Estimating bridge criticality due to extreme traffic loads in highway networks

Around the world, an increasing amount of bridge infrastructure is ageing. The resources involved in the reassessment of existing assets often exceed available resources and many bridges lack a minimum structural assessment. Therefore, there is a need for comprehensive and quantitative approaches to assess all the assets in the bridge network to...

A Non-parametric Bayesian Network for multivariate probabilistic modelling of Weigh-in-Motion System Data

Weigh-in-motion (WIM) systems help to collect data such as vehicular loads, individual axle loads, vehicle type, and number of axles. This is relevant in engineering because traffic load performs an essential function in the design of new bridges and in the reliability assessment of existing ones, in traffic analysis and other areas of...

Vehicular loads hazard mapping through a Bayesian Network in the State of Mexico

Traffic counts collect information that is valuable, for example, in bridge and road design or maintenance processes. The average daily traffic volume is often the most collected measure of vehicular traffic, which is used in the design or assessment of major highways. Permanent control stations, situated in key locations of the highway network,...

A Copula-Based Bayesian Network for Modeling Compound Flood Hazard from Riverine and Coastal Interactions at the Catchment Scale: An Application to the Houston Ship Channel, Texas

Traditional flood hazard analyses often rely on univariate probability distributions; however, in many coastal catchments, flooding is the result of complex hydrodynamic interactions between multiple drivers. For example, synoptic meteorological conditions can produce considerable rainfall-runoff, while also generating wind-driven elevated sea...