Use of Falling Weight Deflectometer Data for Network-Level Flexible Pavement Management

2012 ◽  
Vol 2304 (1) ◽  
pp. 75-85 ◽  
Author(s):  
Amy L. Crook ◽  
Sharlan R. Montgomery ◽  
W. Spencer Guthrie
Keyword(s):  
Flexible Pavement ◽  
Pavement Management ◽  
Falling Weight Deflectometer ◽  
Falling Weight

scholarly journals Measurements of the elastic modulus of pavement subgrade layers using the SASW and FWD test methods

2015 ◽  
Vol 10 (2) ◽  
pp. 174-181 ◽  
Author(s):  
Nur Izzi Md. Yusoff ◽  
Sentot Hardwiyono ◽  
Norfarah Nadia Ismail ◽  
Mohd Raihan Taha ◽  
Sri Atmaja P. Rosyidi ◽  
...  
Keyword(s):  
Spectral Analysis ◽  
Elastic Modulus ◽  
Surface Wave ◽  
Time Lag ◽  
Test Methods ◽  
Pavement Management ◽  
Falling Weight Deflectometer ◽  
Surface Wave Propagation ◽  
Evaluation And Monitoring ◽  
Falling Weight

In pavement management systems, deflection basin tests, such as the Falling Weight Deflectometer test, are common techniques that are widely used, while the surface wave test, i.e. the Spectral Analysis of Surface Wave test, is recently employed as an alternative technique in pavement evaluation and monitoring. In this paper, the performance of both dynamic non-destructive tests on pavement subgrade investigation is presented. Surface wave propagation between a set of receivers was transformed into the frequency domain using the Fast Fourier Transform technique and subsequently a phase spectrum was produced to measure the time lag between receivers. Using the phase difference method, an experimental dispersion curve was generated. Inversion analysis based on the 3-D stiffness matrix method was then performed to produce a shear wave velocity profile. The elastic modulus of pavement layers was calculated based on linear elastic theory. In the Falling Weight Deflectometer test, seven geophones were used to collect in situ deflection data. Based on a back-calculation procedure with the ELMOD software, the elastic modulus of each flexible pavement layer can be obtained. Both techniques are able to comprehensively investigate the elastic modulus of the subgrade layer in existing pavement non-destructively. The elastic modulus between the Spectral Analysis of Surface Wave method and the Falling Weight Deflectometer test on the subgrade layer is observed to be in a good agreement. A correlation of the elastic modulus of thesubgrade layer from both techniques is also presented.

Use of falling weight deflectometer time history data for the analysis of seasonal variation in pavement response

2010 ◽  
Vol 37 (9) ◽  
pp. 1224-1231 ◽  
Author(s):  
Kate Deblois ◽  
Jean-Pascal Bilodeau ◽  
Guy Doré
Keyword(s):  
Phase Angle ◽  
Time History ◽  
Test Site ◽  
Flexible Pavement ◽  
Dissipated Energy ◽  
Falling Weight Deflectometer ◽  
History Data ◽  
The Road ◽  
Pavement Structures ◽  
Falling Weight

This paper presents the results of an exploratory analysis of falling weight deflectometer (FWD) data collected on a large project about the spring thaw behaviour of pavements. The test site includes four test sections, two of which are conventional flexible pavement structures, whereas the other two are built with a cement-treated base. The aim of this study is to verify the applicability of using FWD time history data to evaluate damage to a road during the thawing period. The applicability of the analysis techniques is verified through the phase angle and dissipated energy. The data analyzed were obtained from tests conducted with an FWD on one flexible pavement test section. The results obtained showed a clear difference between the winter, thawing, and summer periods. It was found that the phase angle and dissipated energy can be used to evaluate the road damage during the thawing period through quantification of the phase angle and dissipated energy. These factors can also be used to describe the pavement behaviour in terms of elasticity and viscoelasticity.

Evaluation and Validation of a Model for Predicting Pavement Structural Number with Rolling Wheel Deflectometer Data

2015 ◽  
Vol 2525 (1) ◽  
pp. 13-19 ◽  
Author(s):  
Mostafa A. Elseifi ◽  
Kevin Gaspard ◽  
Paul W. Wilke ◽  
Zhongjie Zhang ◽  
Ahmed Hegab
Keyword(s):  
Pavement Management ◽  
Average Error ◽  
Falling Weight Deflectometer ◽  
Department Of Transportation ◽  
Data Set ◽  
The Road ◽  
Average Prediction Error ◽  
Structural Capacity ◽  
Rolling Wheel ◽  
Falling Weight

