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This study investigates the application of hazard maps in identifying critical locations associated with extreme vehicle load events. Examining extreme vehicle loads can offer valuable insights into the reliability of road infrastructure. Weigh-In-Motion (WIM) systems are commonly employed for this purpose; however, their limited availability often necessitates the use of less-sophisticated traffic counters (LSTCs). Unfortunately, LSTCs are unable to measure vehicular axle loads, posing a significant drawback. To overcome this limitation, we propose a methodology that utilizes data from LSTCs to estimate axle loads and map extreme gross vehicle weights. To demonstrate the feasibility of this approach, we present a case study concentrating on the major highway corridors in Mexico. While WIM stations are absent, data from a network of 1,777 counting stations and origin–destination surveys are available. By quantifying Gaussian copula-based Bayesian networks using the existing information, we generate synthetic site-specific axle loads. Subsequently, we calculate gross vehicle weights for selected return periods. These study findings facilitate the identification of hazardous locations. Additionally, we provide an interactive web map and a graphical user interface to generate synthetic axle loads. These tools, along with the proposed methodology, can serve as the foundation for maintenance strategies for existing roads and bridges. • Traffic counters data to pinpoint critical locations with extreme vehicle loads. • 1777 site-specific Bayesian Networks are used to compute synthetic axle loads. • 180 million trucks were simulated to assess extreme gross vehicle weights. • Spatial distribution of loads is revealed by mapping 50-year and 1000-year events. [ABSTRACT FROM AUTHOR]

Abstract: Weigh-in-Motion (WIM) systems are used, among other applications, in pavement and bridge reliability. The system measures quantities such as individual axle load, vehicular loads, vehicle speed, vehicle length and number of axles. Because of the nature of traffic configuration, the quantities measured are evidently regarded as random variables. The dependence structure of the data of such complex systems as the traffic systems is also very complex. It is desirable to be able to represent the complex multidimensional-distribution with models where the dependence may be explained in a clear way and different locations where the system operates may be treated simultaneously. Bayesian Networks (BNs) are models that comply with the characteristics listed above. In this paper we discuss BN models and results concerning their ability to adequately represent the data. The paper places attention on the construction and use of the models. We discuss applications of the proposed BNs in reliability analysis. In particular we show how the proposed BNs may be used for computing design values for individual axles, vehicle weight and maximum bending moments of bridges in certain time intervals. These estimates have been used to advise authorities with respect to bridge reliability. Directions as to how the model may be extended to include locations where the WIM system does not operate are given whenever possible. These ideas benefit from structured expert judgment techniques previously used to quantify Hybrid Bayesian Networks (HBNs) with success. [Copyright &y& Elsevier]

• Reliability analysis on flood defences designed without probabilistic criteria. • Characterizing precipitation and evaporation time series • Generation of water level data using copulas, Markov chains, and hydrological data. • The resulting probability of failure is consistent with historical sources. In some cases, hydraulic infrastructures of different sizes and importance are built without formal design criteria and it may be desired to infer the reliability level for which it was originally designed. In this paper, we present a method to perform the reliability analysis of a dike that was, in all likelihood, designed without probabilistic criteria. This is the Nezahualcoyotl dike, an ancient structure built by the Aztec empire around 1445 in modern-day Mexico City. The method consists of 1. Characterizing time-series of relevant environmental variables. We use precipitation and evaporation by combining a discrete time-state Markov chain with a copula-based stochastic process. 2. Simulating from the time series model a large number of observations to "load" the structure of interest and finally, 3. Compute the probability of failure of the structure with respect to a failure mechanism. In this case, we focus on overflow. The proposed model is able to reproduce to a good extent the hydrology of the system. The return period for overflow obtained in our research is consistent with historical accounts. This work can be used as a reference to assess the reliability of other (ancient or present-day) structures whose design is based on informal criteria. [ABSTRACT FROM AUTHOR]