Economic losses of bridge failures can mount to millions of dollars per day and spiral quickly. In particular, steel truss bridges are highly vulnerable to member failures, which, if propagated, can cause severe disruptions to the entire system. The vulnerability of these structures has been underscored in recent bridge collapses, which were initiated by the propagation of localised member failures (e.g., I-35W Mississippi Bridge). This paper proposes a methodology for the structural assessment of member failure scenarios in steel truss bridges. A quantitative index (SoD) is proposed to evaluate the consequences of member failures in all bridge elements. The methodology includes a Bayesian Network that captures the relationship between load models and structural responses. Additionally, the methodology integrates Extreme Value Analysis and computes the expected SoD for a 100-year return period. Two complementary approaches are suggested for the analysis of the member failure scenarios. The first approach focuses on the failure scenario itself, examining the post-failure effects in all bridge elements. The second approach evaluates the response of individual elements to various failure scenarios, allowing an in-depth understanding of how different member failures influence specific bridge elements. The methodology has been tested on a railway steel truss bridge in which eleven member failures were simulated. Results allowed to identify the level of significance for the scenarios, providing insights to guide SHM strategies, prioritise interventions and optimise maintenance efforts. This work aims to simplify engineering efforts and support bridge management entities in their crucial fight to improve the bridge's structural safety.

Chemical transport models (CTM) are crucial for simulating the distribution of air pollutants, such as particulate matter, and evaluating their impact on the environment and human health. However, these models rely heavily on accurate emission inventory and meteorological inputs, usually obtained from reanalyzed weather data, such as the European Centre for Medium-Range Weather Forecasts (ECMWF). These inputs do not accurately reflect the complex topography and micro-scale meteorology in tropical regions where air pollution can pose a severe public health threat. We propose coupling the LOTOS–EUROS CTM model and the weather research and forecasting (WRF) model to improve LOTOS–EUROS representation. Using WRF as a meteorological driver provides high-resolution inputs for accurate pollutant simulation. We compared LOTOS–EUROS results when WRF and ECMWF provided the meteorological inputs during low and high pollutant concentration periods. The findings indicate that the WRF–LOTOS–EUROS coupling offers a more precise representation of the meteorology and pollutant dispersion than the default input of ECMWF. The simulations also capture the spatio-temporal variability of pollutant concentration and emphasize the importance of accounting for micro-scale meteorology and topography in air pollution modelling.

This manuscript introduces an exploratory case study of the SIMFAC’s (Sistema de Información Meteorológica de la Fuerza Aérea Colombiana) operational implementation of the Weather Research and Forecasting (WRF) model with a 3DVAR (three-dimensional variational) data assimilation scheme that provides meteorological information for military, public, and private aviation. In particular, it investigates whether the assimilation scheme in SIMFAC’s implementation improves the prediction of the variables of interest compared to the implementation without data assimilation (CTRL). Consequently, this study compares SIMFAC’S 3DVAR-WRF operational implementation in Colombia with a CTRL with the same parameterization (without 3DVAR assimilation) against the ground and satellite observations in two operational forecast windows. The simulations are as long as an operational run, and the evaluation is performed using the root mean square error, the mean fractional bias, the percent bias, the correlation factor, and metrics based on contingency tables. It also evaluates the model’s results according to the regions of Colombia, accounting for the country’s topographical differences. The findings reveal that, in general, the operational forecast (3DVAR) is similar to the CTRL without data assimilation, indicating the need for further improvement of the 3DVAR-WRF implementation.