The growing traffic flow and the increase in transported masses negatively affect infrastructural safety. Several authors have characterized traffic loads on bridges in the American and Chinese context using Weigh-in-Motion (WIM) systems. Conversely, very few studies have been carried out in Europe and, as far as the authors know, none in Italy. This study covers this gap by providing a statistical analysis of raw WIM data collected on a main bridge near the city of Brescia (Italy). First, the traffic flow and the characteristics of vehicles were gathered by a WIM device. Second, some descriptive statistics were performed by computing the probabilistic distributions of numerous vehicular attributes. Third, as a novelty element, a K-means based Clustering technique was adopted on a wide set of vehicular features to detect heavy vehicle clusters. The results showed the existence of three main clusters: two predominately composed by lightly overloaded ordinary vehicles and construction machinery, respectively, and one by mass exceptional vehicles. This study considers a broader set of vehicular parameters than previous ones and then, provides a deeper understanding. Moreover, it shows that axle mass limits violations are noteworthy among mass exceptional vehicles in Italy highlighting the need of improving weight enforcement. These knowledges will be crucial for a rational organisation of the existing assets.

Bridge's vehicular loads characterization through Weight-In-Motion (WIM) systems. The case study of Brescia

Ventura, R;Barabino, B
;
Vetturi, D;Maternini, G
2023-01-01

Abstract

The growing traffic flow and the increase in transported masses negatively affect infrastructural safety. Several authors have characterized traffic loads on bridges in the American and Chinese context using Weigh-in-Motion (WIM) systems. Conversely, very few studies have been carried out in Europe and, as far as the authors know, none in Italy. This study covers this gap by providing a statistical analysis of raw WIM data collected on a main bridge near the city of Brescia (Italy). First, the traffic flow and the characteristics of vehicles were gathered by a WIM device. Second, some descriptive statistics were performed by computing the probabilistic distributions of numerous vehicular attributes. Third, as a novelty element, a K-means based Clustering technique was adopted on a wide set of vehicular features to detect heavy vehicle clusters. The results showed the existence of three main clusters: two predominately composed by lightly overloaded ordinary vehicles and construction machinery, respectively, and one by mass exceptional vehicles. This study considers a broader set of vehicular parameters than previous ones and then, provides a deeper understanding. Moreover, it shows that axle mass limits violations are noteworthy among mass exceptional vehicles in Italy highlighting the need of improving weight enforcement. These knowledges will be crucial for a rational organisation of the existing assets.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11379/574805
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