Evaluation of Rigid Pavement Condition Using Falling Weight Deflectometer

2021 ◽  
pp. 43-52
Author(s):  
Murari M. Pradhan
Keyword(s):  
Falling Weight Deflectometer ◽  
Rigid Pavement ◽  
Pavement Condition ◽  
Falling Weight

Application of Falling Weight Deflectometer Data for Analysis of Superheavy Loads

1996 ◽  
Vol 1540 (1) ◽  
pp. 83-90 ◽  
Author(s):  
Dar-Hao Chen ◽  
Emmanuel Fernando ◽  
Michael Murphy
Keyword(s):  
Falling Weight Deflectometer ◽  
Pavement Condition ◽  
Percent Confidence Level ◽  
Significant Difference ◽  
Before And After ◽  
Transport Vehicle ◽  
Pavement Damage ◽  
The Cost ◽  
Falling Weight ◽  
Surface Deflections

Permitting superheavy loads may increase the rate of pavement damage and the cost of maintenance. An analysis of a proposed superheavy load route (FM519) to evaluate the potential pavement damage caused by a planned superheavy load move is presented. Falling weight deflection (FWD) tests and backcalculations of layer moduli were performed on the FM519. FWD tests and backcalculation of layer moduli were performed on the pavement before and after the superheavy load was moved. ELSYM5 and BISAR were used to evaluate the pavement responses using the backcalculated layer moduli from FWD data. The predictions of surface deflections from ELSYM5 and BISAR were close to (within 10 percent of) the measured deflections from FWD tests. The FWD data and analyses show that the existing pavement structure is adequate for the planned superheavy load move. Finally, the permit was issued with the condition that the transport vehicle should be kept within the travel lanes and away from the shoulder whenever possible. FWD tests were conducted after the superheavy load move and comparisons with before superheavy load move were made. T-tests were performed to check for significant difference at the 95 percent confidence level. T-tests showed that there is no significant difference between before and after superheavy load move. Also, no significant distresses due to this superheavy load were observed after the move, and the pavement condition is consistent with the analysis performed to issue the permit.

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.

Network Pavement Evaluation with Falling-Weight Deflectometer and Ground-Penetrating Radar

2003 ◽  
Vol 1860 (1) ◽  
pp. 90-99 ◽  
Author(s):  
A. Samy Noureldin ◽  
Karen Zhu ◽  
Shuo Li ◽  
Dwayne Harris
Keyword(s):  
Data Collection ◽  
Ground Penetrating Radar ◽  
Pavement Management ◽  
Falling Weight Deflectometer ◽  
Pavement Condition ◽  
Condition Rating ◽  
Structural Capacity ◽  
Pavement Evaluation ◽  
Ground Penetrating ◽  
Falling Weight

Nondestructive testing has become an integral part of pavement evaluation and rehabilitation strategies in recent years. Pavement evaluation employing the falling-weight deflectometer (FWD) and ground-penetrating radar (GPR) can provide valuable information about pavement performance characteristics and be a very useful tool for project prioritization purposes and estimation of a construction budget at the network level. Traditional obstacles to the use of the FWD and GPR in pavement evaluation at the network level used to be expenses involved in data collection, limited resources, and lack of simplified analysis procedures. Indiana experience in pavement evaluation with the FWD and the GPR at the network level is presented. A network-level FWD and GPR testing program was implemented as a part of a study to overcome those traditional obstacles. Periodic generation of necessary data will be useful in determining how best to quantify structural capacity and estimate annual construction budgets. Three FWD tests per mile on 2,200 lane-mi of the network is recommended annually for network-level pavement evaluation. The information collected will allow the equivalent of 100% coverage of the whole network in 5 years. GPR data are recommended to be collected once every 5 years (if another thickness inventory is needed) after the successful network thickness inventory conducted in this study. GPR data collection is also recommended at the project level and for special projects. Both FWD and GPR data are recommended to be used as part of the pavement management system, together with automated collection of data such as international roughness index, pavement condition rating, rut depth, pavement quality index, and skid resistance.

scholarly journals Toward the Development of Load Transfer Efficiency Evaluation of Rigid Pavements by a Rolling Wheel Deflectometer

