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One major issue when considering the effects of climate change is to understand, qualify and quantify how 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. Expected changes to local climatic conditions may potentially lead to changes in the degradation processes of building materials, affecting the durability and service life of structures. A key question is how climate change may produce changes of vulnerability owing to physical and chemical actions affecting structural durability or changes to exposure in terms of the intensity/frequency of extreme weather events. In this context, this article focuses on the impact on structural resistance and associated challenges, considering some recent activities of members of IABSE TG6.1. Several case studies of infrastructure assets worldwide are presented and discussed in this article.

The built environment is already facing severe consequences related to climate change. Considering the durability of structures, the increase of carbon-dioxide (CO2) concentration and changes on temperature and relative humidity may accelerate carbonation-induced corrosion, thus affecting the service life of reinforced concrete structures. Several studies have assessed the potential effects of climate change on concrete carbonation for specific locations, and this requires to convert climate databases, which come in various spatial resolutions, to scales that are suitable for the purpose of the study. However, there is not a consistent methodology for using climate projections databases at various spatial scales (e.g., city, district, region, country, etc.). Hence, the main goal of this research is to propose an approach allowing for multi-region assessment of carbonation of reinforced concrete structures, under changing climate. The proposed methodology is based on a carbonation model that takes into account the effects of climate change over time. Moreover, procedures and recommendations are provided to reduce errors in lifetime assessment, such as selection of climate change scenarios, choice of simulation chains, and bias correction. The use of the proposed approach is illustrated by computing carbonation depths for several places located in three districts in Portugal: Porto (north), Lisboa (Middle), and Faro (South). The overall results allow to conclude that: (i) specific climate conditions inside a district, namely temperature and relative humidity, modify the carbonation depths (e.g.: a variation of 19 % was obtained for the carbonation depth in the district of Lisbon); (ii) bias-correction should be systematically carried out to avoid errors in the assessments (for example, in the district of Faro, the time to initiate corrosion was over estimated by about 7 years without bias correction); and (iii) climate change could accelerate concrete ca, Integral Design & Management

Over the Earth’s history, the climate has changed considerably due to natural processes affecting directly the earth. In the last century, these changes have perpetrated global warming. Carbon dioxide is the main trigger for climate change as it represents approximately up to 80% of the total greenhouse gas emissions. Climate change and concrete carbonation accelerate the corrosion process increasing the infrastructure maintenance and repair costs of hundreds of billions of dollars annually. The concrete carbonation process is based on the presence of carbon dioxide and moisture, which lowers the pH value to around 9, in which the protective oxide layer surrounding the reinforcing steel bars is penetrated and corrosion takes place. Predicting the effective retained service life and the need for repairs of the concrete structure subjected to carbonation requires carbon dioxide forecasting in order to increase the lifespan of the bridge. In this paper, short term memory process models were used to analyze a historical carbon dioxide database, and specifically to fill in the missing database values and perform predictions. Various models were used and the accuracy of the models was compared. We found that the proposed Stochastic Markovian Seasonal Autoregressive Integrated Moving Average (MSARIMA) model provides R2 value of 98.8%, accuracy in forecasting value of 89.7% and a variance in the value of the individual errors of 0.12. When compared with the CO2 database values, the proposed MSARIMA model provides a variance value of −0.1 and a coefficient of variation value of −8.0e−4., Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Integral Design and Management