The corrosion of reinforcement caused by chloride ingress significantly reduces the length of the service life of reinforced concrete bridges. Therefore, the condition of bridges is periodically inspected by specially trained engineers regarding the possible occurrence of reinforcement corrosion. Their main goal is to ensure that the structure can resist mechanical and environmental loads and offer a satisfactory level of safety and serviceability. In the course of assessment, measuring the chloride content, through which corrosion could be anticipated and prevented, presents a possible alternative to visual inspections and corrosion tests that can only indicate already existing corrosion. It is hard to determine the cost-effectiveness and actual value of chloride content measurements in a simple and straightforward way. Thus, the main aim of the paper was to study the value of newly gained information, which is obtained when a chloride content in reinforced concrete bridges is measured. This value was here analyzed through the pre-posterior analysis of the cost of measurement and repair, taking into account different types of exposure and material properties for a general case. The research focus was set on the initiation phase in which there are no visible damages. A relative comparison of costs is presented, where the cost of possible reactive/proactive repair was compared with the maximum cost of measurement, while the measurement is still cost effective. The analysis showed a high influence of the initial probability of depassivation on the maximum cost of the cost-effective measurement, as well as a nonreciprocal relation of the minimum cost of cost-effective reactive repair with the measurement accuracy.

The accuracy of forecasting models for the prediction of an infrastructure's deterioration process plays a significant role in the estimation of optimal maintenance, rehabilitation, and replacement strategies. Numerous approaches have been developed to overcome the limitations of existing forecasting models. In this article, a direct comparison is made between different models using the same input data to derive conclusions of their distinct performance. The models selected for the comparison were Markov, semi‐Markov, and hidden Markov models together with artificial neural networks (ANNs), which have been reported in literature as reliable deterioration prediction models. A quality of fit was performed to measure how well the observed data corresponded to the predicted values, and therefore objectively compare the performance of each model. The results demonstrated that the most accurate prediction was accomplished by the ANN model. Nevertheless, all models presented differences with respect to typical values of concrete decks life expectancy, which is attributed to the inherent difficulties of the database. Additionally, the problem of the visual inspection subjectivity was also regarded as one of the potential causes for the found deviations. Therefore, this article also discusses the shortcomings of current condition assessment practices and encourages future bridge management systems to replace the classical methods by more sophisticated and objective tools.

The key aspect for the quantification of indirect impacts of flooding is the assessment of the disruption of the transportation service considering social and economic consequences. To investigate how flooding can affect road transportation, it is essential to analyze interaction during the flood event itself, as well as on the following days. In this work, two static and dynamic traffic models are applied to a study zone for quantification of the performance and functionality of the network during the flood and after the failure of infrastructure components. A mesoscopic simulation was applied to identify the traffic disruption in the face of flood events. This simulation is capable of considering the road network model, assigning trip paths with the impact of road closures and speed reductions, and evaluating travel time and vehicle volume redistribution in a given disruption scenario. By comparing the traffic analysis results (travel time, travelled distance and street speed changes) in normal and flooded situations, the impact of flooding on a transportation network could be examined. Moreover, modelling outputs from a case study in the Santarém region (Portugal) indicated that in analyzing the flood impacts on a traffic network, even non-flooded infrastructures must be taken into account because of their service disruption.

Natural disasters are unavoidable and can cause serious damage to bridges, which may lead to catastrophic losses, both human and economic. Therefore, the assessment of bridges exposed to these events is of paramount importance to identify possible mitigation needs. The objective of the present work is to present consistent tools that may allow us to obtain the failure probability of a masonry arch bridge under a flood event, leading to local scour. Surrogate models were implemented to ease the computational cost of the probabilistic analysis. Moreover, a stochastic parametric analysis based on the geotechnical properties of the soil components of masonry arch bridges located in Portugal was performed. The results show the failure mechanism of the masonry arch bridges when subjected to scour-induced settlements and the influence of soil density on the failure probability obtained for different flow discharge values and angles of attack. The presented methodology and derived fragility curves can be used to assess bridge performance under a flood event, thus providing useful information for bridge management and monitoring.

The indirect impacts of flooding on transportation networks include, among others, consequences of the service disruption for the users. Indirect impacts are of a wider scale and with a longer incidence in time than direct impacts. The key aspect for the quantification of indirect impacts of flooding is the assessment of the disruption of the transportation service, with social and economic consequences. In this work, a traffic model for a pilot zone is constructed for accurate quantification of the functionality of the network after the failure of infrastructure components such as road segments and bridges. A mesoscopic simulation, which is capable of building a road network model, assigning trip paths with the impact of road closures, and evaluating travel time and vehicle volume redistribution in a given disruption scenario, was used to identify the traffic disruption in the face of flood events. Modelling outputs from a case study in the Santarém region of Portugal indicate which roads are more congested in a day. A comparison between the baseline and a flood scenario yields the impacts of that flood on traffic, estimated in terms of additional travel times and travel distances. Therefore, simulating and mapping the congestion can largely facilitate the identification of vulnerable links.