Pavement Rehabilitation Using 3D Measuring with the Concept of Smart City

Kekula, Frantisek; Langr, Martin; Hrubeš, Pavel

doi:10.1109/scsp.2019.8805715

Cited by 2 publications

(1 citation statement)

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“…Other digital models of the pavement are also used, essentially related to the management and control of infrastructure networks. Among these, the best known are the Digital Model for a Project (DMP), which represents the ideal road condition after the pavement rehabilitation has been made, and the Digital Differential Model (DDM) used to detect areas with extreme unallowed deviations [22]. These models are used extensively during the pavement rehabilitation phase as they make it possible to compare the actual deviations with the maximum deviations allowed by the project specifications and to optimize the maintenance process in progress [23].…”

Section: Introductionmentioning

confidence: 99%

A Method for Obtaining a DEM with Curved Abscissa from MLS Data for Linear Infrastructure Survey Design

2022

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The sudden deterioration of the condition of linear infrastructure networks makes road management a complex task. Knowledge of the surface condition of the pavement is a requirement in order to estimate the causes of instabilities, select the appropriate action and identify all those sections that require urgent intervention. The mobile laser scanning (MLS) technique allows for a fast and safe diagnosis, thus making it possible to plan an early intervention program quickly and cost-effectively. This paper describes a methodology implemented with a twofold purpose: (i) the optimal definition, during the design phase, of the input parameters of the MLS survey (velocity of the vehicle and acquisition rate), defined through the study of the relationship between these parameters and the density of the scanned points and, therefore, with the resolution that allows the analysis of a certain type of pavement distress; (ii) the creation of a Digital Elevation Model with a curved abscissa (DEMc), specific for the analysis of road pavements. The field surveys made and the procedure developed allowed the velocity of the MLS to be associated with the resolution of the DEMc, and thus its capability to highlight distresses at different levels of severity. The creation of the road model is semiautomatic; the height value of each single node of the grid is estimated through spatial interpolation algorithms. Starting from experimental data, a few charts were created that relate the density of the point cloud to the variation of the acquisition rate, together with the minimum resolution. Depending on the type of distress analyzed, it is possible to infer the values to be respected of the parameters. In this way, it should be possible to draw up a few guidelines about MLS surveys addressing linear infrastructures focused on the optimization of the survey design, so as to identify strategies that can maximize benefits with the same available budget.

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Section: Introductionmentioning

confidence: 99%