Backcalculation of Dynamic Modulus Mastercurve from Falling Weight Deflectometer Surface Deflections

2011 ◽  
Vol 2227 (1) ◽  
pp. 87-96 ◽  
Author(s):  
M. Emin Kutay ◽  
Karim Chatti ◽  
Ligang Lei
Keyword(s):  
Dynamic Modulus ◽  
Falling Weight Deflectometer ◽  
Falling Weight ◽  
Surface Deflections

Curvilinear Behavior of Base Layer Moduli from Deflection and Seismic Methods

2000 ◽  
Vol 1716 (1) ◽  
pp. 55-63
Author(s):  
Kamal Tawfiq ◽  
John Sobanjo ◽  
Jamshid Armaghani
Keyword(s):  
Surface Deformation ◽  
Low Frequency ◽  
Field Testing ◽  
Base Layer ◽  
Falling Weight Deflectometer ◽  
Seismic Methods ◽  
Extensive Field ◽  
Curvilinear Relationships ◽  
Falling Weight ◽  
Surface Deflections

The reality of curvilinear relationships of stiffness versus deformation is usually neglected when moduli values from seismic methods are compared with those of deflection methods. On the basis of extensive field testing, results showed that moduli values for the base layers from deflection methods did not conform to those of seismic methods. Deflection testing techniques were signified by the falling weight deflectometer (FWD) and the Dynaflect methods. Seismic testing was carried out by use of the seismic pavement analyzer (SPA) method. The SPA test results yielded moduli values higher than those obtained from the deflection methods. Utilizing pavement parameters obtained from the SPA data, researchers determined surface deflections by use of frequency response functions of signals from the two groups of sensors used in the testing setup. Because of the types of hammers in the SPA testing, two different deflection basins were obtained at each testing point. Comparison of surface deflections from these methods indicated that deflection amplitudes from the FWD method were about 100 times higher than those obtained from the high-frequency hammer of the SPA. At certain pavement sections, deflections from the Dynaflect method were comparable to those obtained with the SPA low-frequency hammer. Accordingly, curvilinear relationships between surface deformation versus stiffness values were derived. These relationships can be used to determine moduli values at all surface deflections, including those from service loads.

scholarly journals Application of FWD data in developing dynamic modulus master curves of in-service asphalt layers

2017 ◽  
Vol 23 (5) ◽  
pp. 661-671 ◽  
Author(s):  
Nader SOLATIFAR ◽  
Amir KAVUSSI ◽  
Mojtaba ABBASGHORBANI ◽  
Henrikas SIVILEVIČIUS
Keyword(s):  
Dynamic Modulus ◽  
Master Curve ◽  
Asphalt Pavements ◽  
Falling Weight Deflectometer ◽  
Master Curves ◽  
Simple Method ◽  
High Frequencies ◽  
Design Guide ◽  
Falling Weight

This paper presents a simple method to determine dynamic modulus master curve of asphalt layers by con­ducting Falling Weight Deflectometer (FWD) for use in mechanistic-empirical rehabilitation. Ten new and rehabilitated in-service asphalt pavements with different physical characteristics were selected in Khuzestan and Kerman provinces in south of Iran. FWD testing was conducted on these pavements and core samples were taken. Witczak prediction model was used to predict dynamic modulus master curves from mix volumetric properties as well as the bitumen viscosity characteristics. Adjustments were made using FWD results and the in-situ dynamic modulus master curves were ob­tained. In order to evaluate the efficiency of the proposed method, the results were compared with those obtained by us­ing the developed procedure of the state-of-the-practice, Mechanistic-Empirical Pavement Design Guide (MEPDG). Re­sults showed the proposed method has several advantages over MEPDG including: (1) simplicity in directly constructing in-situ dynamic modulus master curve; (2) developing in-situ master curve in the same trend with the main predicted one; (3) covering the large differences between in-situ and predicted master curve in high frequencies; and (4) the value obtained for the in-situ dynamic modulus is the same as the value measured by the FWD for a corresponding frequency.

Mechanistic-Based Approach to Utilize Traffic Speed Deflectometer Measurements in Backcalculation Analysis

2020 ◽  
Vol 2674 (5) ◽  
pp. 208-222
Author(s):  
Zia U. A. Zihan ◽  
Mostafa A. Elseifi ◽  
Patrick Icenogle ◽  
Kevin Gaspard ◽  
Zhongjie Zhang
Keyword(s):  
Asphalt Concrete ◽  
Traffic Control ◽  
Parametric Study ◽  
Complex Modulus ◽  
Falling Weight Deflectometer ◽  
Software Application ◽  
Traffic Speed ◽  
Definition Of ◽  
Falling Weight ◽  
Surface Deflections