Because of costs and the slow test process, the use of structural capacity in pavement management activities at the network level has been limited. The rolling wheel deflectometer (RWD) was introduced to support existing nondestructive testing techniques by providing a screening tool for structurally deficient pavements at the network level. A model was developed to estimate structural number (SN) from RWD data obtained in a Louisiana study. The objective for this study was to evaluate the use of the Louisiana model to predict structural capacity in Pennsylvania and to compare the results with those of existing methods. RWD testing was conducted on 288 mi of the road network in Pennsylvania, and falling weight deflectometer (FWD) testing and coring were conducted on selected sites. The prediction from a model used to estimate SN from RWD deflection data was compared statistically with the prediction obtained from FWD testing and from roadway management system records used by the Pennsylvania Department of Transportation to calculate SN. The results of this analysis validated the use of the model to estimate the pavement SN according to RWD deflection data. In general, the predicted SN was in agreement with the SN calculated from the FWD. The original model with the fitted coefficients developed for Louisiana showed an average prediction error of 27%. However, after the model was refitted to the data set from Pennsylvania, the average error dropped to 19%. Results indicated that the model developed for SN prediction from the RWD provided an adequate prediction of SN for conditions different from those for which it was developed in Louisiana.

scholarly journals Preliminary In-Situ Evaluation of an Innovative, Semi-Flexible Pavement Wearing Course Mixture Using Fast Falling Weight Deflectometer

Materials ◽  
2018 ◽  
Vol 11 (4) ◽  
pp. 611 ◽  
Author(s):  
Chiara Pratelli ◽  
Giacomo Betti ◽  
Tullio Giuffrè ◽  
Alessandro Marradi
Keyword(s):  
Flexible Pavement ◽  
Falling Weight Deflectometer ◽  
Falling Weight

scholarly journals Estimation of Flexible Pavement Structural Capacity Using Machine Learning Techniques

2020 ◽  
Author(s):  
Nader Karballaeezadeh ◽  
Hosein Ghasemzadeh Tehrani ◽  
Danial Mohammadzadeh S. ◽  
Shahaboddin Shamshirband
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Ground Penetrating Radar ◽  
Flexible Pavement ◽  
Falling Weight Deflectometer ◽  
Machine Learning Methods ◽  
Back Calculation ◽  
Ground Penetrating ◽  
Falling Weight ◽  
Surface Deflections

The most common index for representing structural condition of the pavement is the structural number. The current procedure for determining structural numbers involves utilizing falling weight deflectometer and ground-penetrating radar tests, recording pavement surface deflections, and analyzing recorded deflections by back-calculation manners. This procedure has two drawbacks: 1. falling weight deflectometer and ground-penetrating radar are expensive tests, 2. back-calculation ways has some inherent shortcomings compared to exact methods as they adopt a trial and error approach. In this study, three machine learning methods entitled Gaussian process regression, m5p model tree, and random forest used for the prediction of structural numbers in flexible pavements. Dataset of this paper is related to 759 flexible pavement sections at Semnan and Khuzestan provinces in Iran and includes “structural number” as output and “surface deflections and surface temperature” as inputs. The accuracy of results was examined based on three criteria of R, MAE, and RMSE. Among the methods employed in this paper, random forest is the most accurate as it yields the best values for above criteria (R=0.841, MAE=0.592, and RMSE=0.760). The proposed method does not require to use ground penetrating radar test, which in turn reduce costs and work difficulty. Using machine learning methods instead of back-calculation improves the calculation process quality and accuracy.

scholarly journals A new method to determine maintenance and repair activities at network-level pavement management using falling weight deflectometer

2017 ◽  
Vol 23 (3) ◽  
pp. 338-346 ◽  
Author(s):  
Amir KAVUSSI ◽  
Mojtaba ABBASGHORBANI ◽  
Fereidoon MOGHADAS NEJAD ◽  
Armin BAMDAD ZIKSARI
Keyword(s):  
Resilient Modulus ◽  
Pavement Management ◽  
Falling Weight Deflectometer ◽  
Pavement Management System ◽  
Pavement Condition ◽  
Maintenance And Repair ◽  
Structural Evaluation ◽  
Data Collection And Analysis ◽  
Field Data Collection ◽  
Falling Weight

Pavement condition assessment at network level requires structural evaluation that can be achieved using Falling Weight Deflectometer (FWD). Upon analysing FWD data, appropriate maintenance and repair methods (preser­vation, rehabilitation or reconstruction) could be assigned to various pavement sections. In this study, Structural Condi­tion Index (SCI), defined as the ratio of Effective Structural Number (SNeff) to Required Structural Number (SNreq), was used to determine if a pavement requires preservation or rehabilitation works (i.e. preservation SCI > 1, rehabilitation SCI < 1). In addition to FWD deflection data, SCI calculation requires pavement layer thicknesses that is obtained using GPR with elaborated and time consuming works. In order to reduce field data collection and analysis time at network-level pavement management, SCI values were calculated without having knowledge of pavement layer thicknesses. Two regression models were developed based on several thousand FWD deflection data to calculate SNeff of pavements and resilient modulus (MR) of their subgrades. Subgrades MR values together with traffic data were then used to calculate SNreq. Statistical analysis of deflection data indicated that Area under Pavement Profile (AUPP) and the deflection at distance of 60 cm from load center (D60) parameters showed to have strong correlation with SNeff and MR respectively. The determination coefficients of the two developed models were greater than those of previous models reported in the literature. The significant result of this study was to calculate SNeff and MR using the same deflection data. Finally, imple­mentation of the developed method was described in determining appropriate Maintenance and Repair (M&R) method at network level pavement management system.

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