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.

EVALUATION OF PFWD AS POTENTIAL QUALITY CONTROL TOOL OF PAVEMENT LAYERS

2010 ◽  
Vol 16 (1) ◽  
pp. 123-129 ◽  
Author(s):  
Amir Kavussi ◽  
Shahaboddin Yasrobi
Keyword(s):  
Field Tests ◽  
Field Testing ◽  
California Bearing Ratio ◽  
Falling Weight Deflectometer ◽  
Drop Mass ◽  
Quality Control Tool ◽  
Plate Size ◽  
Weight Drop ◽  
Pavement Layers ◽  
Falling Weight

Portable Falling Weight Deflectometer (PFWD) that can be considered as simple equipment is mainly used to measure elastic moduli of pavement unbound layers. This paper evaluates the potential use of PFWD to reliably measure the elastic modulus of pavement layers. To achieve this, PFWD tests were conducted on highway sections selected from different projects in Tehran. The California Bearing Ratio (CBR) laboratory tests were also conducted on samples collected during field tests. PFWD testing parameters were varied while performing the field testing. These included drop weight, drop height, plate diameter and position of additional geophones. In addition, PFWD moduli were compared with those obtained from performing FWD testing on the same site. It was found that drop mass and loading plate size affect PFWD modulus significantly. In addition, the results indicated that good correlation exist between PFWD moduli and FWD and CBR results. Santrauka Nešiojamasis krintančio svorio deflektometras PFWD (angl. portable falling weight deflectometer) yra nesudetingas prietaisas, dažniausiai naudojamas kelio dangu nesurištu sluoksniu tamprumo moduliui nustatyti. Straipsnyje apžvelgta, kaip PFWD naudojamas kelio dangu sluoksniu tamprumo moduliams matuoti. Taikant PFWD išbandyti skirtinguose projektuose Teherane (Iranas) panaudoti kelio dangu skerspjūviai. Bandiniams papildomai atlikti Kalifornijos santykinio atsparumo rodiklio CBR (angl. California bearing ratio) nustatymo eksperimentiniai tyrimai. Atliekant lauko tyrimus naudoti skirtingi PFWD bandymu parametrai: krintantis svoris, kritimo aukštis, plokštes skersmuo ir papildomai išdestyti geofonai. PFWD nustatyti tamprumo moduliai palyginti su tamprumo moduliais, išmatuotais naudojant krintančio svorio deflektometra FWD (angl. falling weight deflectometer). Nustatyta, kad PFWD matavimu rezultatams didele itaka turi kritimo mase ir apkrovimo plokštes matmenys. Gauti eksperimentiniu tyrimu rezultatai parode, kad PFWD, FWD ir CBR matavimai gerai koreliuoja tarpusavyje.

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.

Assessment of Falling Weight Deflectometer Data for Stabilized Flexible Pavements

2000 ◽  
Vol 1709 (1) ◽  
pp. 19-25 ◽  
Author(s):  
Alexander K. Appea ◽  
Imad L. Al-Qadi
Keyword(s):  
Resilient Modulus ◽  
Ground Truth ◽  
Monitoring And Evaluation ◽  
Structural Condition ◽  
Base Layer ◽  
Falling Weight Deflectometer ◽  
Structural Capacity ◽  
Pavement Monitoring ◽  
Aggregate Base ◽  
Falling Weight

Backcalculation of pavement moduli through the utilization of the falling weight deflectometer (FWD) is used for pavement monitoring and evaluation. The performance and structural condition of nine flexible pavement test sections built in Bedford County, Virginia, have been monitored over the past 5 years using FWD. The nine sections include three groups with aggregate base layer thicknesses of 100, 150, and 200 mm, respectively. Sections 1, 4, and 7 are control, whereas Sections 2, 5, 8 and 3, 6, 9 are stabilized with geotextiles and geogrids, respectively. The FWD testing used five double-load drops ranging from 26.5 to 58.9 kN. The deflection basins obtained from the testing have been analyzed using the ELMOD backcalculation program to find the pavement structural capacity and to detect changes in the aggregate resilient modulus. The analysis shows a reduction in the backcalculated resilient modulus of the 100-mmthick base layer. The reduction was 33 percent over 5 years for the nonstabilized section compared with the geosynthetically stabilized section. The reduction in base layer resilient modulus may have resulted from subgrade fine migration into this layer as confirmed by excavation. The study confirms the effectiveness of using woven geotextile as a separator in a pavement system built over weak subgrade. This supports the continuous rutting measurements and ground truth excavation conducted in late 1997.

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.

Deterioration of Asphalt Pavement at Long-Term Pavement Performance Program Site 49-1001 in Utah

2005 ◽  
Vol 1933 (1) ◽  
pp. 121-125
Author(s):  
Brandon J. Blankenagel ◽  
W. Spencer Guthrie
Keyword(s):  
Base Layer ◽  
Pavement Performance ◽  
Falling Weight Deflectometer ◽  
Water Drainage ◽  
Base Materials ◽  
Pavement Deterioration ◽  
Pavement Base ◽  
Aggregate Base ◽  
Falling Weight

Highway 191 near Bluff, Utah, features a well-monitored section of the long-term pavement performance (LTPP) program. Constructed in 1980, this section of flexible pavement performed well for nearly 13 years. Through this time, cracking of the asphalt layer was minimal. In the fourteenth year, however, the extent of longitudinal cracking in the wheel path increased and necessitated placement of a chip seal on the pavement surface. The purpose of this research was to determine the cause of pavement deterioration using LTPP data. Deflection basins obtained from falling-weight deflectometer testing were analyzed to investigate the extent to which structural degradation influenced deterioration of the pavement. Pavement layer modulus values were plotted against time and clearly show that weakening of the pavement base layer immediately preceded the occurrence of cracking. The geography of the site, as documented in photographs, supports the conclusion that inadequate water drainage at the site permitted saturation of the aggregate base layer during a period of midsummer flooding. This finding emphasizes the importance of specifying non-moisture-susceptible base materials and providing necessary drainage works in pavement design.

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.

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