2020 ◽  
Vol 5 (1) ◽  
pp. 7
Author(s):  
Pawan Deep ◽  
Mathias B. Andersen ◽  
Nick Thom ◽  
Davide Lo Presti
Keyword(s):  
Load Transfer ◽  
Three Dimensional ◽  
Transfer Efficiency ◽  
Falling Weight Deflectometer ◽  
Rigid Pavement ◽  
Important Indicator ◽  
Rigid Pavements ◽  
Load Transfer Efficiency ◽  
Rolling Wheel ◽  
Falling Weight

The jointed rigid pavement is currently evaluated by the Falling weight deflectometer which is rather slow for the testing of the jointed pavements. Continuous nondestructive evaluation of rigid pavements with a rolling wheel deflectometer can be used to measure the load transfer and is investigated. Load transfer is an important indicator of the rigid pavement’s condition and this is the primary factor which is studied. Continuous data from experimental measurements across a joint allows for the determination of not only the load transfer efficiency provided parameters characterizing the pavement is known. A three-dimensional semi-analytical model was implemented for simulating the pavement response near a joint and used for interpretation and verification of the experimental data. Results show that this development is promising for the use of a rolling wheel deflectometer for rapid evaluation of joints.

Primary Results for the First Texas Mobile Load Simulator Test Pad

1997 ◽  
Vol 1570 (1) ◽  
pp. 30-38 ◽  
Author(s):  
Dar-Hao Chen ◽  
Ken Fults ◽  
Mike Murphy
Keyword(s):  
Permanent Deformation ◽  
Falling Weight Deflectometer ◽  
Pavement Condition ◽  
Pavement Distress ◽  
Moisture Contents ◽  
Load Simulator ◽  
The Right ◽  
Falling Weight ◽  
Simulator Test

The pavement behavior under 630,000 axle repetitions from the Texas Mobile Load Simulator (TxMLS) was studied. During the course of TxMLS testing, nondestructive testing and in situ instrumentation (pressure cell, strain gauge, and multidepth deflectometers) were applied to monitor and assess the pavement condition. In addition, pavement distress data, such as measurements of permanent deformation, rutting, and cracking, were collected and analyzed. At the end of 630,000 axle repetitions, the maximum permanent deformation was approximately 22 mm. A forensic study was then performed by cutting a 1.8-m by 3-m trench in the middle of the test pad and three nuclear density gauge (NDG) tests were performed on top of each layer to determine the densities and moisture contents. It was found that the lime-treated gravel base (LTB) layer contributed the most to rutting. The axle-load applications increased the density of the LTB layer in the pit area, which caused the pavement structure to settle and consolidate. The highest rate of rutting was observed at the early stages of loading, between 10,000 and 20,000 axle repetitions. The higher deflections on the left wheelpath as observed from falling weight deflectometer data were probably due to the higher moisture content and the instrumented pit. NDG data showed that the LTB, lime-stabilized subgrade (LTS), and subgrade in the left wheelpath all had higher moisture contents than those in the right wheelpath.

DIPLOBACK: Neural-Network-Based Backcalculation Program for Composite Pavements

1997 ◽  
Vol 1570 (1) ◽  
pp. 143-150 ◽  
Author(s):  
Lev Khazanovich ◽  
Jeffery Roesler
Keyword(s):  
Neural Network ◽  
Winkler Foundation ◽  
Multilayer Composite ◽  
Falling Weight Deflectometer ◽  
Subgrade Reaction ◽  
Elastic Parameters ◽  
The Neural Networks ◽  
Elastic Layers ◽  
Falling Weight ◽  
Moduli Of Elasticity

A neural-network-based backcalculation procedure is developed for multilayer composite pavement systems. The constructed layers are modeled as compressible elastic layers, whereas the subgrade is modeled as a Winkler foundation. The neural networks are trained to find moduli of elasticity of the constructed layers and a coefficient of subgrade reaction to accurately match a measured deflection profile. The method was verified by theoretically generated deflection profiles and falling weight deflectometer data measurements conducted at Edmonton Municipal Airport, Canada. For the theoretical deflection basins, the results of backcalculation were compared with actual elastic parameters, and excellent agreement was observed. The results of backcalculation using field test data were compared with the results obtained using WESDEF. Similar trends were observed for elastic parameters of all the pavement layers. The backcalculation procedure is implemented in a computer program called DIPLOBACK.

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