Numerical data for modelling pavement deflection behaviour under the TSD

This dataset provides a numerical simulation of pavement mechanical behavior under Traffic Speed Deflectometer (TSD) measurements. It consists of simulated deflection slope data for various pavement structures and subgrade properties, generated using the Alizé-LCPC software, a standard tool in French pavement engineering. The dataset addresses limitations in traditional Falling Weight Deflectometer (FWD) methods, offering a more accurate and computationally efficient approach for estimating the Subgrade Resilient Modulus (M<sub>R</sub>) using machine learning models. This resource is valuable for researchers aiming to enhance pavement evaluation methods and develop predictive models for road infrastructure maintenance and assessment. The data are openly accessible, facilitating widespread research collaboration and the application of advanced data analytics in pavement engineering.

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Publication Details

Journal
Data in Brief
Published
2024-11-15
DOI
https://doi.org/10.1016/j.dib.2024.111135
Primary Topic
Infrastructure Maintenance and Monitoring
Type
article
Field-Weighted Citation Impact
0.00

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article

Numerical data for modelling pavement deflection behaviour under the TSD

Abdelgader Abdelmuhsen, Murilo Freitas, Amine Ihamouten, Denis Liévre et al.
Data in Brief
Infrastructure Maintenance and Monitoring
article

Numerical data for modelling pavement deflection behaviour under the TSD

Abdelgader Abdelmuhsen, Murilo Freitas, Amine Ihamouten, Denis Liévre, Franziska Schmidt, A. Cothenet, Jean-Michel Simonin
article en

Abstract

This dataset provides a numerical simulation of pavement mechanical behavior under Traffic Speed Deflectometer (TSD) measurements. It consists of simulated deflection slope data for various pavement structures and subgrade properties, generated using the Alizé-LCPC software, a standard tool in French pavement engineering. The dataset addresses limitations in traditional Falling Weight Deflectometer (FWD) methods, offering a more accurate and computationally efficient approach for estimating the Subgrade Resilient Modulus (M<sub>R</sub>) using machine learning models. This resource is valuable for researchers aiming to enhance pavement evaluation methods and develop predictive models for road infrastructure maintenance and assessment. The data are openly accessible, facilitating widespread research collaboration and the application of advanced data analytics in pavement engineering.

Data in BriefVol. 57
Université Gustave Eiffel (FR)
Conseil Régional des Pays de la Loire
Openalex Percentile: Top 77%
Infrastructure Maintenance and Monitoring
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