Truck Weights on Municipal and National Roads without Weight Enforcement

Abstract

One of the main causes of premature deterioration in pavement structures is overweight heavy vehicles. To characterize these vehicles, real loads of motor vehicles of more than four tons should be monitored, especially vehicles classified as C2 (2-axles, single units), C3 (3-axles, single units), T3-S2 (5-axles, single trailer) and T3-S3 (6-axles, single trailer) type, as they represent 99.5% of the Costa Rican truck fleet. This study includes six temporal weighing surveys on municipal roads and seven weighing surveys on national roads, comprising the weight of 525 and 554 trucks, respectively. On municipal roads, C2 vehicles with bulk and wagon body types are predominant (67% of surveyed vehicles), whereas on national routes T3-S2 vehicles predominate (42%). Likewise, it was determined that most of exceeding data correspond to vehicles transporting pit material, construction materials, and merchandise on both types of roads. Compared with municipal roads (8%), the percentage of overweight vehicles is more than twice that on national roads (18%) where weight regulations are not enforced. To estimate updated and realistic load data that can be included in pavement design manuals and guides, the same results are provided in parameters such as truck factors and load spectra.

A Spatial Heterogeneity-Based Segmentation Model for Analyzing Road Deterioration Network Data in Multi-Scale Infrastructure Systems

“…For instance, the spatial distribution of road deterioration has been predicted for roads in New York, USA [15], and road condition future scenarios have been predicted for fifteen low-volume roads in Kerala, India [16]. With the accumulation of data, data-driven investigations, > REPLACE THIS LINE WITH YOUR PAPER IDENTIFICATION NUMBER (DOUBLE-CLICK HERE TO EDIT) < 2 especially large spatial scale data analysis methods, have become more important for network-level infrastructure management, such as state, regional, and national level decision-making [17][18][19]. Another concern is providing in-depth data analysis for simultaneously satisfying different road users' requirements.…”