vector regression (SVR)-based downscaling approach to downscale rainfall at several locations in a study area. Because the rainfall generation mechanisms cannot be the same for all the sites in a study area, conventional multisite downscaling approaches that assume the same rainfall generation mechanism should not be used. Therefore, a new downscaling approach is proposed that (1) divides the study area in several climatological regions, and (2) develops different downscaling models for each of the climatological regions to obtain future projections of rainfall. The new approach was implemented on rainfall data obtained for Republic of Ireland to demonstrate the effectiveness of the approach compared with existing approaches. Future projections of rainfall were obtained for the period 2012-2050 corresponding to four Representative Concentration Pathway climate change scenarios. The performance of the SVR approach was compared with that of relevance vector machine-and deep learning-based downscaling approaches.

The INFRARISK project is developing reliable stress tests on European Critical Infrastructure using integrated tools for decision-support. This aims to achieve higher infrastructure network resilience to rare and low probability extreme events. As part of the project, a hazard assessment methodology is developed to account for extreme natural hazards with cascading effects. Often hazard scenarios arising from cascading effects lead to disastrous consequences because such hazards are not prepared for. In particular, this paper focuses on the cascading hazard scenario involving earthquake triggered landslides. Traditional risk analysis considers each risk source as independent from the others. As a consequence, values for risk are usually defined regardless of interactions among the multiple risks present in a region. The current approach accounts for interaction between the two hazards in such a way that the probabilities of occurrence can be aggregated as part of an overall risk assessment methodology. The methodology is then demonstrated on a virtual road network case study as a proof of concept.