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Falling weight deflectometer dispersion curve method for pavement modulus calculation.

Authors :
Wang, Xue
Huang, Hai
Zhang, Kun
Shen, Shihui
Source :
Philosophical Transactions of the Royal Society A: Mathematical, Physical & Engineering Sciences. 9/4/2023, Vol. 381 Issue 2254, p1-20. 20p.
Publication Year :
2023

Abstract

The falling weight deflectometer (FWD) test is a common non-destructive testing method for evaluating the structural capacity of pavements. At present, data processing of the FWD test mainly focuses on the deflection data, while paying less attention to the deflection-time history. Because a FWD is equipped with impulse loads and geophones, which allow for the generation and capture of surface wave signal propagation, it is hypothesized that Rayleigh wave dispersion theory can be applied to calculate the modulus profile along the pavement depth by analysing the dispersive properties of the deflection signal measured during FWD tests. To test this hypothesis, we develop a new methodology for the FWD test and data analysis, referred to as the FWD dispersion curve method. We first introduce the concept of the new method, followed by an illustration of the procedure and the experimental set-up. Case studies on three concrete pavement segments are then presented to evaluate the effectiveness of the FWD dispersion curve method. Modifications to the existing FWD device are further recommended for the impact loading sources and signal collection process so that the modulus of a much shallower layer, such as the concrete slab and upper asphalt layers, can be obtained. This article is part of the theme issue 'Artificial intelligence in failure analysis of transportation infrastructure and materials'. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
1364503X
Volume :
381
Issue :
2254
Database :
Academic Search Index
Journal :
Philosophical Transactions of the Royal Society A: Mathematical, Physical & Engineering Sciences
Publication Type :
Academic Journal
Accession number :
164936851
Full Text :
https://doi.org/10.1098/rsta.2022.0167