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Bridge fires are low-probability but high-consequence incidents. Generally, bridge design codes and standards, in contrast to building codes, do not take into account the concept of fire safety. However, recent high-profile fire incidents on bridges and in other infrastructure have opened a debate on the need for fire resistance requirements on bridges. An overview of fire hazard in bridges is presented. A state-of-the-art review related to the bridge fire hazard was carried out. Different conditions and complexities associated with characterizing fire hazards in bridges are discussed, and a design strategy to integrate performance-based fire safety into bridge design is suggested. Further, a strategy to assess and repair fire-damaged bridges is proposed. A case study is presented to evaluate the fire performance of a composite steel bridge girder. Finally, needed research that can lead to improved performance of bridges during fire incidents is highlighted.

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h i g h l i g h t sA meta-analysis framework for a stochastic characterization of occupancy variables. Sensitivity ranking of occupancy variability against all other sources of uncertainty. Sensitivity of occupant presence for building energy consumption is low. Accurate mean knowledge is sufficient for predicting building energy consumption. Prediction of peak demand behavior requires stochastic occupancy modeling. a b s t r a c tOccupants interact with buildings in various ways via their presence (passive effects) and control actions (active effects). Therefore, understanding the influence of occupants is essential if we are to evaluate the performance of a building. In this paper, we model the mean profiles and variability of occupancy variables (presence and actions) separately. We will use a multi-variate Gaussian distribution to generate mean profiles of occupancy variables, while the variability will be represented by a multi-dimensional time series model, within a framework for a meta-analysis that synthesizes occupancy data gathered from a pool of buildings. We then discuss variants of occupancy models with respect to various outcomes of interest such as HVAC energy consumption and peak demand behavior via a sensitivity analysis. Results show that our approach is able to generate stochastic occupancy profiles, requiring minimum additional input from the energy modeler other than standard diversity profiles. Along with the metaanalysis, we enable the generalization of previous research results and statistical inferences to choose occupancy variables for future buildings. The sensitivity analysis shows that for aggregated building energy consumption, occupant presence has a smaller impact compared to lighting and appliance usage. Specifically, being accumulatively 55% wrong with regard to presence, only translates to 2% error in aggregated cooling energy in July and 3.6% error in heating energy in January. Such a finding redirects focus to the accurate estimation of lighting and appliance usage for a better prediction of aggregated energy consumption. Furthermore, it proves that accurate knowledge of the mean profiles is sufficient, that is, stochastic occupancy models do not play a significant role in the prediction of aggregated consumption in a conventional office building where the interaction between the operation of building systems and the spatial and temporal variability of occupancy is weak. When it comes to peak demand behavior, occupancy variability should be taken into account, as static profiles are not able to produce adequate estimates of power duration probabilities close to the power peak.

Recent studies suggest that the underperformance of IPOs in the post-1970 sample may be a small sample effect or “Peso problem.” That is, IPO underperformance may result from observing too few star performers ex post than were expected ex ante. We develop a model of IPO performance that captures this intuition by allowing returns to be drawn from mixtures of outstanding, benchmark, or poor performing states. We estimate the model under the null of no ex ante average IPO underperformance and construct small sample distributions of various statistics measuring IPO relative performance. We find that small sample biases are extremely unlikely to account for the magnitude of the post-1970 IPO underperformance observed in data.

People are typically faster to categorize the race of a face if it belongs to a race different from their own. This Other Race Categorization Advantage (ORCA) is thought to reflect an enhanced sensitivity to the visual race signals of other race faces, leading to faster response times. The current study investigated this sensitivity in a cross-cultural sample of Swiss and Japanese observers with a race categorization task using faces that had been parametrically degraded of visual structure, with normalized luminance and contrast. While Swiss observers exhibited an increasingly strong ORCA in both reaction time and accuracy as the face images were visually degraded up to 20% structural coherence, the Japanese observers manifested this pattern most distinctly when the faces were fully structurally-intact. Critically, for both observer groups, there was a clear accuracy effect at the 20% structural coherence level, indicating that the enhanced sensitivity to other race visual signals persists in significantly degraded stimuli. These results suggest that different cultural groups may rely on and extract distinct types of visual race signals during categorization, which may depend on the available visual information. Nevertheless, heavily degraded stimuli specifically favor the perception of other race faces, indicating that the visual system is tuned by experience and is sensitive to the detection of unfamiliar signals.