A Correction Model for the Continuous Deflection Measurement of Pavements Under Dynamic Loads

Abstract

Pavement deflection is the primary indicator reflecting road-bearing capacity. The falling weight deflectometer (FWD) and the Benkelman beam deflectometer (BBD) are widely used in deflection measurement, and are suitable for measuring the elastic deflection of pavement under static loading or dynamic impact loading. In recent years, there has been an increasing interest in continuous dynamic deflectometers, especially velocity-based deflectometers. The velocity-based continuous dynamic deflectometers, such as the laser dynamic deflectometer (LDD), are used to solve the problems of traffic speed and non-destructive pavement deflection measurement. However, there can be many deviations caused by the varied environmental parameters in practical application. In this article, through the review of the mechanism of the Euler-Bernoulli beam upon an elastic foundation and the corresponding influencing factors, the internal and external factors causing dynamic deflection measurement deviation are analyzed and summarized. The influences of pavement temperature, vehicle speed, pavement condition, and other factors are summarized and discussed. On this basis, a model of environmental parameter correction for the continuous dynamic deflectometer is established using a regression analysis method. Numbers of experiments confirmed the reliability of the correction model under different measurement conditions. The correlation experiments between the LDD and different types of deflectometer confirmed the accuracy of the correction model under different measurement conditions. The results showed that the correction model for the continuous deflection measurement of pavements under dynamic loads has strong robustness in different environments and conditions.

Performance Evaluation of Traffic Speed Deflectometer Based on Virtual Standard Test Road

“…Wix et al [16] compared the TSD measurements in New South Wales in two years and found better repeatability of the maximum deflection. Liao et al [1] showed that the variability of TSD measurements at three speeds (30,50, and 80 km/h) was lower than 5%. Shrestha et al [17] discussed the repeatability and correlation of TSD using approximately 9540 km of measuring data.…”

“…Deflection is a significant property reflecting the bearing capacity of the pavement [1]. Earlier, instruments such as the Benkelman Beam Deflectometer (BBD) and the Falling Weight Deflectometer (FWD) were used to measure it.…”

“…As a complex measuring instrument, factors from both internal and external sources may cause inaccurate TSD output, such as the deflection model [12], dynamic axle load [1], road roughness [1], [13], temperature [9], and road noise [14]. Therefore, it is essential to analyze the accuracy of the measured value and evaluate the measuring performance of TSD.…”

Assessment of pavement deflection under vehicle loads using a 3D-DIC system in the field

“…In field tests, there are three typical methods of deflection measurement: static load-based deflectometer, which provides the maximum deflection under a static load 14 ; stationary dynamic impact deflection measurement, as the falling weight deflectometer (FWD) 15 ; and moving dynamic deflection measurement, e.g. the laser dynamic deflectometer (LDD), which captures the deflection along a line on the pavement as the vehicule moves 16 , 17 . Though the static load-based Benkelman beam has been used extensively in India and several other countries for structural evaluation of in-service pavements, the FWD is considered to be the most appropriate equipment 14 , 18 .…”

Data Prediction of Deflection Basin Evolution of Asphalt Pavement Structure Based on Multi-Level Neural Network

“…Under different load levels, 19 kinds of structures' deflection basins with different stiffness levels have been regularly tested and data collected, and their changes have been analysed [14]. As an important indicator reflecting the bearing capacity of asphalt pavement structures, deflection has always been the focus of highway builders and scientific researcher [15][16][17]. This paper uses the data collected from the RIOHTRACK ring road to predict the deflection basin of the road by establishing a multi-level neural network model.…”