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Sectioning Procedure on Geostatistical Indices Series of Pavement Road Profiles

Title: Sectioning Procedure on Geostatistical Indices Series of Pavement Road Profiles
Authors: Nicolosi V.; D’Apuzzo M.; Spacagna RL.; Evangelisti A.; Santilli D.
Contributors: Simona BalzanoGiovanni C. PorzioRenato SalvatoreDomenico VistoccoMaurizio Vichi; Nicolosi, V; D’Apuzzo, M; Spacagna, R; Evangelisti, A; Santilli, D
Publisher Information: Springer
Publication Year: 2021
Collection: Universitá degli Studi di Roma "Tor Vergata": ART - Archivio Istituzionale della Ricerca
Subject Terms: Pavement management · Road surface macrotexture · Geostatistics variogram scheme · Spatial data analysis · Dynamic sectioning; Settore ICAR/04 - STRADE; FERROVIE E AEROPORTI
Description: Road sectioning plays a crucial role in Road Asset Management Systems and nowadays high-speed laser-based devices are able to quickly collect a huge amount of data on pavement surface characteristics. However, collected data cannot be directly employed in road maintenance planning but synthetic values have to be derived and this implies a high computational effort in identifying effective synthetic indices and road homogeneous sections. To this purpose, the Geostatistical tools, in terms of Variogram scheme have been applied for characterizing road surface. “Range” and “Sill” values, deriving from the Variogram application, have been proposed as pavement surface characteristics synthetic indices (namely the macrotexture) to identify different road surfaces. Once that Variogram scheme has been applied, a dynamic sectioning procedure can be employed to detect homogeneous road pavement sections and compared with more traditional descriptors. Preliminary results obtained by an experimental smart road, seem to highlight that the Variogram variables can be promising in both road texture characterization and homogeneous section identification
Document Type: book part
Language: English
Relation: info:eu-repo/semantics/altIdentifier/isbn/978-3-030-69943-7; ispartofbook:Statistical Learning and Modeling in Data Analysis: Methods and Applications; serie:STUDIES IN CLASSIFICATION, DATA ANALYSIS, AND KNOWLEDGE ORGANIZATION; alleditors:Simona BalzanoGiovanni C. PorzioRenato SalvatoreDomenico VistoccoMaurizio Vichi; https://hdl.handle.net/2108/284434
DOI: 10.1007/978-3-030-69944-4
Availability: https://hdl.handle.net/2108/284434; https://doi.org/10.1007/978-3-030-69944-4
Rights: info:eu-repo/semantics/restrictedAccess ; license:Copyright dell'editore
Accession Number: edsbas.20AC032F
Database: BASE