Vehicular Loads Hazard Mapping Through a Bayesian Network in the State of Mexico

Abstract

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, gather data the entire year. However, one of the disadvantages of traffic count data is that most counters used, do not measure total vehicle weight and axle load data. Traffic counts display only the classification of vehicles, traffic volume, average daily traffic, and annual average daily traffic. Axle loads on the other hand are required, for example, as input in the design of pavement and new bridges, and the reliability assessment of existing ones. Weighin-motion (WIM) systems are usually used to collect vehicle load data. The State of Mexico (in central Mexico) has 115 permanent vehicle counting stations with 745 traffic counting points in its federally administered road network. However, due to the lack of WIM stations, it is not possible to obtain axle load data. In this paper, a Bayesian Network (BN) quantified with data from WIM stations in the Netherlands is used to describe the weight and length distribution of heavy vehicles registered in the permanent vehicle counting stations of the State of Mexico federal highways. The Dutch and Mexican vehicle types are matched according to similar characteristics. Later, synthetic WIM observations from the BN model are analysed through extreme value theory and vehicle loads with selected return periods are computed for all study counting points. The outcome is a mapping methodology with a linked database. The traffic volumes and extreme loads can then be easily found and compared with other highways in the network. This work shows that hazard maps can be implemented to provide importantly and summarized information to understand the risks of extreme traffic loads and to help in the reliability assessment and maintenance strategies of pavements and bridges.

Reliability Analysis for Overturning and Sliding of Lacustrine Dikes: The Nezahualcoyotl's Dike Case

“…In this paper, materials used in constructing the Nezahualcoyotl dike are characterized, and synthetic time series of water levels during the dry and wet seasons at Lake Texcoco is used to model the structure loadings (methodology in Torres-Alves and Morales-Nápoles (2020) and expanded by Mendoza-Lugo et al (2023)).…”

“…Data sets of precipitation, evaporation, and water levels at Lake Texcoco result from the statistical modeling presented in Torres-Alves and Morales-Nápoles (2020) and Mendoza-Lugo et al (2023). Although this process is not the primary goal of this paper, it is briefly described in the following paragraphs.…”

“…Later, Mendoza-Lugo et al (2023) extended this model by i) including the simulation of the hydrological variables during the dry season, and ii) updating the water balance equation by adding more water inflow and outflow sources. In this paper, the resulting synthetic time series of water levels from Mendoza-Lugo et al ( 2023) are used as input to estimate the dike's failure probability for overturning and sliding.…”

“…Based on a water balance equation first given in Torres-Alves and Morales-Nápoles (2020) and later modified and expanded by Mendoza-Lugo et al (2023), the synthetic time series of water levels in Lake Texcoco is estimated. Water inflow sources are precipitation (X), streamflow into the water body (Q), surface runoff (Q r ), and subsurface runoff (Q s ).…”

“…In this paper, synthetic time series of daily water levels (E t ) at Lake Texcoco that spans 5,000 years (Section 2) are used. This data set is the result of the statistical model presented by Torres-Alves and Morales-Nápoles (2020) and Mendoza-Lugo et al (2023) based on Markov Chains, bi-variate copulas, and water balance equations, as presented in Section 2.2.…”