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Numerical modeling of pavement deflection behavior under the Traffic Speed Deflectometer

A Abdelmuhsen, J-M Simonin, F Schmidt, D Lièvre, A Cothenet, M Freitas, A Ihamouten

TL;DR

This study develops a numerical forward model using Burmister's elastic linear isotropic theory, implemented in Alizé-LCPC, to simulate TSD deflection slope data $D_S$ and derive $S_V$ for estimating the Subgrade Resilient Modulus $M_R$. A synthetic dataset of 235 deflection-slope scenarios (under dual-wheel loading) across $M_R$ values from 16 to 250 MPa is created, enabling ML-based inverse modeling to predict $M_R$ from $S_V$-derived features. The framework analyzes TSD loading and sensor configurations, showing that sensors farther from the load capture more informative variability in $S_V$, and provides a structured data-processing workflow that yields a concatenated deflection-slope database for ML use. The dataset and methodology offer a scalable path to more efficient pavement health assessment and maintenance planning via TSD-derived indicators, with open data available via a DOI and license.

Abstract

This database outlines the development of a numerical model for simulating pavement mechanical behavior under the Traffic Speed Deflectometer (TSD).

Numerical modeling of pavement deflection behavior under the Traffic Speed Deflectometer

TL;DR

This study develops a numerical forward model using Burmister's elastic linear isotropic theory, implemented in Alizé-LCPC, to simulate TSD deflection slope data and derive for estimating the Subgrade Resilient Modulus . A synthetic dataset of 235 deflection-slope scenarios (under dual-wheel loading) across values from 16 to 250 MPa is created, enabling ML-based inverse modeling to predict from -derived features. The framework analyzes TSD loading and sensor configurations, showing that sensors farther from the load capture more informative variability in , and provides a structured data-processing workflow that yields a concatenated deflection-slope database for ML use. The dataset and methodology offer a scalable path to more efficient pavement health assessment and maintenance planning via TSD-derived indicators, with open data available via a DOI and license.

Abstract

This database outlines the development of a numerical model for simulating pavement mechanical behavior under the Traffic Speed Deflectometer (TSD).

Paper Structure

This paper contains 13 sections, 4 figures, 4 tables.

Table of Contents

  1. Introduction
  2. Alize-LCPC
  3. Database Parameters
  4. Pavement structure: Mechanical properties
  5. TSD Loading mechanism
  6. Coordinates of TSD Simulated Measurements
  7. TSD Load Configuration
  8. Laser Doppler Sensor Position
  9. Structured Data Processing Framework
  10. Results
  11. Mechanical Behaviour Modelling
  12. Data Modelling and Processing
  13. Conclusion

Figures (4)

  • Figure 1: TSD measurment system
  • Figure 2: Structure of the pavement under study ref13. In scientific terminology from ref12 , ($HMA$) denotes a Hot Mix Asphalt. Similarly, ($BC-g2$) represents grade 2 aggregate bituminous concrete, while ($pf_4$) refers to class 4 subgrade.
  • Figure 7: TSD Numerical Model: (a) TSD load configuration ref16, (b) Simulation of pavement behavior under TSD Abdelmuhsen23aAbdelmuhsen24Abdelmuhsen22bAbdelmuhsen23b.
  • Figure 8: $Sn_{1}$ to $Sn_{7}$ Deflection slope analysis Abdelmuhsen23aAbdelmuhsen24Abdelmuhsen22bAbdelmuhsen23b