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The time at which climate change signal can be clearly distinguished from noise is known as time of emergence (ToE) and is typically detected by a general circulation model (GCM) signal-to-noise ratio exceeding a certain threshold. ToE is commonly estimated at large scales from GCMs, although management decisions and adaptation strategies are implemented locally. This paper proposes a methodology to estimate ToE for both precipitation and temperature at local scales (i.e., river basin). The methodology considers local climatic conditions and unbiased GCM projections to estimate ToE by using the statistical power to find when the climate significantly differs from the historical one. The method suggests that ToE for temperature already occurred in three Chilean basins (Limarí, Maipo, and Maule). However, in terms of precipitation, an earlier ToE is clearly identified for the Maule basin, indicating that risk assessment and adaptation measures should be implemented first in this basin. [ABSTRACT FROM AUTHOR]

• A new self-centering shear key geometry is proposed that eliminates residual displacements of the superstructure, using only the structure's own weight, geometry and conventional bearing pads to generate the required restoring force. • The proposed solution is simply a change in the kinematics of the bridge allowing superstructure's self-centering. • The new self-centering technique is used to analyze a typical Chilean bridge using a set of records classified by soil types, considering the three translational components of the ground motion components. The results indicate that the technique is feasible, but care needs to be taken in accounting for an increase in forces in the substructure. • The proposed shear key concept does not need additional devices such seismic isolators or energy dissipation elements. With a simple change of the geometry, the elimination of the residual displacements ensures the superstructure's self-centering behavior. Shear keys are elements in bridges designed to prevent or limit transverse unseating, rotation, and/or collapse of the superstructure responding to strong-intensity earthquake input ground motion, as well as to absorb breaking and various self equilibrating forces. During the 2010 Maule earthquake, Chile's highway infrastructure was seriously impacted. Shear key failures were endemic and did not function as intended. As a result, some bridges experienced partial or complete collapse. Even when the shear keys appeared to have worked, the superstructure exhibited large offsets, which required expensive repairs. An expensive retrofit of undamaged bridges was also carried out as a result of the inadequate response of the bridge infrastructure. This paper addresses the behavioral issues of bridges designed incorporating conventional shear keys and proposes an innovative self-centering concept that eliminates residual displacements in the superstructure. The self-centering shear key concept, as it will be termed here, makes use of the bridge self-weight as a restoring force to ensure self-centering. This concept proposal takes advantage of the kinematics of the bridge. The self-centering shear key concept was validated for a typical Chilean bridge via an extensive study that made use of nonlinear time history analyses. The results indicate that the increase in seismic demand on the substructure is small enough to maintain the bridge base structure in the elastic range while eliminating any residual displacements in the superstructure. [ABSTRACT FROM AUTHOR]

• Inelastic finite element model of a reinforced concrete wall building slice. • Earthquake and tsunami loadings in sequence applied to the building model. • The tsunami capacity is sensitive to the Froude number. • The tsunami capacity is only affected by severe previous earthquake damage. This paper investigates the behavior of a typical Chilean reinforced concrete wall building under sequential earthquake and tsunami actions using a double pushover analysis approach. A real concrete wall structure that was damaged after the M w = 8.8 2010 Maule earthquake is considered as a case study building and a simplified nonlinear finite element model of a fictitious slice of the building is subjected to earthquake and tsunami loading in sequence. The analysis of the building under these loadings consists of three stages: the structure is first subjected to seismic loading by means of a pushover analysis until a specific damage state is reached; then a pushover with the same load pattern but in the opposite direction is applied until the shear at the base is zero; and finally, the building is subjected to tsunami loading by means of a variable depth pushover analysis until the maximum capacity is reached. Different tsunami load cases are considered in this study, varying the Froude number and the direction of the tsunami loading. The results show that the tsunami response of the building is mainly dependent on the Froude number of the tsunami flow; however, when the seismic damage is severe, the tsunami capacity of the building is found to be reduced. This is more likely to occur when the effect of the tsunami increases the damage previously induced by the earthquake in the same direction. [ABSTRACT FROM AUTHOR]

Average speed is crucial for calculating link travel time to find the fastest path in a road network. However, readily available data sources like OpenStreetMap (OSM) often lack information about the average speed of a road. However, OSM contains other road information which enables an estimation of average speed in rural regions. In this paper, we develop a Fuzzy Framework for Speed Estimation (Fuzzy-FSE) that employs fuzzy control to estimate average speed based on the parameters road class, road slope, road surface and link length. The OSM road network and, optionally, a digital elevation model (DEM) serve as free-to-use and worldwide available input data. The Fuzzy-FSE consists of two parts: (a) a rule and knowledge base which decides on the output membership functions and (b) multiple Fuzzy Control Systems which calculate the output average speeds. The Fuzzy-FSE is applied exemplary and evaluated for the BioBío and Maule region in central Chile and for the north of New South Wales in Australia. Results demonstrate that, even using only OSM data, the Fuzzy-FSE performs better than existing methods such as fixed speed profiles. Compared to these methods, the Fuzzy-FSE improves the speed estimation between 2% to 12%. In future work, we will investigate the potential of data-driven machine learning methods to estimate average speed. The applied datasets and the source code of the Fuzzy-FSE are available via GitHub. [ABSTRACT FROM AUTHOR]