Backcalculation analysis of pavement layer moduli is typically conducted based on falling weight deflectometer (FWD) deflection measurements; however, the stationary nature of the FWD requires lane closure and traffic control. In recent years, traffic speed deflection devices such as the traffic speed deflectometer (TSD), which can continuously measure pavement surface deflections at traffic speed, have been introduced. In this study, a mechanistic-based approach was developed to convert TSD deflection measurements into the equivalent FWD deflections. The proposed approach uses 3D-Move software to calculate the theoretical deflection bowls corresponding to FWD and TSD loading configurations. Since 3D-Move requires the definition of the constitutive behaviors of the pavement layers, cores were extracted from 13 sections in Louisiana and were tested in the laboratory to estimate the dynamic complex modulus of asphalt concrete. The 3D-Move generated deflection bowls were validated with field TSD and FWD data with acceptable accuracy. A parametric study was then conducted using the validated 3D-Move model; the parametric study consisted of simulating pavement designs with varying thicknesses and material properties and their corresponding FWD and TSD surface deflections were calculated. The results obtained from the parametric study were then incorporated into a Windows-based software application, which uses artificial neural network as the regression algorithm to convert TSD deflections to their corresponding FWD deflections. This conversion would allow backcalculation of layer moduli using TSD-measured deflections, as equivalent FWD deflections can be used with readily available tools to backcalculate the layer moduli.

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.

PFWD's Application in the Evaluation of Structural Strength of Field Hot Regeneration Asphalt Pavement

2013 ◽  
Vol 723 ◽  
pp. 141-148 ◽  
Author(s):  
Jian Guo Wei ◽  
Bin Wang
Keyword(s):  
Correlation Analysis ◽  
Field Test ◽  
Dynamic Modulus ◽  
Reliable Method ◽  
Structural Strength ◽  
Asphalt Pavement ◽  
Falling Weight Deflectometer ◽  
Beam Method ◽  
Structure Strength ◽  
Falling Weight

To evaluate the pre and post change of structure strength of old asphalt pavement field hot regeneration, we use the portable falling weight deflectometer method (PFWD) and benkelman beam method (BB) respectively to do the field test research. The field test researches rely on the ANXIN highway old asphalt pavement field hot regeneration project. We got the data about pre and post regenerations asphalt pavement static bending deflection (l0), PFWD dynamic deflection (lp) and PFWD dynamic modulus (EP). The correlation analysis among static bending deflection, PFWD dynamic deflection and PFWD dynamic modulus suggest that PFWD method is a more stable and reliable method than BB method and PFWD method can be a new evaluation technology for the old asphalt pavement field hot regenerations pavement strength.

Interpretation of pavement deflection measurement data using an elastodynamic stochastic approach

1998 ◽  
Vol 25 (1) ◽  
pp. 151-160 ◽  
Author(s):  
Mehdi Parvini ◽  
Dieter FE Stolle
Keyword(s):  
Finite Element ◽  
Measurement Data ◽  
Stochastic Approach ◽  
Falling Weight Deflectometer ◽  
Stochastic Finite Element ◽  
Element Approach ◽  
Subgrade Modulus ◽  
Low Sensitivity ◽  
Falling Weight ◽  
Surface Deflections

Pavement deflection measurements, together with backcalculation procedures, are widely used to estimate the layer moduli of pavement-subgrade systems. Sensitivity analysis of a sample problem indicates that conclusions drawn from static analyses with regards to deflection sensitivity to variation in layer moduli may apply when characterizing uncertainty associated with the interpretation of the falling weight deflectometer (FWD) data. The uncertainty associated with the values of the backcalculated parameters from deflection data is investigated in this paper using an elastodynamic, stochastic finite element approach. The results of the simulations indicate that, in order to properly estimate surface layer moduli, loading frequencies higher than that of excitation by typical FWD loading are required. The low sensitivity of deflection uncertainty to random variations in surface modulus, when compared with that associated with subgrade modulus, is demonstrated to contribute to high variations in backcalculated surface modulus from measured surface deflections. Although focus is placed on uncertainties in elastic modulus and deflection, the methodology presented in the paper can be used to quantify uncertainties associated with other layer properties and pavement responses.Key words: stochastic, finite element, pavement deflection, elastodynamic, backcalculation, layer moduli, falling weight deflectometer test.

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.

Relationship between Dynamic Modulus and Static Detection Index in Red Sandstone Subgrade

2013 ◽  
Vol 353-356 ◽  
pp. 1112-1115
Author(s):  
Chong Chen ◽  
Jian Long Zheng
Keyword(s):  
Water Content ◽  
Dynamic Modulus ◽  
Resilient Modulus ◽  
Falling Weight Deflectometer ◽  
Red Sandstone ◽  
Bearing Plate ◽  
Static Modulus ◽  
Falling Weight ◽  
Static Detection ◽  
Compaction Ratio

The portable falling weight deflectometer (PFWD) test, bearing plate test, beckman beam test, and compaction test were carried out in this study. The relation of dynamic modulus and static modulus, dynamic modulus and deflection, dynamic modulus and compaction ratio, dynamic modulus and water content (consistency) was built. It turns out that there are good power function regression relationship between dynamic modulus and static modulus, dynamic modulus and deflection, dynamic modulus and compaction ratio, dynamic modulus and water content and consistency. Key words: red sandstone; dynamic modulus; resilient modulus; deflection; compaction ratio; water content

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.

Sign in / Sign up

Export Citation Format

Share Document